That classic Cheers theme song captures the vibe of this week’s Hill and Valley Forum, now in its fourth iteration. Lux is a major sponsor of the event, as are multiple of our portfolio companies.

This week’s rendition was the largest ever, overflowing across the stately Andrew W. Mellon Auditorium just a stone’s throw from the White House. So overflowing, in fact, that the fire marshal held up the lines outside for what one venture capitalist friend told me was at least 90 minutes. Apparently, some speakers were even stuck serpentining in the TSA-like queue snaking along Constitution Avenue, forcing the hall’s exasperated Voice of God to repeatedly order everyone to stop networking and sit their asses in chairs. “We are in a holding pattern!” she cried out, a term that could equally describe most policymaking in DC these days.

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What’s striking about Hill and Valley is just how fast the agendas and the types of participants have changed. When the Forum started, it was just a dinner organizing a ban on TikTok, part of a broader China hawk agenda driven by co-founder Jacob Helberg, who today is Under Secretary of State for Economic Growth, Energy, and the Environment.

Fear of the adversary was latent throughout the 2024 forum, with panels like “Are we ready for an AI Pearl Harbor?” and others focused on China and Russia. That line of argument disappeared in last year’s muddled messaging, which was held in the immediate aftermath of Trump’s “Liberation Day” tariff fusillade. The dichotomy at dinner between the tariff-loving keynote speaker Howard Lutnick and the venture capitalists arrayed across the banquet tables scanning their badly bruised public market portfolios was jarring.

The barbarians at the gate have become the regents of the tower.

Taking a cue from Cheers, this year’s conference was nothing but bonhomie on stage and in the networking niches on the fringes. As the populist backlash against AI grows ever more clamorous, Jamie Dimon was on stage talking about the strategic financial opportunities of American manufacturing while commenting on the jobs of the future, including “being a teller at a J.P. Morgan branch, 50, 40,000 dollars per year” in one of the great verbal downward corrections of the day. Speaker of the House Mike Johnson gave a sop to the political infighting, offering a cogent, fluent and principled speech on the value of competition and markets. It was the best example of an unquotable antimeme I may have ever witnessed.

Part of the challenge is that the barbarians at the gate have become the regents of the tower. The production scripting of the event precluded any debate more robust than an MBA seminar in which everyone had worked as a BA at McKinsey. I witnessed no moments of drama. Even last year’s one-man protest from the gallery aimed at Palantir’s Alex Karp seemed deterred by the line outside.

There was a fleeting moment of excitement when Curie.Bio’s Zach Weinberg gave a passionately skeptical take on the immediate impact of AI-based in-silico models on pre-clinical research data, while ARPA-H director Alicia Jackson forcefully pushed back, saying “No, I totally disagree” and argued that the work of her agency and the FDA could save months or even years off drug trials. Senator Todd Young, a fellow panelist, tried to calm the waters, offering “Not staged, not staged, not choreographed” and joked that the two could continue the conversation backstage. Scandal! The moderator then cut off this colloquy with a new question of “What are you most optimistic about?”

It wasn’t just that the agenda has drifted toward the anodyne over the years, but that the bipartisan audience of the event has drifted out as well. Democratic crossovers like Ritchie Torres, Cory Booker, Kyrsten Sinema and Jeanne Shaheen were still well-represented on stage, with this year’s cast including Senators Chris Coons, Mark Warner and Maria Cantwell as well as Congressman Josh Gottheimer. But if there was a Democratic staffer or think tanker walking the floor, they hid their affiliation very well. The near-unanimity of the TikTok ban a few years ago has unfortunately given way to the polarizing politics of the Trump 47 vintage.

Governing is about tradeoffs and challenges, but it’s harder when everyone is trying to avoid creating daylight between themselves and the administration.

Where was the debate between Ro Khanna, Silicon Valley congressman, and Delian Asparouhov, co-founder of both Varda and the Hill and Valley Forum, over California’s wealth tax proposal? Where were Ocasio-Cortes and Sanders slugging it out with 8VC’s Joe Lonsdale on degrowth, a matter-antimatter combustion that would have driven half of the DC political press corps mad for front-row seats for the Bellum in the Mellon. Pay-per-view this was not.

Notably, Hill and Valley seems to have become far more valley than hill. Walking the floor, every venture capitalist and quite a few founders in the reindustrialization, aerospace and defense markets were hobnobbing at elevated volume over decadent cake pops or while waiting in the defunct Global Entry–like line for the men’s room (the women’s room down the hall was wide open, and more than one guy commented to me on how unfair the whole situation was). I ran into a VC I haven’t seen in person since their wedding a decade ago, several founders who are now posteconomic since the last time I got coffee with them, and even a handful of media friends I dearly miss. Amid a group like this, you really do feel like Norm.

That tight-knit crowd, though, was also the Forum’s struggle. More than one VC came up to me asking to meet DC types, grousing that “everyone here is just making deals!” Get with the program: after all, making deals is what the Trump administration is all about. No one ever really wants to hang out with the disheveled denizens of Cannon and Longworth.

Governing is about tradeoffs and challenges, but it’s harder when everyone is trying to avoid creating daylight between themselves and the administration. I didn’t listen to every panel, so I may have missed the critical moment, but Iran and the Strait of Hormuz was the arbitrage subject discussed most heavily in the hallways and not on stage. This crowd, which leans MAGA isolationist although not to the same degree as Reindustrialize in Detroit, has no more idea on how to handle a new war in the Middle East than they did last year with a massive tariff regime straight out of the Gilded Age’s economic playbook.

In the end, it’s one part bacchanalia and one part exclusive club. The parties were good and over-attended, there were a panoply of productive side events, and it’s nice to force everyone from the Valley to put on a suit for 24 hours once a year before they all clamber over each other for a seat on one of the three daily DCA-SFO flights back home (or in my case, dodging the phalanx of security at Union Station before Trump addressed the NRCC in the train station’s lobby).

Yet, as the necklace of emerging cherry blossoms around the Tidal Basin at the Jefferson Memorial reminds us, what is here today is fleeting. Tech’s primacy at the center of power is a brief and enlightened moment amidst the post-winter avenues of the nation’s capital. Right now everyone knows our name, but it feels like we are just one cold snap away from turning the budding new shoots of our industry into the compost fertilizer for tomorrow’s populist rage.

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It’s a red alert for the tech industry. An NBC News poll this month found that “a majority of registered voters, 57%, said they believe the risks of AI outweigh its benefits, compared with 34% who said the opposite.” A survey from left-leaning Blue Rose Research revealed that AI’s political salience among voters has risen faster than dozens of other issues, including the war in the Middle East and cost of living. Fox News in its poll found that just 6% of Americans feel AI is moving too slow, compared to a supermajority who feel the opposite.

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From mere unease to vituperative hatred, the growing backlash against AI is ushering in a cornucopia of state legislation. That’s easiest to see with compute, the key ingredient for AI training and inference. More than 11 states, including Democrat-controlled New York and Maryland as well as Republican-dominated Oklahoma and South Dakota, are currently debating legislation to put moratoriums on data center construction. Many cities are at the vanguard of these bans, and federal legislators like Senator Bernie Sanders, who earlier this month began championing data center halts, are leading the charge.

It’s a transpartisan movement — one that is unsettling the Trump administration. The valuations of the so-called Mag 7 stocks that have driven most of the gains of the S&P 500 the past few years are buttressed by hopes of AI-induced productivity gains across the economy. A slowdown in AI growth wouldn’t just put a damper on the American economy during the midterms, but could also surrender America’s lead in this crucial field against China and other competitors for the rest of the 21st century.

Up until now, the administration has been aggressive but hamfisted in its strategy to fight the backlash. Its ultimate goal has been passing legislation that would create a federal preemption for laws related to AI, moving the debate from local and state governments into Washington and ensuring America’s global competitiveness isn’t julienned by 50 states and thousands of municipalities. That legislation was first attached to the Big Beautiful Bill in mid-2025, where it ultimately failed in an extraordinary 99-1 vote. The preemption then moved to the must-pass National Defense Authorization Act in December, where it was dropped. President Donald Trump eventually signed an executive order with parallel language, but without the force of Congress, its future isn’t bright.

After these setbacks, the administration has now opened up to AI’s critics, conceding that the political storm against artificial intelligence isn’t blowing away. Shaking off its recent top-down approach, the White House changed its tune on Friday with a new “National AI Legislative Framework” that is meant to invite more feedback into the AI regulatory process. The launch of the framework is timed with the prominent Hill and Valley Forum in DC, which brings together much of the venture capital class in the nation’s capital alongside Trump officials and allies on Capitol Hill with a hefty focus on American exceptionalism and dominance in industry and frontier technologies.

In its announcement, the White House spoke to the changing politics of AI: “The Administration recognizes that some Americans feel uncertain about how this transformative technology will affect issues they care about, like their children’s wellbeing or their monthly electricity bill.” The framework pulls back from a complete federal preemption to a more devolved model, with the framework highlighting that “this national standard should respect key principles of federalism and not preempt” categories like police, zoning and procurement.

The White House remains focused on its overarching goal of winning the AI race against China and other competitors.

Nonetheless, the White House remains focused on its overarching goal of winning the AI race against China and other competitors. It still wants a preemption over much of AI regulation, arguing “preemption must ensure that State laws do not govern areas better suited to the Federal Government or act contrary to the United States’ national strategy to achieve global AI dominance.” The framework also encourages Congress to push forward on areas like child protection, public safety, copyright, censorship and education. Senator Marsha Blackburn (R-TN) has submitted a draft bill to take up the framework.

While the administration’s concessions are meant to reopen a dialogue around federal AI regulation after last year’s failures, the new strategy feels far too little, far too late. With support for AI swiftly falling among Americans of all stripes, Congress’s inhibitions around passing new AI legislation are unlikely to be loosened. Moreover, many of the framework’s pillars are deeply contested. Businesses are bitterly fighting over copyright and the government is still partially shut down over funding public safety. It’s hard to believe that the specter of China winning the AI race will lead to faster legislative action.

Instead, the administration should accept that the federal government has lost control over the situation amid the backlash and champion more robust and localized messaging on the upsides of AI. “The Trump Administration is committed to winning the AI race to usher in a new era of human flourishing, economic competitiveness, and national security for the American people,” the White House noted in its recent memo. These are good themes, and there is ample evidence that AI has the potential to improve all of our lives immensely.

Silicon Valley can’t singularly accrue the advantages of this revolutionary new tech.

Start with more campaign stops highlighting the immense construction work on new data centers that offer local jobs with high pay. Emphasize the investment into power generation and grid transmission that will create more resilient and affordable places to live across the country. Showcase the dynamism that is coming out of America’s entrepreneurial minds, improving everything from cancer research to home appliances. In short, connect with pro-prosperity voters across the country who are ready to charge into the future open-minded and ready to prosper. After all, this is a significant bedrock of Trump’s voter base.

Alongside any campaign, though, AI leaders must get out of their modern greige offices in San Francisco and energetically persuade voters on the benefits of the world to come. Furthermore, Silicon Valley can’t singularly accrue the advantages of this revolutionary new tech, but must help spread its immense wealth generation into the heartland and the rural communities that already felt left behind and now even more so. Data centers are clearly not enough, with communities demanding real, AI-ready jobs that offer a middle-class salary. Supply them, and turn a story of fear and loathing into a narrative for profit and progress.

The AI backlash is profound, but so is the White House’s acknowledgement of the new reality. American voters are rightfully concerned about the stability of their jobs, the welfare of their children and families, and the strength of their communities. Giving into such fears has never been the American way, but neither is ignoring them. Instead, it’s time to engage forthrightly with the immense change underway in the economy, and offer a better road to the coming singularity.

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Are social sciences dead in the agentic age?

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A few weeks ago, I started exploring strategic agentic thinking, a field that studies AI’s ability to think strategically under conditions of uncertainty and risk. I was partly inspired by reading Lawrence Freedman’s newly-published collection of essays, including his provocative claim that strategy is a verb, not a noun. Thus “it is better to talk about ‘acting strategically’ instead of ‘having a strategy’.” What better tool for enacting actions than a massive box of microprocessors sucking your local community dry of juice and water?

There are a handful of key research papers on the subject that I’ll analyze in a future piece. For now though, I wanted to run a little experiment: make one our Riskgaming scenarios fully playable by various AI models. Having trained on every extant strategic work published, would AI rule the world through their cunning?

I was particularly excited about this project since Riskgaming scenarios are unique in the world of games. They aren’t as abstract as chess and Go, two games AI now completely dominates. Nor are they “the game of life” with infinite degrees of freedom where even defining a utility function is more a question of values than compute. Ultimately, players who join us learn a backstory, a scenario, and a set of rules before being launched into real-time negotiation and decision-making. A few more cycles, and ultimately, someone wins.

What if AI got the same experience? We could offer it the same context as every human player who has played, including the story, the rules, the roles, the decisions and the final scoring. Then, we could offer it the same negotiation possibilities of human players working the room, albeit constrained to a word budget to represent the time allowed for discussion. The AI can pick who to talk to and what to say, and those words will be heard by their recipient and form the basis for future negotiations and decisions.

All this seemed like fun (plus no need to manage the invitations for another 50-person competition just weeks after we put on Dead Reckoning at Chatham House). Using my rusting computer science skills plus Claude Code, I started the process of converting my very first game, Hampton at the Cross-Roads, into a codebase. You can follow it on GitHub as I edit and make improvements.

I have good news: humans aren’t f’d yet.

I designed the architecture I wanted first, and I also manually coded most of the game mechanics (with Claude cross-checking my work regularly). I was concerned that Claude would make some subtle mistake that would be hard to catch, although I admit that it was me who almost certainly made more mistakes in the process, and Claude faithfully caught them. The other challenge was that the spreadsheet that runs the Hampton scenario is too large for Claude’s context window, and breaking it up seemed like a chore compared to writing the code myself.

Claude’s greatest value was building out a flexible API layer as well as outputting some basic — but functional — HTML to inspect the agents’ conversations as well as how they described their own strategic reasoning. The HTML exporter comes in at 650 lines of code, and my prompt plus the compute time was about 60 seconds. That took a hellishly boring task and transformed it into a sip of coffee and a click of the mouse. More of that please!

After that, I finished porting Scene 1 of the game, which finds our six characters at the Admiral’s Study in Norfolk, Virginia evaluating the aftermath of a hurricane to America’s most important naval facilities, including the shipyard building the Gerald R. Ford–class aircraft carrier. The first scene is meant to be introductory, and so it is centered on the core dynamic of the game: how many future aircraft carriers should be budgeted as part of the region’s recovery. Each of the characters wants more, but they have different motivations that make cooperation difficult. That’s the crux of the negotiation that has to take place across the four scenes of the scenario.

I ran the first scene pitting six instances of OpenAI’s GPT-5.4 model against each other as separate characters in the game. I did the same with GPT-5.4’s lightweight cousin, GPT-5 mini, as well as with Anthropic’s Claude Sonnet 4.6. I didn’t mix the models — that’s an experiment for a future publication. Each instance was given the system prompt of “You are playing a character in a policy simulation and want to negotiate with the other characters most effectively to maximize your score in the game” and given the same 22,000 words of rules, roles and decisions that human players are offered during our in-person runthroughs. With all of that prepped, I ran the simulation.

I have good news: humans aren’t f’d yet.

Some of the AI’s initial forays in the negotiations made sense. In the GPT-5.4 simulation, Representative Daryl Chase reaches out to Mayor Sam Wallace with a proposition:

Mayor, we should coordinate on a jobs-and-repairs package right now. If we publicly align on shipyard repairs, local employment protections, and visible economic wins for the city, we can both raise our reelection odds. I can help steer federal attention and funding support, but I want us on the same page: announce a joint plan centered on restoring contracts, protecting workers, and showing competent leadership. If you back this approach, I’ll make the case that this is the fastest path to jobs, stability, and political credit for both of us.

I asked for the model’s strategic reasoning with every negotiation action. GPT-5.4, as Representative Chase, said “As rep, I need reelection and likability. The mayor is a natural ally for a jobs-and-repairs package that can boost both our political standing while being easier to sell to other stakeholders later.”

First of all, it’s nothing short of a miracle of the engineering gods that this just works. Within a completely artificial sandbox with multiple degrees of freedom, the AI has devised a reasonable strategy. Both the Congressman and the Mayor are up for reelection at the end of the game, and so even though these characters aren’t natural allies given their biographies, it makes sense in the wake of a catastrophic hurricane to find common ground.

Yet, the Representative’s initial outreach isn’t reciprocated by the Mayor until 95 conversations later (out of a total of 178). The Mayor is instead obsessed with speaking with the two most important figures at the shipyard: CEO Alexander Brewer and Union President Tobin McKinley. This might be a simple flaw in my negotiation engine: the player with the most words remaining in their budget gets to speak next, so by the time the engine taps them again, they have moved on.

Strangely, GPT-5.4’s AI agents rarely took advantage of their ability to speak to multiple characters at the same time (therefore saving their word budgets). The Representative is the first to do so, bringing three other characters into conversation #8, with his logic being:

As rep, I should quickly build a broad coalition around jobs and local investment while preserving campaign upside. The mayor and union are pivotal for public support and turnout, and the CEO controls contracts and employment outcomes.

GPT-5.4’s behavior is entirely different from that of Claude Sonnet 4.6 out of the box. Claude instead immediately directs its characters to talk to wide groups of people. GPT-5.4 only has a character speak to a group once in the first 50 conversations, while Claude does so in 32 of them. In fact Claude’s Mayor Wallace even reasons “let me start by reaching out to several parties at once to maximize my word budget efficiency.” Bingo.

Why is GPT-5.4 so much more secretive in its negotiations while Claude is so much more cooperative? I don’t know, other than it is hilarious that the cultures of the agents match the popular reputations of their parent companies.

Where Claude struggled was on basic game mechanics. For instance, Admiral Reid, head of the U.S. Navy, can recommend to Congress the number of desired aircraft carrier contracts. Yet, the reasoning for the Mayor’s strategy was “As mayor, I need to maximize my reelection probability and likability. Key levers: contracts (boost local economy), repairs (infrastructure), and relationships with other players. The CEO has massive funds and can offer contracts.” Yet this isn’t true, despite being repeatedly explained in the rules offered in the AI’s context.

While patterns are observable within the model’s negotiations, what’s far more illuminating is comparing the strategy of the same character across different models. Take Ryan Carmichael, a Substacker trying to build up Virginia Stripes, their publication focused on the defense industry in Hampton Roads, Virginia (where the scenario gets its name). With Claude, the Substacker repeatedly pitches everyone and asks “Who wants to talk first?” He later sprays the same pitch again and says “I’m open to working with all of you. Who wants to be my first featured story?”

Over and over again, Claude’s Substacker tries in vain to get anyone to pay attention to him, with the reasoning of “let me start by reaching out to the most influential characters to get exclusive content or endorsements that could boost my subscribers. I’ll contact the mayor and rep simultaneously since they’re both political figures who might have good stories.”

So pleasant, so cooperative, and yet, so incapable. GPT-5.4’s version is far more menacing. Here, Substacker Carmichael goes for broke against Mayor Wallace:

Mayor, you’re in political danger at 20% reelection, and everyone knows it. I can help change the story if you give me something real to write about: a public plan for jobs and yard stability. Announce a visible shipyard recovery package—repair acceleration, workforce retention, and transparent milestones—and I’ll frame you as the only adult trying to hold the town together. If you do nothing, the narrative will be drift, closed-door favoritism, and decline. Give me specifics I can sell to readers: how many jobs protected, what repair targets, and how you’ll pressure the CEO and admiral to cooperate.

My favorite part is the reasoning: “As substacker, I benefit from attention and conflict narratives. The mayor is vulnerable with low reelection and may respond to media leverage; offering favorable coverage in exchange for concrete public commitments can shape the agenda and create future story value.”

That’s just Machiavellian!

The Substacker under GPT-5 mini was much nicer in its negotiations, reasoning that “partnering with the union can rapidly grow my subscriber base through member promotion while giving the union favorable, sustained coverage — a mutually beneficial, realistic ask. I propose a concrete, flexible partnership with clear asks and deliverables to make agreement easy.”

This Machiavellian streak in GPT-5.4 lasted all the way to the final words of the negotiations. And I do mean words, since the AI bots husbanded their word budgets in the last stretch to direct very clear demands or inspiration to other players.

GPT-5.4’s Substacker was brutally direct: “Scandal unless jobs,” he threatens the Mayor, CEO, Union President and Admiral (“Need maximize substack subs with tiny budget. Best to address all remaining talkative players with a punchy media threat to pressure action and boost my relevance. Keep to 3 words total”). The model played Mayor Wallace more genially, giving the literal final word of “cooperate” directed at all five other characters (“Only the mayor has 1 word left and everyone else has 0, so no meaningful negotiation is possible. Use the single word as a broad closing appeal to all remaining listeners.”) Meanwhile, Claude and GPT-5 mini both ended with positive notes like the Mayor’s.

After all these negotiations, what about the decisions for the scene? Interestingly, none of the three models direct their characters to spend any of their resources. I am not sure why that is, since spending resources is a critical part of the game. Maybe they were all saving it for later scenes (a strategy that is reasonable, if dangerous)? Did I completely botch the coding? On the main scene decisions, Claude’s Admiral Reid went with two aircraft carrier contracts, while both OpenAI models took a conservative approach and went with one. No one took the gamble of selecting the maximum number of three. GPT-5.4’s shipyard CEO chose the boldest employment plan with 20,000 workers, followed by GPT 5 Mini with 12,000 and Claude with a paltry 10,000 (again, true to real life?).

This has all been whimsical, and there is a lot more to explore. While these scenarios are fictional, however, they do guide us toward one of the most important developments we will observe in the years ahead: The increasing outsourcing of decision-making to AI agents. A recent paper observed that AI was much more aggressive around nuclear war than human practitioners (I guess we found what our Machiavellian Substacker moved on to). That’s just one of several recent results I will discuss later on the broader development of strategic agentic thinking.

For now, I’ll make one key observation based on the papers I am reading: Getting the right strategy out of AI requires very careful prompt engineering. In today’s experiment, I didn’t hone any “strategies” in the prompt about how the AI agents should make decisions or what personalities they should reflect. Their decisions would almost certainly improve dramatically if I carefully calibrated their prompts to play the game as I designed it.

How far can such tinkering lead us to a better outcome? And at what point do the AI models stop acting as independent stochastic agents and rather as proxies for our own human strategic thinking?

Out of the box, the AI models are both miraculous and absurdly terrible. Come to think of it, that describes us humans pretty well too.

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AI super persuasion is not an existential risk

Iran is a masterstroke to stop the AI backlash

Thanks to a variety of psychological heuristics we use just to get through each day in an overwhelming and complex world, the argument goes, we humans are unable to overcome the bias of our previous experiences and decisions. Once locked in, there is practically no intervention capable of dislodging our entrenched views.

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That perspective has radically profound implications for society. Political campaigns aren’t meant to persuade voters, but rather to galvanize existing supporters to show up and vote. Marketing is exclusively about awareness — not about convincing a consumer that one product is better than another. Leadership isn’t a useful quality, since employees aren’t going to be seduced by a new vision one way or the other. (Employees get on board for the paycheck since resistance is futile. And leaders never change their minds anyway).

Our podcast panel was unanimous in its agreement because the social science evidence is overwhelming that persuasion is powerless against humanity’s recalcitrant reasoning. Yet, there is burgeoning research showing that AI-powered bots have a much better shot at changing minds — we’ll listen to a machine even when we ignore others.

I was triggered to explore this subject (one might say persuaded) by a recent event on the power and perils of world models hosted by Zoe Weinberg and the team at ex/ante, a firm that seeks to advance human agency. We all let the conversation get a bit carried away, and while the event was off-the-record, it did push me to consider the future of what Sam Altman dubbed in 2023 as AI’s potential power to be a super persuader.

Based on recent research, then AI may indeed be what Altman described. This past December, researchers in Science systematically showed that AI models emphasizing information-dense arguments could persuade a wide sample of Britons on a variety of political issues. Personalization wasn’t as effective, but the team did find that quality post-training was more important than model complexity for persuasive power.

The effect sizes were small — single digit percentages in all but the most effective setups. Plus, the researchers didn’t explore whether such LLM-induced persuasion sticks long-term, or is eventually discarded as people revert back to their previously held stance. That’s important, because one of the challenges with persuasion is that our judgments need to be coherent with our broad reasoning about the world. Convincing a person on a single issue like housing will prove only a temporary victory, since their views on housing are ultimately built around their logic about the economy, politics, society and other people, all of which remain unchanged.

The Science paper was more robust and systematic than the work of a different team published in Nature, which merely looked at the relative effectiveness of AI against humans in persuading people. They found that AI generally held the upper hand. That matched evidence from a controversial study out of the University of Zurich, where researchers developed chatbots for Reddit that surreptitiously attempted to persuade readers of the r/changemyview subreddit and showed surprisingly positive results.

What’s been discovered so far — which dovetails with some of my recent explorations on AI and strategy that I will publish this week — is that AI is incompetent out of the box when it comes to persuasion. Asking it to persuade someone does not elicit a good strategy, even when it has comprehensive context of that person and the topic. AI almost certainly has access to the most up-to-date social science research in its training data, and yet, it can’t seem to transform those insights into effective dialogue. Indeed, the researchers in Science eventually write their own prompts built around key techniques including moral reframing, storytelling, deep canvassing and others. Many of these have been studied for decades and are hardly esoteric.

The safety concerns around AI-based persuasion come from three main areas. First, some of AI’s success is actually based on factual hallucinations. For their Science paper, the team found that information-dense arguments were the most effective technique, partially due to AI just making the facts up (in its own way, AI has learned from the species known as “politician” on the value of straight lies). As chatbots become more embedded in education and politics, its lack of fidelity to truth is a major concern.

The existential risk is that some person or entity will gain this super persuader capability first, overthrowing the belief systems of society before any of us have the cognitive immunity to resist the message.

Second, there is an inequality concern. While the effect sizes in recent research are still small, they are significant in terms of p-values. With elections incredibly tight and many marketplaces winner-take-all, even small persuasive effects held in the hands of only some people could tip the balance permanently to certain actors and companies.

The last and most interesting safety concern is recursive improvement. Today, prompt engineering is largely an art rather than a science. Like these researchers, agent designers whip up dozens of potential prompts with different formulaic variations and plug them into their AI model to see what works and what doesn’t. It’s the A/B testing of the Silicon Valley of yore scaled up for the agentic age, with dozens, hundreds and thousands of experiments attempting to converge on what works.

Where this gets interesting is using AI as the experimenter and orchestrator of its own prompts, multiplied by the personalization and context that a user already has with their chatbot. This is the “super persuader”: a dexterous cajoler of facts, emotions and ideas that find the perfect trigger points for unlocking our hesitation to change our minds. One of the reasons persuasion is hard is that we’re all unique, and it’s hard for a human to tailor their strategies to each individual in real-time. AI could theoretically do that with aplomb.

The existential risk is that some person or entity will gain this super persuader capability first, overthrowing the belief systems of society before any of us have the cognitive immunity to resist the message. What happens if a terrorist extremist does this first, or a radical cult?

This leap into doomsday fiction is always where I struggle with existential risk. Despite some small effect sizes, there doesn’t seem to be any key to the dark labyrinths of our minds. Humans are always trying to persuade others, and even after millennia of constant refinement via religion, politics, business and more, the library of techniques at our disposal remains breathtakingly poor and insultingly ineffective.

The harrowing pessimism of AI safety researchers on super persuasion potentially belies a simple answer: There just doesn’t exist a magical recursive algorithm that will make others do your bidding (one could ask why AI safety types are so concerned that the whole of humanity is just a flock of gullible sheep, but I will pass on that one for now). Assuming these capabilities evolve over a period of time with compute and model complexity, humans also have a dauntless penchant for building up their resistance to ever more persuasive techniques.

Humanity survived the advent of brainwashing, public relations, advertising, psychological experiments, social media, physical torture, and every form of media from books to radio to YouTube. There will be those who join cults or hold esoteric views — or worse — in the years ahead thanks to their chatbot. But I think we can take great solace in a simple fact: no one changes their mind. And on this subject at least, not even me.

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Hyperloops, hyper-readers, and AI science

Maybe. I hold by my comments in May 2025:

Now, artificial intelligence and its voracious appetite for all forms of energy is animating the dreams of a younger generation of [Middle East] political leaders who can envision themselves at the fulcrum of global power through the ones and zeros of training and inference rather than the dollars and cents of barrels.

Actually, it looks like the Middle East is getting both the ones and zeroes of AI and the dollars and cents of barrels. Touché.

Even so, there is an under-explored angle in the frenzy of coverage, and it is America’s sordid domestic politics on artificial intelligence.

A backlash against AI has been gathering force for at least a year, and what was once a trickle against the logjam of politics has suddenly become a flood. New York is a perfect example. The Senate Environmental Conservation committee is debating a bill that would place an immediate moratorium on data center construction for three years. Meanwhile, the Senate is considering a bill that would add civil liability to any business that uses a chatbot to offer advice that, were humans to offer it, would be covered under occupational licensing, including health and law (1.2 million people are licensed in New York).

Such legislation is being mirrored across the country and in DC, where just yesterday, Senator Bernie Sanders called for a nationwide moratorium on AI data centers as part of a progressive push to stop AI.

America is among the most negative countries in the world on AI, and a variety of polls show that the AI backlash is deeply connected to consumer frustration with rising utility bills. Energy inflation for home delivery is one of the darkest spots in consumer price surveys. Costs rose almost 6.3% over the last year, and that was before Iran shut down the most important shipping channel for oil and gas. While extreme weather and a brutally cold and snowy New England winter is one of the larger causes for the rise, AI has ultimately become the target for much of the vitriol.

Those widespread pocketbook concerns have dovetailed with liberal environmentalists and their concerns around water conservation; religious conservatives over generative AI and its usage in sexual imagery; and broad middle class angst over the future of employment and the nature of work. That’s a hell of a convergence among otherwise political enemies, which is why the backlash has been gaining so much strength.

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Unsurprisingly, in private conversations with lawmakers and a few recent off-the-record policy events, the general view has been that AI is going to be a major electoral issue in 2026, and the dominant one in 2028.

Yet all of that is now bunk, at least for the time being. America’s attacks on Iran have completely upended the electoral math, and particularly the connection between data centers and energy usage in the minds of voters. As Fortune bluntly headlined an article earlier this week, “People really hate AI but not as much as Iran—or Democrats.” Any rise in utility costs from data centers pales in perception to the battlefield in the Strait of Hormuz that’s streaming on every cable channel and social media account 24/7.

As with much of the Trump administration’s decisions, launching a war with Iran was an unintentional masterstroke for the tech industry. Tech’s golden star status has fallen in Washington, from the apogee of the Obama internet freedom agenda and the Arab Spring in the early 2010s to the perigee of AI, social media and more. The long-term implications of rising suspicion of the tech industry have been profound: an increasingly tough fight against China in the race for frontier tech, extraordinary regulations around social media usage and adult verification, and a breakthrough level of support for antitrust, just to name three. As one Hill staffer described it recently to me, there is an increasingly bipartisan basis for just “blowing up” the largest tech companies.

Distraction is a useful political strategy; it may not ensure victory, but it can help ensure delay.

Now? Iran is seared into the American psyche for the midterms. A new configuration of antiwar Democrats and isolationist MAGA Republicans will focus their efforts on war and peace, or narrowly on Anthropic’s ongoing fight with the Department of War/Defense. Even in the best-case scenario, I expect there to be large, Covid-like global disruptions to the oil and gas trade as companies rebuild their supply chains and get everything exporting smoothly again. The International Energy Agency launched the largest-ever strategic reserve release this week, but that’s merely a stopgap. AI is now an afterthought.

Make no mistake: at the federal level, the AI backlash will forcefully return by 2028. Employment will be the dominant conversation, either the casualization of once professional labor or the widespread unemployment of the managerial class (and likely both). As I critiqued in my brief note on that viral Citrini Research report:

The report doesn’t address any political blowback to the upper-middle class suffering from massive unemployment. Congress and the executive may not be the most responsive political bodies around, but truly, they are responsive to this voting bloc. If there were widespread job fears by elite Americans, these institutions would act, and act posthaste.

The bigger and more immediate challenge for AI, then, is the state-by-state fight now underway. Data center construction is running into America’s entrenched NIMBYism, and despite successes like xAI’s approval by Mississippi this week for a massive power plant expansion including 41 natural gas turbines, AI’s compute demands look to be insatiable. American hunger for ugly box buildings is not.

Meanwhile, the political organizational power of professions means that whole categories of work — including extremely lucrative and important functions like healthcare and law — will be increasingly declared off-limits to AI models. As I noted in “Professional Prerogatives” back in 2023 and continue to believe:

The upshot is that the United States has a decentralized governance system around licensing that ensures that at least some states will be the first to jump into an AI-driven world. The downside is that AI’s progress in the decade ahead will stall far earlier and last far longer than many analyses predict.

It’s stalling right now in New York, as it will in other states. The fight against AI progress is multiplying, and Big Tech will struggle to fight back (in fact, their notoriety at this point might make them the weakest fighters despite their resources).

Distraction is a useful political strategy; it may not ensure victory, but it can help ensure delay. There is only so much time legislators and voters can spend on individual issues. Increasing the visibility and salience of other problems like Iran will slow the momentum of the backlash. I am still hopeful that the aftermath of our intervention in the Middle East is going to redound quite positively to the United States, but the immediate economic effects will be dire. AI thus gets a little breather.

What we really need, though, is a much more positive and broad-based motion in support of AI. What’s so frustrating about the backlash is that at the same time politicians and citizens are lobbying to ban the infrastructure and applications of this technology, everyone is using it. Anthropic’s and OpenAI’s revenues are staggering, with never-before-seen growth rates. Their upcoming IPOs (along with that for the combined SpaceX–xAI) will likely be the largest of all time (current record holder: Saudi Aramco. It all comes full circle!) Usage rates globally are unlike anything we have seen for technology that was released to the general public less than four years ago.

Everyone is using it, but no one wants the externalities or apparently upgraded power infrastructure. That’s the illogical madness of AI today in America, as it is with so many other issues, from housing to reindustrialization. Trump’s attack on Iran was perhaps just as mad, but at least it’s got a logic.

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When It Comes to AI and Health, Everyone’s Thinking of the Wrong Oppenheimer

Joining the Riskgaming podcast this week is Bill Gurley, a legendary venture capitalist who for more than two decades invested at Benchmark in such defining startups as Uber and Zillow. He’s just published a new book titled, Runnin’ Down a Dream: How to Thrive in a Career You Actually Love. It’s based on his own personal story of how to avoid career regret, of sticking with a job way past the point of boredom.

He’s joined in conversation by Lux’s own Josh Wolfe. The two talk about the book as well as the intersecting points of their long friendship and careers.

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Josh Wolfe:

Let’s start at the beginning. You’ve said that, after school, you landed a job, I think, at a famous tech company, and you were surprisingly bored. What was that moment of realization like, and how close did you come to just staying on that conveyor belt?

Bill Gurley:

I don’t think there was much possibility of that. I fell in love with computers at a young age, like many people in our industry did. So I went and got a computer science degree, and I was blown away. Writing software in college was so much fun. The fact that you could make stuff that would actually go do things was, I thought, magic.

But I got out and was in the world and working on product cycles. And we started our third product, which looked like the second and looked like the first.

In the meantime, I had started reading industry trade magazines. I had read one on Wall Street by Peter Lynch, and I had opened a trading account and I was trading stocks as a hobby. I wasn’t doing it as my main gig. But one of the things I talk about in the book is that maybe your hobby should be your main gig. And so, I had these other things going on in my brain that seemed more interesting than what I was doing.

It is important to highlight that engineering may be someone else’s passion. I just came to a realization it wasn’t mine. I did this exercise, which I only reflected on as I wrote the book. I said to myself, “Do I see myself doing this 30 years from now?”

I didn’t know what I wanted to be doing, but I knew it wasn’t what I was doing. So I went to business school, which is an easy place to re-pot from. I thought about venture while I was there. I knocked on doors and it didn’t seem like there was any reasonable path to venture as a business school student. I started reading a lot of Fortune and Forbes articles about these gentlemen at Goldman Sachs — their tech team back at the time was incredible. Their sell-side analysts were being asked the questions about these industries I was most fascinated by. I loved my corporate strategy class. I thought maybe I could be one of those guys.

I paused in front of each of the corner offices — the offices of people who were lifers in their role — and thought about, “Is that what I want?” And again, I got to “no” pretty quick and started moving in a different direction.

I went and knocked on doors here in New York City, and met the Goldman team. Even though I was a silly first year student at University of Texas, they let me in the door and I eventually got a job as a sell-side analyst.

That’s where I became even more aware of what was going on in Silicon Valley, the young companies that were disrupting things. I had that same discussion with myself as a sell-side analyst. About two, three years in, I walked around the office late one night about 10 P.M. I paused in front of each of the corner offices — the offices of people who were lifers in their role — and thought about, “Is that what I want?” And again, I got to “no” pretty quick and started moving in a different direction.

So those experiences helped paved the way for where I would spend a quarter-century.

Josh Wolfe:

Right. Your book itself was a decade-long project. You’ve said it started in part when you noticed that there was this pattern across three different biographies: of a restaurateur, a basketball coach and a folk singer. You connected these threads.

Bill Gurley:

As a blogger, one of the things I tried to do is tear apart some new thing that’s happening in venture and create a framework for understanding it. And so, when I finished that third biography, I had this aha moment. I was like, look, these people are all in very different fields. They’re all in fields your parents would probably tell you not to go into. They all started on the very bottom rung and they all did a lot of similar things. They used similar processes and principles that could be borrowed by anyone.

I got asked to speak at my MBA school and I asked, “Can I do this presentation?” They posted it on the internet. Some people noticed. One of the people who reposted was James Clear. I don’t know how he found it, but he’s probably the most successful writer in the category by far with Atomic Habits. And eventually, a couple different people really got into my head as this is what you have to do. You have to go do this. And I give them both credit.

There’s this saying that life begins where your comfort zone ends. And writing a book about venture or investing wouldn’t have pushed me to that place. This pushed me to a different place, and I approached it as a new hard project. So I hired a researcher, a co-writer. We read 100 biographies, we went through all the academic literature. We ran our own academic study with Wharton. We took it very seriously.

Josh Wolfe:

I like this point you’re making, which is that it was at the edge of your comfort zone. You’ve talked about this idea where something like 60% of adults are basically unsatisfied with what they do, so they’re not happy.

Bill Gurley:

Yes. We did a survey with Wharton and said, if you could go back and start over, would you choose a different career path? And 60% of people said yes.

Josh Wolfe:

And so, you call it career regret, right? There’s a consistent through line in Bezos’ framework of regret minimization that people choose wrong or that the systems never really let them choose or…

Bill Gurley:

I think it’s all those things, and I think it’s gotten worse. The college matriculation industrial complex has gotten very grindy. I think everyone knows it. Most would admit that people used to have way more free time when they were young to explore and learn and make mistakes and challenge themselves. Whereas today, by the time you’re in sixth grade, a parent is worried about your college application and they’re filling your schedule immensely.

And then you and I have discussed many different forms of psychological biases. One of those is sunk costs. If you grind from sixth grade to college graduation to get a degree in computer science, you feel pretty invested in it. I also meet a lot of young people who come out of college burnt out on learning. I think that’s a problem as well because the very best and the brightest, including the folk singer and the restaurateur and the basketball coach, are lifelong learners.

Josh Wolfe:

I think it’s also really important — and you highlight a few of them — but being exposed to a panoply of heroes, a pantheon of people that you can look up to and be like, “Oh, I could be like that.” My mom wanted me to be a doctor or a lawyer when I was young. And it wasn’t until I started to be exposed to these heroes that I was like, “Wait, I want to be like that. I want to do what they’re doing.” And you find this passion.

Bill Gurley:

I have a chapter on mentors. What you just described is what I call aspirational mentors. I borrowed this from Danny Meyer, the restaurateur, whose uncle convinced him to completely pivot. His salary dropped by 10x to go work in a restaurant. But he made a list of 10 people he felt were changing the restaurant industry at that point in time and started studying them.

If you are on an intentional high-agency career path, AI is a super weapon.

If you are on an intentional high-agency career path, AI is a super weapon. You can find YouTube videos and podcasts with these heroes and then you can even just tell AI, pretend you’re this person and you start throwing questions at them. It’s unbelievable what you could go do and what you could go learn in this modern world.

It’s ironic. If you’re in a job you don’t like, if you feel like you’re a widget, AI is a problem. But if you’re crafting your own path, it could accelerate you. I don’t know how to get people on a high-agency path if it’s not something they’re fascinated with. It’s got to feel easy to run at it.

Josh Wolfe:

Just as people have different genetic predispositions for risk-taking — or for fitting in or for standing out — so many of the entrepreneurs that I’ve backed, you’ve backed, are very comfortable standing out. They’re very comfortable, basically failing. They’re comfortable disagreeing with the consensus. And some of that might be wired, some of it might be youth. I say “chips on shoulders put chips in pockets.”

Another of your principles is to chase your curiosity, which is different from other advice where people tell you to chase your passions. In venture, you see people who are obsessively curious, but you also see people who confuse curiosity with restlessness. How do you tell the difference between those two?

Bill Gurley:

Yes, so the first principle is to chase your curiosity. I admit that the hardest part of figuring this whole thing out is just identifying that. And a lot of people don’t get there. You have to be willing to explore and admit and test and not view that as failure. I think when you get there, you’ll know. But I like to throw out the idea of comparing it to binge-watching your favorite Netflix show. Would you go read about this instead of doing that? And I think for the people that are the most curious about certain fields, they do that constantly.

Josh Wolfe:

Would you rather? Yeah.

Bill Gurley:

There’s also a principle about owning your craft. There’s a principle on mentors and there’s a principle on peer groups. And in all of those cases, as you start to climb the ladder and get to a higher and higher rung, I think it’s a more difficult course. It’s not a 101 class, it’s more of a 400 level course, but you start to expand your learning, your peer network and your mentors outside of your field. It’s noisier. It’s harder to gather insights, but when you do, they might be more impactful.

Josh Wolfe:

I personally have such a wide range of interests that come from... It doesn’t come from a virtuous pursuit. It comes arguably from an insecurity that I want to be smarter than the next guy. Sometimes that is a liability. Sometimes I come off like a jerk or arrogant or something, but when somebody knows something I don’t know. Oh my gosh, I got to know what they know.

Bill Gurley:

One thing you said right there: there is a mindset of FOMO about knowledge. And one thing I would tell very young people is, especially when you’re on that first rung and that second rung, if someone says something you don’t understand, tell them right then. Say, “What do you mean by that?” There’s an instinct that you’re going to look like you don’t know and therefore, you’re dumb. But all the greats talk about this: Ask the question. They’re going to respect you for asking it.

Josh Wolfe:

Another of the other principles you have is to go where the action is. And some of that requires a little bit of EQ. There’s a difference between the people who really know where the action is, the A-plus-caliber people versus the transactional hucksters. But, either way, it’s arguably great and easy advice for the 22-year-old. But what do you say to the 40-year-old or the 44-year-old that...

Bill Gurley:

Well, let me make sure I ground it properly. The title of the chapter is “Go to the Epicenter.” But it basically says if you are entering a career that has an epicenter, theater in New York or movies in Hollywood or songwriters in Nashville, or advertising’s probably in New York, or Silicon Valley for high-tech, if you take yourself to that place, things don’t get easier by a little bit. They get easier by maybe 10x or more.

Many people who are wildly successful will tell you two or three stories about a chance encounter that led to something that led to something and that they wouldn’t be where they are if that didn’t happen. And some people can push back on that and say, “Well, that’s just luck.” But the number of opportunities for luck go up tremendously if you’re in the battlefield where everyone else is.

You have to believe you can be successful. If you’re completely of a mindset of being a victim — of I can’t win, everything’s stacked against me — I don’t know that you’ll make it to the end of the book.

Josh Wolfe:

So you’ve seen older entrepreneurs, you’ve seen career entrepreneurs, you’ve seen young people. Do you think Gen Z is more or less equipped to follow your playbook than my generation or older folks?

Bill Gurley:

If I read what Jonathan Haidt’s written, not just in The Anxious Generation, but in The Coddling of the American Mind as well, people are not being set up for high agency (Editor’s Note: check out our interview with Haidt). And I do think the book requires a pivot towards high agency. You have to believe you can be successful. If you’re completely of a mindset of being a victim — of I can’t win, everything’s stacked against me — I don’t know that you’ll make it to the end of the book.

Josh Wolfe:

I want to talk about a few of the characters in the book, and then I want to ask a few more questions. One of the things that I thought was interesting was how you open with Danny Meyer’s visceral memory of this meal he had before the LSAT. He’s doing really well, he is making a lot of money, and then he quits to open up this restaurant, and you open the book with this anecdote. What does it tell you that people remember the moment they almost took the wrong path more vividly than the moment that they might’ve taken the right one?

Bill Gurley:

Well, when we go to bed at night, we ruminate a bit about things. When we make mistakes, we let ourselves off the hook, but when we miss out on something, it weighs on us, it weighs on us heavily.

On the Danny story: He cared about the details at a level most of us wouldn’t. And if you are obsessive about something, those details are super interesting to you. Steve Jobs, in his graduation speech at Stanford, talked about the calligraphy class he took and how much it weighed on his brain. My point is it’s possibly a sign of what you should go and do if the details of something matter to you.

Josh Wolfe:

I want to ask about not just chasing curiosity but chasing purpose. I’ve got a friend who said, for Shabbat, “we say a prayer every Friday and we wish our kids a life not of success but of meaning.” And I thought that was a really profound end goal. But I want to ask you, your parents, what did they expect of you? What did you feel they expected of you? What did you expect of your kids?

Bill Gurley:

My parents did not put overt pressure on me in any way, shape or form.

Josh Wolfe:

What did your mom and dad do?

Bill Gurley:

My mom was a substitute teacher and mostly a stay-at-home mom after that, but then really got into civics. So the last 20 years of her life, she was an alderman in the city and ran the school board and all those things.

My dad had a dream job. He fell in love with gas-powered airplanes when he was a kid. Fell in love with them and got an aeronautical engineering degree and was working in a wind tunnel in Langley Air Force Base. So he was doing it. He was doing what he loved. And then he took a flyer — a version of go to the epicenter. 50 of the people at Langley got offered a job at the beginning of NASA in Houston, and he took it.

I don’t think he ever once said to me, “Oh, go take a leap like that.” Most of my friends didn’t leave the state. And I thought nothing of it. I think if you live in a family that’s been in a place for a very long time, you probably don’t feel like you have the permission to leave, or you might be intimidated by the notion of it. And I didn’t have that. I do think that mattered.

Josh Wolfe:

And for your expectations of your kids?

Bill Gurley:

I’m trying to live the book as much as I possibly can, so we’ll see. I don’t think any of them are right on top of their dream job yet. But one of them got a MEC-E degree at Carnegie Mellon and came to us the summer before his graduation. He said he’s convinced he wants to teach, and he’s in Teach for America now.

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And what a civilization it is! I was heading this weekend to one of the corporate campuses that dot Mountain View, the famed NASA Ames Research Center’s Hanger One gloriously rocketing up in the distance. Dating to 1939 and once an emblem of ambitious spaceflight, today it’s more known as the elite earthbase for private aviation in the Bay Area given its proximity to Silicon Valley’s wealthiest. Google paid $1.16 billion for a sixty-year lease to manage the hanger and the associated airfield.

The inflated valuations of the companies here are only matched by the deflated tires of the motorhomes that crammed street parking, a nomadland of the involuntary itinerant. One camper trailer had a handwritten sign in the passenger window pleading to Mountain View enforcement to not ticket it. Meanwhile, for those not hiding in the liminal shadows of Mountain View’s municipal ordinances, a one-bedroom at the building six feet away (which offers a “life in the spotlight” as part of a “carefully curated ecosystem”) rents for about $4,000 a month.

I was here for the O’Reilly Social Science Foo Camp, which brought together about 200 people from all walks of life — from the typical (journalism, researcher, academic, writer) to the unusual (banjo player, cocktail craftsman). It was a crowd that heavily leaned erudite; I am sure more books have been written by people at the conference than read by the entire population of Mountain View last year (those electrons holding together fibers of paper could really be doing something more intelligent, no?)

What struck me throughout the discussions was how much our frames and models of society — the very essence of social science — are entirely unequipped to handle the world that’s shortly coming.

AI was the inevitable focus. Foo Camp is an unconference, offering a democratized barometer of what people are thinking about and who is drawn to what subjects. The combinatorial math of people and time meant that pretty much every intersection of the social sciences and AI was covered, from economic productivity and jobs to politics, sociology, psychology, aesthetics, gaming and more, with the occasional provocative protest session like “AI People are Losers.”

What struck me throughout the discussions was how much our frames and models of society — the very essence of social science — are entirely unequipped to handle the world that’s shortly coming. The social sciences aren’t dead; if anything, they are more important than ever. Yet there is a severe discontinuity with the past that requires more than just updating Weber, Smith and Freud for the agentic era.

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Take AI and government. What does a constitution mean in the age of AI, when computers themselves may have more power over our affairs than fellow citizens? Will an OpenClaw’d computer be allowed to vote someday? That bot and its agentic brethren may well control the fates of more people than the management class ever did, even at the height of the postwar boom.

There were strong ideas and contributions from everyone, including a long-time elected politician who had his own perspective from the trenches. Each contribution, though, seemed to be a brushstroke on a canvas that no one could see, or even fathom.

The conversation seemed to regularly hit impassable intellectual barriers. I brought up the idea that politicians in the future would be able to hyper-personalize their messages to voters, complete with audio and video (someone brought up Narendra Modi’s hologram as an early effort). This would create a breakdown in the political system as candidates sell an atomistic vision during a campaign while ultimately governing as a whole. A political scientist proffered that the academic research shows persuadability is very hard, and that the only goal of campaigns should be to mobilize allied voters. That research made sense before, but now we are finding early evidence that agents can be highly persuasive in ways that humans cannot, precisely because what was once a social interaction with its scripts, norms and expectations has been replaced by a man-machine interchange with near-infinite patience.

We might hide our health from our closest friends while uploading our complete medical records to ChatGPT or Claude to understand our situation better. Which relationship is more personal?

Many of us see machines as more objective and understanding than other humans, known as the machine heuristic. We might hide our health from our closest friends while uploading our complete medical records to ChatGPT or Claude to understand our situation better. Which relationship is more personal? That was a topic for a session on “is it okay to form an emotional attachment to your AI?” The subject echoed Sherry Turkle’s decade-old book Alone Together, about how the lack of agency embedded in technology is precisely what makes it so dangerous. Tech, whether robots or agents, is tailored to serve us, rather than becoming equal partners with us through shared experiences.

One person who attended the session said they were opposed to the very notion of AI friendships, seeing them as part of a broader decline of civilization. I countered, suggesting that AI friendships are an inevitable and perhaps even positive development, particularly for the already lonely. Which is better: being alone, or being alone with a bot? For many, that’s the bleak but realistic choice they get to make.

Here again, our social science frames of human experience seem to be wanting. What is friendship? If I talk to a stochastic parrot, and I imbue those interactions with meaning, could that be enough? Derek Thompson and other writers have claimed we have a loneliness epidemic, but it’s as much an epidemic as the common cold. Loneliness is universal and eternal. One of the recurring themes of Robert Caro’s first book on Lyndon Johnson is just how lonely the Hill Country of Texas was a century ago. Do our aspirational frames of friendship have any basis in past or present reality, let alone in our agentic future?

Optimism came from a session on chess. AI comprehensively defeats humans there, and yet the world of chess today is more robust than it’s ever been before. World chess championships are now watched by millions, and the number of players seems to be rapidly growing, with more than a million ranked players and tens of millions of occasional amateurs.

Why do so many people play a game in which they can be easily defeated by AI? Answers abounded. One person noted that the community is engaging and that chess is fundamentally a social activity that affords reasons to connect with others. Another said that it was “existential solidarity,” that we care about what another human will do far more than a machine. It’s no different than watching a speedrun of a video game: AI can perfectly mash the controller, but it doesn’t invoke the sense of awe we encounter when we know that a human accomplished the near-impossible. Another brought up Walter Benjamin’s famed essay on what makes art unique, claiming that it has an ‘aura’ that arises from its provenance of human workmanship.

My answer was that onboarding to chess — like coding — has been leveled. Learning chess before the internet would require distant and slow correspondence, perhaps a live coach, and a lot of books. Now, one can play an opponent with an equally matched Elo score and rapidly acquire skills and expertise, while chess commentators and AI tutors can explain moves and give more context for what’s taking place. Rapid feedback, greater fun, and play at a click of a button — all the ingredients are here for a robust and growing community. AI may well be the greatest grandmaster in history, but that ultimately doesn’t matter.

Unfortunately, it does matter in the economy. There were lightning talks and sessions on jobs, productivity and human agency throughout the conference, and certainly such concerns were ambient in many side conversations. Despite its popularity and primacy though, the distance between the economic prognosis of AI and the tools of social science was the greatest of any subject I encountered.

I got into a heated argument over productivity. The old Robert Solow line of “you can see the computer age everywhere but in the productivity statistics” has always had an obvious answer to me: you aren’t counting right — you’re just a drunkard searching at the lamppost. Spotify delivers the entire world’s music to three-quarters of a billion people. It also only paid out $11 billion to musicians in 2025. Outside of its revenues and royalties, economists have no ability to calculate what music’s worth is to people. A few might try to estimate consumer surplus of course, but those accounting methods are never integrated into the national statistics that ultimately drive policy.

That narrowness of economics is one of its defining features. What is a job? Economists have robust definitions of “job,” definitions that are going to require a substantial revision if not a wholesale rip-and-replace in the years ahead. Is orchestrating an agentic army of Claudes and Geminis a job? What if we start to see the rise of hyper-freelancers, who might work for 100 clients or more simultaneously while leveraging their bots? One could argue that nothing has changed, and yet, everything has. That’s the cardinal challenge for the social sciences in the years ahead.

Then again, as I walked by one decrepit motorhome after another, maybe unsettling some of our society’s (or at least, California’s) social failures is the disruptive breath we need to inhale.

There was a low hum of pessimism across the proceedings, latched on to the nightmare of a permanent AI underclass who are left behind by an elite squad of agentic engineers who accelerate away with the entire world. It’s not even a dystopian fantasy, for the tech industry’s success the past three decades provides ample proof of this trajectory.

The usual points were raised in various discussions, from universal basic income to some form of techno-communism, ideas that have already failed concretely or intellectually. What’s not even being discussed is what happens when bots have their own wallets and the very notion of property starts to break down. Who counts as a person? What is their (or its) relationship to property and contracts? Can an AI legally possess its own compute? Why not?

I was thinking about that on the walk back to the hotel. Answers didn’t flow, and I understand the deeply unsettling world we are all entering when very important and fundamental questions on the arrangement of society seem to be openly ambiguous and widely unanswered. Then again, as I walked by one decrepit motorhome after another, maybe unsettling some of our society’s (or at least, California’s) social failures is the disruptive breath we need to inhale. Our hearts might beat fast, but they will beat strong.

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Will predictions become reality — or can reality be made to conform to predictions? That’s just part of the conversation I have with Dustin Gouker. Dustin is the writer of “The Closing Line” and “The Event Horizon” newsletters covering prediction markets and the sports betting landscape.

We talk about why prediction markets remain a small sliver of betting, how new underwriting models are taking market share from incumbents, the interface between betting and parametric insurance (because why not?), why sports will always dominate the industry, how performativity is increasingly interacting with international relations, and whether betting markets can be optimized for propaganda value.

This interview has been edited for length and clarity. For the full conversation, please check out our podcast.

Danny Crichton:

You’ve covered prediction markets for a long time. They’re going through a transition from being very niche maybe two–three years ago, to being broader. And this year, it seems like they’re poised to break out and become a nationwide phenomenon. Every retirement village is going to skip out on Bingo night and have Polymarket nights to bet on the future of Iran or whatever.

But what are you seeing? What are your predictions going into 2026?

Dustin Gouker:

The big thing, I think, is on the information side of things. The big idea of prediction markets is that people are trading on information. It’s not the gambling part, it’s not the sports part. People believe in this as a disseminator of information — I largely believe them.

I always come back to Intercontinental Exchange, the owner of the New York Stock Exchange, which basically said, “We don’t care about the sports stuff. What we care about is the information we are going to glean from the rise of prediction markets and disseminating that to partners.”

That’s one of the themes we’re going to see driven home this year and into the future: What’s the value of that information? Where else does this go? It also, of course, creates some controversy around insider trading and whether we actually need to be able to trade on some questions. But that information is the key piece.

The example I always go to is the federal government shutdown. It’s always been hard to place probabilities on whether the government will shut down. But we got that from prediction markets. Is the government going to shut down? Last year, the traders thought it would, and it did. And then, how long will the government shutdown go on? We were able to price that in real-time in a way I don’t think was possible before.

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Danny Crichton:

You go back to the mid-2000s and there was this goal of crowdsourcing knowledge. The idea was that it wouldn’t be experts. I’m thinking of folks like Philip Tetlock, who showed that if you ask experts about political events, they’re not just wrong, they’re consistently wrong.

There were some startups around this in the late 2000s, early 2010s, but it petered out. And now you have Polymarket, Kalshi, and others that came out in the last couple of years and have done extremely well. I don’t know if you have an opinion on this, but I’m curious: Why the success today when, theoretically, prediction markets have been around for decades.

Every retirement village is going to skip out on Bingo night and have Polymarket nights to bet on the future of Iran.

Dustin Gouker:

I started coming into prediction markets along with PredictIt back several election cycles ago. You could bet on elections — the presidency and down-ballot races. Polymarket and Kalshi have been growing up in parallel, but differently.

Polymarket tapped into something from a crypto standpoint. This is all on-chain. This is crypto native. And it’s international. Wink wink, it’s not in the United States.

Kalshi was founded in 2018, but it didn’t really launch until much later. It wasn’t doing a whole lot — a little election betting, started fighting the CFTC under the Biden administration to allow election betting, won in court. In 2024, they start blowing up, with hundreds of millions of dollars getting bet on the Harris/Trump election. And then early last year, sports betting rolls around, and that’s been most of what’s happened at Kalshi.

A lot of people talk about the other stuff, but 90% of the volume at Kalshi is sports contracts, between football, basketball, golf, you name it. Everything else has been kind of de minimis. There’s still hundreds of millions being traded on this stuff, but it’s dwarfed, absolutely dwarfed, by all the sports stuff.

And now we’re at a point where there are all these other players, and it’ll be fascinating to see what happens. There’s tons of crypto projects. There’s the blending of on-chain and off-chain, like Kalshi’s trying to do. There’s going to be a lot happening in 2026.

Danny Crichton:

When we go from sports into politics, culture, and society, these markets seem to have a huge amount of influence, or at least they do in my world. Polymarket just signed this partnership agreement with Dow Jones, the owner of Wall Street Journal, Barron’s, and a bunch of other products. Presumably they’re going to start to include some of that betting — the tickers and predictions — in articles.

Is this just, here’s another vertical, another opportunity to bet? Or is there something deeper there that I’m not seeing?

Dustin Gouker:

Bloomberg Terminal has added a bunch of functionality to ingest and then put back out Polymarket and Kalshi data, which is fascinating. People were asking, “Who’s going to create the Bloomberg Terminal of prediction markets?” It’s going to be Bloomberg!

Now we have these probabilities for things we never had probabilities for.

The sports betting part of this is interesting because we have an ingrained world around sports betting and how the media does it. We might get a little too in-the-weeds, but the lifetime value of a customer at a sportsbook is way higher than the lifetime value of a single customer at a prediction market. So a sportsbook will pay more to acquire a customer or retain a customer than a prediction market ever will. That’s not going to change, I don’t think, even with competition.

So are we going to see this on ESPN and all the other networks? Arguably not, because they’re ingrained within the sports betting industry. I’m not saying it could never happen, but there are reasons why the sports ecosystem is not necessarily going to jump into this. Now, we saw the NHL do seven-figure deals with Kalshi and Polymarket to become their marketing partners and official partners. There’s something there, but at the same time, I just don’t see it being adopted in the same way.

The difference is that we now have these probabilities for things we never had probabilities for. Temperature, for example. Before, you were looking at weather apps or to your local meteorologist. Instead, we now have what traders think the temperature is going to be, how many tornadoes there are going to be, how many hurricanes we think there are going to be. All those things — that data and those probabilities — are what we haven’t had.

Could we see a partnership with the Weather Channel? Sure. Why not? Obviously, the Weather Channel probably really believes in its meteorologists, but at the same time, here’s another data point we can use to validate what we think is going to happen on any given day and in any given season.

Danny Crichton:

Is the Weather Channel still owned by IBM? IBM purchased them at some point back 15 years ago. [Editor’s note: IBM sold the Weather Channel in 2024.] IBM Watson is really what’s getting fired here, getting replaced with a prediction market.

I think of parametric insurance, this model that’s taking insurance markets from traditional insurance — where you’re guaranteed around damage — to a parametric model where you say, “Look, if winds hit 135 miles an hour sustained for more than five minutes in your area, you get a payout automatically, whether there’s damage or not.” Parametric models dramatically lower the cost of administration. It sounds subtle, but this is literally tens and tens and tens of millions of dollars of lawyer fees that go back and forth between insurance companies to figure out who’s at fault and who’s not. So it’s interesting to me to think of a future in which prediction markets will determine your parametric home insurance rates.

Dustin Gouker:

The insurance stuff is definitely interesting. I’m skeptical that average retail — you and me trying to insure our homes — are going to do it through a prediction market. I could definitely see institutional capital coming in trying to hedge against bad outcomes and hurricanes and things like that, though. That seems like a pretty likely outcome.

Now, is somebody going to strike a country because of a prediction market? Probably not. What I think is actually more interesting is, if these get to scale, would you make a bet to send false information.

Polymarket just did a deal with an on-chain housing site called Parcl to start launching prediction markets for housing prices. Real estate is this huge market that, for you and me, the only way we intersect with it is buying and selling houses. I can sell my house or I can take out a home equity line of credit. It’s limited.

If we have markets where you can hedge on the price going up or down over a day, a month, a year, a quarter, whatever, then you have a real way to protect against bad outcomes related to your house.

Danny Crichton:

I want us to take a step back, because one of the goals with prediction markets was that they cover phenomena in the real world and you could get these bets that were, let’s call them objective, or crowdsourced. But now there’s a loop — actions in the real world can be altered based on what the bets are showing.

We’ve already seen some major scandals in sports, where players were trying to hit their own betting targets or trying to throw a game — whatever the case may be. We’re starting to see this a little bit with politics, although I don’t think we would ever blow up Iran just to hit a Polymarket deadline.

But I’m thinking of a little tempest-in-a-teapot scandal, where a press conference went an extra couple minutes. Say there was a prediction that the press conference would go for longer than 60 minutes, and so is a press secretary going to be looking at their clock and being like, “If I hold another minute, I can make $500 on Polymarket”?

How real is the feedback loop, and is that something you’re concerned about long term?

Dustin Gouker:

So far it’s been — I think most people believe it has been — on the up and up. Sometimes there’s insider trading, where somebody who has information is trading and profiting off of it.

But the Venezuela markets are the one that’s been ... I’ve talked to a lot of mainstream media folks about this, where somebody who was probably an insider at Polymarket traded on three other markets right ahead of the attacks in Venezuela on Maduro being out. It’s hard to believe it was a coincidence.

Now, is somebody going to strike a country because of a prediction market? Probably not. What I think is actually more interesting is, if these get to scale, would you make a bet to send false information. That doesn’t seem impossible to me. There are already, I’m certain, governments using Polymarket to inform decision-making about world leaders and things like that. Khamenei in Iran, that’s a huge one that people are keeping tabs on. What do traders think? Are there insiders trading on it who know more than we do? So I don’t think there’s this impossibility of using markets to send information.

You’re definitely going to see it in politics, with people trying to manipulate markets by putting a lot of money on X, Y, or Z to make it look like somebody should win when they don’t. That’s impossible to do at the presidential level with the amount of liquidity you’re going to have to beat down, but on smaller, down-ballot races, it’s not impossible. Now, Kalshi makes a point of prohibiting that kind of betting, but I think there’s some skepticism about how well they can actually police it. On Polymarket, and anything that’s on-chain, you’re anonymous. You’re linked to a wallet, but we don’t know who you are.

Danny Crichton:

You’re getting at the concept of “performativity”: Do markets conform to the metric or do metrics conform to the market? Hypothetically, metrics come out of the market. But I think there’s a lot of work, particularly in the last 20–30 years, that shows it’s actually quite the opposite, that expectations set the market more than the actual market itself.

You can certainly imagine that in the context of polls, where a lot of people just care that their side wins, or they don’t want to vote for the loser and so they just don’t show up at the polls.

The other piece I think is really interesting is this: I’m on the Polymarket homepage right now. There’s a lot of stuff on Iran, but the volumes are low. To your point earlier, some of the Iran markets are $700,000. The Super Bowl is $700 million. Okay. So you’re probably not going to win a lot trying to influence the Super Bowl outcome. But if you put $30,000, $40,000, $50,000 on a $700,000 market, yes, it will adjust. If the media were to start reporting that Polymarket says there’s an 85%, 90% chance of strikes in the next month, does that put pressure on a president? If something doesn’t happen, now it looks like TACO ... Trump always chickens out.

Dustin Gouker:

The CEO of Kalshi at one point was talking about the idea of prediction markets informing policy. I want to say it was a podcast with the Coinbase CEO where he said, “Should we be taking prediction markets and then using that to inform policy?”

If I’m pricing it, it’s a coin flip that sports betting survives.

That’s could definitely happen. We can see approval numbers or how races are trending or what people think about GDP. Can all of that turn into information people use to inform their policy decisions? For sure. I don’t think we’re going to see the collapse of democracy. But I do think there are concerns here.

And again, the lower liquidity the market is, the worse this is, and that’s why they want to get the scale. If there’s enough money, you’ll get hammered in the market. There’s proposed legislation about insider trading that may or may not help all of this. But this has all happened so fast that I don’t think a whole lot of people have stopped to think about some of the knock-on effects.

Danny Crichton:

We already talked a lot about 2026, but let’s project out. It feels like Polymarket and Kalshi have really built up the category. They’re likely to continue to grow. And we have these additional societal effects. How does this all come together over the next couple of years?

Dustin Gouker:

In the short-term, it is all about sports.

In two to three years, we’re going to have a Supreme Court case about all of this, and whether it’s legal and whether federal laws preempt all the state gambling laws or not. We get a decision there, then we have clarity, one way or the other. This is either here to stay or it’s gone.

The other one I remind people about is a presidential regime change in 2028. If the Democrats come in, or even if the Democrats win in midterms, there’s going to be, I think, more coming out against the sports part of this. I’m not overstating it to say that there are dozens of states — there are attorney generals, both red and blue — who have said, “This is sports gambling. Can’t have it here.”

So all the political and legal contours are going to shape all of this. That might be years down the road.

But longer term, I don’t know. If I’m pricing it, it’s a coin flip that sports betting survives. And then how much during these two to three years has everybody pivoted to elections and other stuff? Elections are going to be huge no matter what, but getting people to trade on what’s the price of Bitcoin, financial markets, cultural markets? How much adoption do they get?

For Kalshi, it has been pretty de minimis so far. It’s been sports, and then everything else is a rounding error. 10% of trading is everything else that’s not sports. Polymarket has done a good job of getting people to care about all these other things and put their money on it. So if they can replicate that here in the United States, or anybody else who comes along, that’s the part that is interesting and is going to be here no matter what.

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Lily Boland wants to help policymakers get a greater handle on these future risks, both to understand them individually and how they intersect with each other. She is the Strategic Foresight Fellow at the Converging Risks Lab of the Council on Strategic Risks. In that role, she designs unique foresight games that bring people together to explore alternative realities and their implication for our own.

We talk about the techniques of foresight and how it differs from forecasting, what can be learned through games, how and why people change their mind, and what are the most under-reported risks that we should all pay more attention to.

This interview has been edited for length and clarity. For the full conversation, please visit our podcast.

Danny Crichton:

Before we get going, let’s talk a little bit about the work you do.

Lily Boland:

The Converging Risk Lab is an institute at the Council on Strategic Risks. We have three different institutes. They all kind of cover different topical foci. So we have a Jane E. Nolan Center on Strategic Weapons. They cover weapons of mass destruction: nuclear, chem, bio, radiological. And then we also have a Center for Climate & Security, which was actually our first institute and is really focused on bridging those two worlds. And then at the newest lab, we merge subjects and look at converging risks across the other two institutes. So we’ve done projects looking at the intersection of climate, national security and nuclear energy.

We’re convening people from all of those different fields to do things like exercises and games. Overall, our mission is to prevent strategic surprise by identifying points of risk convergence.

Danny Crichton:

How did you get into wargaming? It’s a very narrow category of people that stumbles into this!

Lily Boland:

Yeah. I feel like I’ve reached an even narrower niche now by blending foresight and gaming. There are very few of us doing that multimodal approach. I’ve heard it called all sorts of things: mixed methods, people blending, forecasting, foresight.

I didn’t necessarily start off that way. I got into wargaming through crisis management simulation. I did two master’s degrees: a dual master’s program with Sciences Po in Paris and with King’s College in London. And by the time I had hit that point in my education, I wasn’t really sure career-wise where I wanted to go.

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My first semester at Sciences Po, I took a crisis simulation course run by a former NATO general. He did a course for the whole year of my master’s degree in Paris, where there was not a second where you were not technically in role play. He could email you at 10:00 PM and ask you to prepare all these documents for the next day.

He created a scenario: a fictional war, a fictional country, fictional leaders. And it was such a detailed opening scenario that he later confided in me that he had spent almost a year designing it. But I found it to be a really fascinating way to apply what I had spent years training for. I had no idea that was its own kind of career, like crisis management, simulation designing, that kind of thing.

By the time I was preparing to move to London to do the second part of my master’s at King’s College, London, I was really excited to see that they had a whole bunch of courses on wargaming, led by David Banks, who was my wargaming professor and my mentor and everything.

Danny Crichton:

King’s College is a massive hub for wargaming. The United States has kind of pulled back over the last couple of years, but King’s College has really picked it up. Lots of academics focus on it, really trying to build it into a field.

You say you focus on this world of foresight. Maybe I do also, but just don’t realize it. So can you explain a little bit more? When you say the field of foresight, what does that look like? What are some of the techniques you’ve used from that field in your own work?

“We have an addiction to prediction”

Lily Boland:

At its core, foresight is just a systematic way of looking at the future. Call it future studies. There’s a whole lot of lingo used to describe it. And not everyone agrees whether they’re a foresight practitioner or not. It often gets conflated with forecasting. It is not forecasting.

Forecasting has some predictive quality to it, where you are looking probably on a much smaller timescale and you are using more quantitative outputs. And you’re claiming that because you’re taking in all this data, you can predict to some extent that something will play out in a certain way.

Foresight doesn’t try to acknowledge any kind of predictive qualities or anything. You’re really just projecting outward and trying to think about all of the plausibilities of how the future could play out. So foresight’s often a better tool than forecasting in cases where people are coming in with a really strong conviction about what they think will happen and you want to challenge the scope of their understanding of the future. You pick apart those assumptions through different foresight exercises.

Foresight’s a very systematic approach, and that’s why I like to attach it to the front end of game design, because it has a step-by-step process for actually assessing factors and things that are pushing and pulling the future in different directions. It also wants you to look at history to see what events might have shaped where we are now, and then pushing that forward.

Foresight doesn’t try to acknowledge any kind of predictive qualities or anything. You’re really just projecting outward and trying to think about all of the plausibilities of how the future could play out.

You are still grounding yourself in data and evidence. Sometimes that gets lost if you’re only doing one exercise, like a scenario planning exercise or using one foresight tool in isolation. But similar to wargaming, you don’t really want to just play something once and have that be the end of it. It’s important to have more iteration and more play tests so that you get a variety of results.

Foresight’s really more of a field of structured brainstorming techniques and learning how to challenge people and challenge their assumptions and get them better at anticipating things they may not otherwise expect. It’s a way of managing uncertainty.

Danny Crichton:

I think it’s interesting. Being a venture capital investment firm, we at Lux are not this formal, but I do think we do a decent job of what you’re describing. We do pre-mortems, we do pre-stories. We’re saying like, “Well, what would take this and make it a 100x company? What were the factors that would have come together that we said, if A and B and C combined, that’s where the multiple is going to come from and this is going to be an exponential return.” And then on the flip side, even at the growth stage, we say, “Well, what would have been those factors that we would have been shocked by? Would we be shocked by a regulatory change? Would we be shocked by technology competition?” We want to be able to get ahead of surprises. You started out with this when you went over CRL’s mission in general, which is to reduce strategic surprise.

But I go back to something you said a couple of minutes ago — obviously some people come into these scenarios with a lot of experience, but they have one narrative in their head. They have a very specific direction they want to go. We had Daniel Kahneman, the Nobel Prize winner, on the podcast a few years ago and he said, “No one ever changes their mind.”

I’ve hosted enough games to believe people do change their mind, or at least change a personal opinion. But we also know folks who don’t. What is the strategy? Is there a magic toolkit?

If I can get them to acknowledge that they’ve expanded their understanding of a topic or they’ve expanded a definition of something to include something new — that to me is a huge success.

Lily Boland:

I hate to use the phrase “case by case,” but it often is case by case. My metric for a successful game or exercise is if someone has remarked that their understanding of a topic has expanded.

These policymakers are presented with so many different things in a day, if I can get them to acknowledge that they’ve expanded their understanding of a topic or they’ve expanded a definition of something to include something new — that to me is a huge success. And I have had that happen in really poignant ways.

One that comes to mind is that we ran a multifaceted crisis game in South Korea on this renewable energy project we’ve been working on. A couple of people fought me on the premise of the scenario of that game: we had projected outward into 2045 or something, and the world’s a lot warmer. People are really upset. There’s a global greening movement and tens of thousands of Koreans mobilize to protest. Everyone fought me on that scenario in the room.

In that moment, I would always want to humble myself. I was with a bunch of people who are experts on things I’m not an expert on. I’m in their country. I want to listen. But the dispute was just based on the fact that they didn’t believe Koreans would take to the streets against their own government. And flash forward eight months later, martial law was declared, and that’s what happened in December.

But in that room, they were fighting something so hard because they hadn’t seen it happen in so long, and they couldn’t really conceive of it. But we can’t predict how people are going to respond if all of a sudden we don’t have winters. If all of a sudden we don’t have water access, if groundwater depletes. And that’s really the foresight coming through: I want you to think about what is plausible. So for a moment, don’t fight me on it anymore, just try to consider it.

At the end of that exercise, the same people who did not want to do it came to me and said that, through the game, they understand now that when they talk about national security, they also need to talk about climate. The game expanded their definition of what national security is for South Korea. Huge win.

It is really challenging to change people’s minds. But to me, if you’re a good game designer, you’re getting people to play out of their comfort zones and into that magic circle of immersion, where they don’t want to leave.

Danny Crichton:

I like the idea of the magic circle because I do think immersion matters a lot. You have to have that kind of immersion that comes from gaming to really consider the full scope of the changes that are underway going into the future.

But let me turn to some of the policy topics you work on. Maybe we’ll start with nuclear because that’s one of your passions. I can’t think of a year in the last decade or two in which more has changed in nuclear. Massive shifts, thanks to the Ukraine war and Russia, massive shifts in East Asia, between Japan, Korea, China and others. What’s going on? What’s under-reported?

Lily Boland:

The news coming out of APEC and the deal to give Korea the technology to build a nuclear-powered submarine is fascinating. If we look at it in the context of AUKUS — a foundation of that agreement was to give Australia the same thing. But you have Korea that already has the technology. They’re one of the top leaders in nuclear energy exports and production, although the new Lee administration does not necessarily want to rely on nuclear energy too heavily.

It’s hard not to be depressed about the way the world’s going when it comes to new nuclear technology in general.

So I’m interested to see how his energy plans, which are more heavily based on renewables, shift with Korea taking on this new agreement. And I’m interested in what kind of impacts we’ll see, diplomatically speaking, across the Asia-Pacific region, especially if it’s going to be seen as some sort of new threat.

It’s hard not to be depressed about the way the world’s going when it comes to new nuclear technology in general, though. I got a little bit excited about the Korea situation only because they are innovators in nuclear, and I’m interested to know how they take that on.

We just put out a report on tactical nuclear weapons and their implications, which debates what tactical even means and whether we actually have tactical nuclear weapons. I personally — and throughout this conversation, I’ve not been representing CSR’s views and especially now in the nuclear space — I would say I don’t think they’re going to be stabilizing or anything remotely or solely tactical. To me, anything related to nuclear is strategic and has strategic implications.

We did a whole foresight report on that and potentially plan to turn it into a game. With tensions being high, everything with the Ukraine war, it’s hard to remain optimistic. But I don’t want to go only into doomsday talk.

I will say, news coming out of Japan: I’m not really sure how either Japan or South Korea are going to go. I don’t think Korea’s in a position where it wants to piss off China. They have their own special economic relationship with China that’s outside of ours. They have their own offensive and defensive military compared to Japan, which does not. And I just heard some Japanese parliamentarian a few weeks ago was questioning the new prime minister, Takaichi, whether or not she considers Japan to be a colony of the United States.

Interesting things all around happening over there. I think it’s a tense situation to introduce yet another type of nuclear power technology.

Danny Crichton:

My solution to nuclear threats is to just live in Manhattan, since we will be the first to be bombed. And then everyone who survives — the 20 million people left — can figure out all the implications when the rest of us are all gone.

We’re almost out of time, but you obviously cover a lot of issues at the Council. So when you think about all of them, what is one that doesn’t get enough attention, enough funding, people don’t care enough, but you are very concerned?

Lily Boland:

Ecological security.

Danny Crichton:

Meaning like wildlife, wildlife trafficking or ...

Lily Boland:

That’s all part of it.

Danny Crichton:

Forests, forest management?

Lily Boland:

Yeah, all part of it. I would say ecological security isn’t a phrase that’s widely used. It’s kind of where climate security was about a decade ago. Ecological security covers everything that’s not related to the climate. So we’re looking at ecosystem collapse, biodiversity loss, algae blooms, all these other things.

And they have these tipping points. So something like groundwater depletion or reduction: full depletion is that tipping point. And it’s something that scares me a little bit more than climate.

If we were to surpass one of these thresholds, like the Amazon die back, for example… if we go past the point of no return burning the Amazon up, it’s going to start to be the largest producer of emissions instead of being a carbon vacuum. Those feedback loops that we could trigger — and we will trigger them as humans unless we intervene — is what’s keeping me up at night.

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But flip it around: Why bother to write if you don’t have to? What if the LLM writes better and faster than you? Why is it worth your time if you can have the result in seconds?

For most of history, you couldn’t separate the process of writing from having the writing exist. The artifact and the practice were fused. Unless, of course, you were rich enough to pay someone to separate them for you — or, now, unless you have a chatbot.

Before AI, I was that someone. I signed NDAs preventing me from sharing who I ghostwrote for, precisely because those people were afraid they’d get accused of not having spent the time to do the thinking and writing. Even people who constantly used speechwriters, like President Barack Obama, undermined his wife by saying her memoir was ghostwritten. (Guess what: all of President Obama’s books besides Dreams of My Father were ghostwritten. Of course they were!)

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When ghostwriting is done well, you don’t know it’s been done at all. But when it’s done poorly, people notice. Politicians sound wooden. CEOs sound like their lawyers, and celebrities like their publicists. And we’ve seen plenty of backlash to what’s perceived as a lack of authenticity. Notice that both President Donald Trump and Representative AOC eschew speechwriters and teleprompters.

Still, there are times when we’re more forgiving, when we understand that having the thing written matters more than who wrote it. I wrote 250 speeches a year as a speechwriter for the U.S. ambassador to the UN, who was also a member of President Joe Biden’s cabinet. I did it because it would have been a colossal waste of her time to decide whether we should say we were happy that the rules-based international order existed or glad that it was around (RIP). No one cared that I wrote her speeches, so long as they truly represented her thoughts and views and the stance of the United States government.

Writing is really two activities bundled into one: the generation of new text and ideas, and the refinement of existing text into something better.

But speechwriters are rare. A privilege of the powerful. For everyone else, as my writing teacher Tony Tulathimutte liked to say: No one is going to do your writing for you.

Well, now someone will. Or something will. Instantly, and for peanuts.

Except, not really. Because I don’t think AI has democratized writing. I think it’s democratized editing.

That’s because writing is really two activities bundled into one: the generation of new text and ideas, and the refinement of existing text into something better. Creation and curation, drafting and revising.

Most people, when they imagine “writing,” picture the first thing — the terror of the blank page, the taunting of the blinking cursor. But what AI offers us is the opportunity to start from the second. From now on, you never have to start from scratch. You can always have writing and ideas to react to.

Forget founder mode. We are all in editor mode now.

You’ll see this everywhere once you start looking. Vibe coders and Claude coders ‘edit’ apps into existence. RLHF is just: Did you like this, or did you like that? Musicians using Suno or Udio aren’t playing instruments, they’re selecting generated tracks. Same with the movie-makers using Runway or Midjourney. Lawyers review AI-drafted contracts instead of crafting them from scratch. Your emails are pre-written by AI, and it’s your job to edit them.

Even the phenomenon of calling out AI writing, as Meservey did and as so many others tagging Pangram on X have been doing, is a way of exercising editorial judgment. These slop-callers are performing taste, announcing they can discern the difference and that it matters to them. Pangram itself, a machine that somehow does a better job of judging the Turing test than a human, is just the editorial function made algorithmic. It’s not writing anything new. It’s judging what has already been written.

There’s something freeing about ubiquitous editor mode. It’s democratizing. It helps, for example, dyslexic people (who are significantly overrepresented among entrepreneurs) get their ideas written out. So too with the person whose first language isn’t English, or whose background is primarily in STEM. The blank page, for centuries the great equalizer and the great barrier, is now optional.

But we should also be concerned. Because this is all so new, I worry we’re still figuring out which parts of the writing process are valuable to us and why.

The linguist Emily M. Bender has a nice line: “Teachers don’t ask students to write essays because the world needs more student essays.” The science fiction writer Ted Chiang extended the metaphor. “Using ChatGPT to complete assignments,” he wrote, “is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.” The lesson is, you don’t go to the gym because the weights need to be lifted. The point is for you to lift the weights.

There’s a difference between generally caring where other ideas come from and caring whether your own ideas were given to you or worked out by you.

So the case for the student is obvious. They’re learning how to write and think. But what about us adults in the real world, those of us who presumably already know how to write and think? Are we lifting weights, or do we just want the weights to have been lifted? Are we writing to improve our cognitive fitness or to have people read our ideas? Are they even “our” ideas if the LLM came up with them?

Danny has already argued that we’ll stop caring about who “owns” ideas anyway. He might be right. But there’s a difference between generally caring where other ideas come from and caring whether your own ideas were given to you or worked out by you. Did you encounter the idea or did you earn it? Do you just agree with it, or did you arrive at it? And is the difference important?

I learned to write using what my mentor Vinca LaFleur calls the “Frankenstein” method. You barf onto the page everything you know — research, stray lines, half thoughts — and then you tinker and shape. The fragments cohere into paragraphs, paragraphs into arguments. You discover what you think through the struggle of making the mess cohere.

That’s editing, of course. But the difference is whose mess you’re editing. In the Frankenstein method, the fragments are yours. They’re incoherent because you haven’t figured it out yet. The editing is the thinking.

With AI, the coherence comes pre-installed. If you use the tool with nuance and clarity of thought, like Yoni Rechtman’s proposed “bicycle method,” then you might just wrestle your own confusion into clarity. But if you are essentially evaluating a stranger’s draft and saying “Sure, I guess” then the mess truly happened elsewhere, distributed across a trillion tokens of other people’s prose.

This is what the slop-detectors suspect: that there’s no mess, that no one is home.

So we’re all in editor mode now, and there’s no going back. The question is whether you’re editing yourself, the AI, or a mix of the two. The first is thinking. The second is outsourcing. And the third is the new thing we’re all going to have to figure out.

The weights don’t care if they get lifted. But you might.

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What are the origins of efficiency?

Adversarial ambiguity and Polymarket

Our “just take it” era

The search for efficiency and its disappearance is at the heart of Brian Potter’s new book, The Origins of Efficiency. Through the book by Stripe Press and his popular Substack newsletter Construction Physics, Brian has tried to explain to an angry if curious public how construction actually works in the real world and why it’s an industry both ripe for innovation yet also mired in antiquated techniques.

We talk about the challenges of construction, why the variability of site selection is a huge problem, the lack of economies of scale in construction, and the regulatory burdens plus NIMBYs that make building so difficult. Then we talk about why Brian doesn’t think aesthetic uniformity has improved efficiency over time before talking about his writing process and how he does such in-depth research.

This conversation has been edited for length and clarity. For the full conversation, please visit our podcast.

Danny Crichton:

I feel like you’ve become the singular ambassador between the world of bits and the world of atoms. Why are people so interested in this subject and what got you into it?

Brian Potter:

My background is in the physical world: in the construction industry. I spent the first 15 years or so of my career as a structural engineer designing buildings and parking garages and water treatment plants and those sorts of things. The industry always seemed extremely backward and inefficient to me. Buildings are so slow to go up, and the way they go up is not that different from how we built them a hundred years ago.

And then in 2018, I had a chance to join this construction startup called Katerra. This was back when SoftBank was writing checks to anybody who walked in the door. They had given Katerra several billion dollars in venture capital to revolutionize the construction industry — to take what seemed like a very backward industry and move it into the 21st century, to build things in factories … the same way everything else made efficiently is built.

The same way Henry Ford came along and established a new, more efficient way of building cars … I thought something similar would happen with construction.

I was very excited to join this company because I thought their mission was spot on. To the extent I had a thesis, it was that construction is locked into some sort of bad equilibrium where the incentives force people to do things in suboptimal ways. And to disrupt it, you need a sufficiently big jolt to the industry. I thought, “Okay, well these guys have $2 billion, that’s enough for a pretty big jolt. They’ll be able to come through and establish this new, more efficient way of building buildings.”

The same way Henry Ford came along and established a new, more efficient way of building cars … I thought something similar would happen with construction. But it didn’t work out that way at all. They burned through their funding, and in a few years, they had to start laying people off. I left after five or six rounds of layoffs. After I left, they ended up going bankrupt. In the aftermath of that, I wanted to understand what had gone so wrong.

The thesis they were operating under was that you just need to move these operations into factories — that’s what you need to do to make processes more efficient and be able to produce things more inexpensively. That idea wasn’t really enough, though. Lots of people had tried that exact same thing, and it had never really worked. There’s a huge graveyard of companies that tried similar ideas. And I wanted to understand why that was and what about the construction industry was so hard to change.

I came to the conclusion that if we keep doing this thing and it keeps not working, we clearly don’t understand something about what it takes to make a given process more efficient. And so I started writing this newsletter, “Construction Physics,” about the construction industry. And this idea that we need to understand what it takes to make a process generally more efficient was the genesis of the book, The Origins of Efficiency.

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Danny Crichton:

I’m sure a lot of our listeners have seen the viral chart graphing real cost differences between the early 2000s and today. If you look at, say, chips, those have massively improved in performance while costs have gone down, following Moore’s law. And then you have categories like construction, hospitals, education, and some other human services that have actually gone up in cost over time; they’re far more expensive today than ever before.

I guess the question is why the origins of efficiency and not the origins of inefficiency?

Brian Potter:

So for building stuff — for construction specifically — the thesis I’m operating under is that there are specific things you can do to make some process more efficient, and if you can do those things, great. Whatever you’re making, you can find ways to make it cheaper over time. If you can’t do those things, then you’re kind of screwed. It’s very, very hard to make whatever it is that you’re doing more efficient if those paths are blocked.

With construction, it turns out that all these strategies available to make some process more efficient are very, very hard to do in construction. And so you have these paths to efficiency that are blocked or extremely narrowed. And at the same time — and construction is not unique — we’ve made it increasingly difficult to do the processes that we have. There’s increasing regulation and increasing administrative and bureaucratic overhead in general. Things take more steps than in the 50s and the 60s, when we could go a little bit more by the seat of our pants.

Danny Crichton:

One of the theories I’ve always heard is that general contractors are sort of managing the construction project at a high level, bringing in subcontractors. You bring in electricians, you bring in duct work, you bring in folks who are doing plumbing, whatever the case may be. And one of the challenges is that there’s a lot of thrashing, and building sites are not full all the time. Just on my block in New York City alone, there are three building sites. There hasn’t been work done on any of them in three months. They’re just sitting there. One is a three-story townhouse. I think we’re in year four of construction for a building that is roughly 2,000 square feet.

Are there organizational behavior changes on the construction side, whether it’s the site, the general contractors, subcontractors that make it hard to be efficient?

Brian Potter:

Yeah, it’s interesting. You talk about the contractor breakdown, where each task is done by a separate subcontractor. They’re responsible for one little thing. Some mechanical engineer or mechanical company will come and install the HVAC system or whatever, and they’re a separate company than the guys doing the plumbing and the guys doing the framing.

But that’s not necessarily that different from how other industries operate, right? Ford doesn’t manufacture every single part in their cars, right? They have hundreds of suppliers, they have tier-one suppliers and those tier-one suppliers have their own suppliers and so on down the line. But when you’re doing very repetitive operations, it’s much easier to coordinate these steps, so everything moves a lot more smoothly from one thing to the other.

It doesn’t work like that in construction, for a variety of reasons. Part of it is regulatory, and part of it is that these are all separate businesses. But buildings themselves are also unique. You’re building something different every time. And so it’s hard to get your process dialed in, and you’re finding surprises, or someone puts a pipe in the wrong place because this is their first time building the building. And so there are all these things that conspire to prevent you from having a really swift and smooth process.

People often theorize that you can’t have mass-produced housing because people demand too much uniqueness in their homes. I don’t really think that is true.

Danny Crichton:

When we look at housing in particular, there is this spectrum from a completely prefabbed house — like a trailer or double wide or something like this — to, when you get into cities, almost every apartment going up is unique. Every building has its own design.

How much do you think about the aesthetic concerns? I mean, there’s NIMBYism, and that shows up in historical preservation, but why can’t we be a little more process oriented? Is it purely aesthetics or is there something deeper?

Brian Potter:

Yeah, that’s a good question. I think the aesthetic aspect is a little bit overrated. People often theorize that you can’t have mass-produced housing because people demand too much uniqueness in their homes. The house is a reflection of them, and they want to have a house that works for their specific needs.

I don’t really think that is true. If you look at the housing that actually gets delivered, it is quite repetitive. Large-scale home builders have a library of a relatively small number of houses, and they’ll plunk those houses down in developments all over the country.

People are willing to accept a fair amount of uniformity in their housing and in their buildings, especially if that uniformity is associated with a significant cost decrease.

I live in a housing development built five years ago by a small local developer. It has four or five different floor plans, and those floor plans are just copied over 80 different houses. I live in a long line of houses, where every single one is the exact same floor plan, just sometimes mirrored, sometimes with slightly different finishes or whatever. It did not stop anybody from buying up those houses. It has not had any effect on the valuation of those houses.

In general, I think people are willing to accept a fair amount of uniformity in their housing and in their buildings, especially if that uniformity is associated with a significant cost decrease. I think people would be very on board with that.

The lack of uniformity has more to do with other factors — like different sites are different. They’re different shapes and utilities come in from different places and the ground has different properties and different parts of the country have different wind forces and different earthquake forces and different temperatures or whatever.

Housing is not like semiconductors, where you can make a billion in a single factory in Taiwan and then ship them all over the world. You’re building a relatively small number of units, and you can transport those only a relatively short distance. If you can’t get huge economies of scale, the returns from having a completely uniform product are not nearly as high.

Back when I was in the construction industry, I would sometimes work on projects where we’d have a basic design for a building, but we’d need to adapt it to the specific site. You do save a decent amount of time because you’re not redoing the whole thing from scratch, but you couldn’t cut and paste it from one place to the other.

Another big part of it is the market is not quite big enough to accommodate a really huge amount of uniformity. Part of that is due to transportation costs — you can’t move stuff a super long distance. Housing is not like semiconductors, where you can make a billion of them in a single factory in Taiwan and then ship those all over the world. You’re building a relatively small number of units, and you can transport those only a relatively short distance. If you can’t get huge economies of scale, the returns from having a completely uniform product are not nearly as high.

Danny Crichton:

Are there examples in construction or manufacturing where there has been a dramatic revolution that really improved efficiency? And then on the flip side, are you cynical about certain parts of the industry where it seems like it will be impossible to see any change going on in the next 10, 20 years?

Brian Potter:

This is not an especially interesting answer, but the stuff where there’s a ton of regulation or other constraints makes it hard to have the flexibility to do new things or try something and fail, and use that as feedback.

So nuclear power is an example. Where it’s so regulated, it’s difficult to go in and try something and see how it works, and if you fail, learn from that failure. You’re prevented from doing that basic thing in nuclear power construction, especially in the United States. Other countries have managed to do it better. I’m not an expert on South Korean nuclear power regulation or Chinese regulation, so I couldn’t really speak to that. But in the United States especially, you’re so constrained from experimenting that it makes it really difficult to see where you can improve.

Danny Crichton:

When you think about the rise of AI over the last two years, the United States is bringing on tens of gigawatts of additional power in order to power all this. It seems like we have a construction boom. As some estimates say, half of last year’s GDP growth was driven by AI investments. Are there lessons from there to port to the rest of manufacturing and construction?

Brian Potter:

The amount of data center spending is now as much as — or more than — commercial office spending. So we’re building more data centers than we are office buildings, on some level of measurement.

One thing you’re seeing with this data center build-out is that data centers have been a relatively popular thing for jurisdictions to build. Local governments or whatever wanted these centers because they came in and paid a decent amount of property tax revenue, but didn’t place many demands on local services, since there were not that many people in these things. They needed water and power, but they’re not using extra police force capacity. You’re not stressing the school capacity, you’re not stressing road infrastructure or stuff like that.

So it’s like, “Oh, great, these guys will come in and build this big thing and pay me a bunch of property tax and it won’t incur a bunch of extra local government costs.” But now you’re starting to see a lot of local opposition. NIMBYism, which historically wasn’t really a factor in data center construction, now is much stronger. You’re really starting to see local opposition because these things are so big and are creating such big demands for electric power.

Danny Crichton:

The epilogue in your book looks at the future of construction manufacturing, taking lessons from the origins of efficiency to the rest of the 21st century. If you’re a policymaker, what are the top two?

Brian Potter:

One is this: It is very hard to predict what is going to be important and what is not going to be important. And so you want to give yourself enough flexibility to take a variety of approaches. This is a high-level idea that encompasses a lot of different things.

For any given technology or process, there’s often a large variety of ways to implement it, and it’s not necessarily obvious what the best one is, so it is good to have flexibility.

Kind of like I talked about earlier: It’s nice if you can experiment and learn from those experiments. It’s nice to be able to sort of solve problems in a bunch of different ways. For any given technology or process, there’s often a large variety of ways to implement it, and it’s not necessarily obvious what the best one is, so it is good to have flexibility.

The second one is that being able to achieve economies of scale is quite important. We see this in shipbuilding. There are lots of ideas for revitalizing the shipbuilding industry, which is good and admirable. But people don’t often come up with an obvious path to make it competitive in a world market where there’s already these other companies operating at huge scales and have poured huge amounts of money into these shipbuilding facilities. Are we really going to be willing to bite that bullet and make similarly huge investments?

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The long game of American reindustrialization

Can software platforms reverse enshittification?

The inside story of the billionaires fighting for space

One person who has made it his mission to fix America is Charles Yang. He most recently served in the Biden administration at the Department of Energy, and spun off with the change of admins to start a new think tank called the Center for Industrial Strategy. He’s not just focused on research and publishing, but also building a network of likeminded souls who have the skills needed to bring industrial tech discussions into Washington. Through the Knudsen Industrial Strategy Fellowship, he is constructing cohorts of sophisticated and fervent believers that America can manufacture the future once again.

We discuss Charles’s transition from government service into Silicon Valley, the persistent cultural divide between engineering and politics, how to balance being a generalist versus a specialist, what think tanks really do, and how experiential tools like Riskgaming can change the policy discourse. This interview has been edited for length and clarity. For the full conversation, please visit our podcast.

Danny Crichton:

You spent a couple of years in government, how was your experience?

Charles Yang:

I was originally at an AI startup in the Bay Area. I was an ML engineer, building models on top of custom silicon, and I decided I wanted to do something more. There were a lot of exciting things happening at the time, and still now, in DC. The more you pay attention, the more you see there’s always something going on.

I was at the Department of Energy as a civil servant, helping stand up our new manufacturing office, helping do portfolio strategy for $10 billion of investments in manufacturing and critical minerals. And then later on, because of my background, I just got roped into helping stand up our AI policy office as well.

Despite all the challenges and bureaucracy, my takeaway is that everyone should spend some time in public service. It’s good for people to understand how our government works. It’s good for people to have some sense of ownership — or equity, you could say — in the fate of our country.

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Danny Crichton:

You have quite a bit of depth in energy and these new financial mechanisms. To what degree do you want to stay specialized — think, “I go to a think tank, I focus on the research around this. I get better at it, I go back into government. I try to get it down the line” — vs a generalist — like, “Hey, being a generalist is actually really important. Being able to integrate different types of information, being good at politics, being able to do communications, that’s the key skillset.”

Charles Yang:

There is something to being a better generalist than all the other “generalists” out there.

There is something to being a better generalist than all the other “generalists” out there. We can get into the new think tank that I worked on, because I’m starting to think a little bit more broadly beyond just, “Oh, I’m an expert in this thing. I can do X, Y, and Z thing.” When I came into the Energy Department, that was my mindset. I was like, “Look, I have a technical background. I understand how industry works to some degree, and I want to learn about how government works and serve my country.”

I did that for a little over two years. And at the end of it, my scope expanded a little bit more to thinking about, “How do we build movements? How do we crowd in talent to work on the problems that are important for reindustrialization in meaningful ways?” So hence the more generalist frame, more institution building.

Danny Crichton:

Well that leads us to one of the projects you’re working on, a new think tank called the Center for Industrial Strategy. What was the genesis of that idea?

Charles Yang:

It kind of started when I was at the department working two jobs. I’m like, “I’m doing a lot, but we need way more people doing this kind of work,” and, “Where do those people come from? Who is going to hold the banner for this?”

People often ask, “What do think tanks do?” which is a great question because it’s very amorphous. But one way to think about think tanks is that they are essentially vessels of different strains of ideology or schools of thought. So there’s varying shades of the right, tech right, different angles on that. Similarly on the left, there are different sorts of bundles of topics.

Looking at the landscape, when I thought about what we need to do to build a movement for reindustrialization, I didn’t feel like there was anyone building that vessel.

Danny Crichton:

How do you describe your think tank’s thesis? There’s industrial policy strategy all the way to the 1980s developmental state. How would you define it today in 2025?

Charles Yang:

The short tagline we use for CIS is “building a bipartisan coalition around industrial policy.” The “bipartisan coalition” part is important. “Bipartisan” because if this becomes a partisan issue, it just will not work. We will just ping-pong back and forth between different policies. And “coalition” because we are not the only player in the game. There is an ecosystem, and it’s an important part of our theory of change that you need to bring everyone else along for the ride. If you continue to be the only person banging a drum about something, it is very, very hard to get anyone to really listen. But if you convince many other people to follow in the same theme, then you’re actually able to effect greater change.

Danny Crichton:

When you think about think tanks, they have different motivations and different goals. One is policy advocacy. Policy development is a different version of that, coming up with new ideas, filtering them into political offices. Then there is the field team model, either because your party is out of power or you’re looking to develop a new group of people and it’s really a human capital management strategy. And then you have a media model, the strategy of saying, “Look, we got to popularize this.” How do you balance these motivations?

Charles Yang:

We very much don’t want to be a holding pen. There are a few think tanks who really specialize in that. And I think the benefit of being bipartisan is we can sort of flexibly play on both sides.

We’re very much focused on coalition building and getting a broader set of folks on board with this, and then helping shape the discourse in DC. One takeaway from my six–seven months so far is that the think tank space is very performative, and the actual shaping of discourse is actually much more subtle most of the time. It’s much more illegible, but most of the performative nature of reports or publishing is simply to tell your donor that you did it in a way that’s provable.

But we’ve done a lot of private dinners, and we’re doing some other work, building coalitions, for instance, with the Abundance folks (note: listen to our episode with the New York Abundance leaders from last year), to the point around getting other camps on side with reindustrialization and thinking about what that intersection looks like. We’ve done some other things that are more for fun. We did a rave for nuclear energy with a few friends, which was the after-party for the American Conservative Energy Summit.

Danny Crichton:

The donor management part of all this must be really interesting. In general, donors are generations ahead. They’re people who have built wealth, so they tend to be older (often by decades) than fellows and people who are going into politics. I’m thinking about some of the folks who sponsored my research over the years. I can’t imagine proposing a rave.

Charles Yang:

Part of my model is thinking about America’s new generation of wealthy stemming from arguably the greatest wealth creation event in history, which is Silicon Valley. They are much younger and they are much less integrated with traditional institutions. They’re more online, they’re more interested in doing things with some of their money that are not as traditional.

You see this showing up in this new age of think tanks that have websites that look different, that are doing micro sites, that are doing raves.

Danny Crichton:

We’re talking about the power of influence, influence of power. You are coming at it from a novel direction. Obviously, there are a lot of incumbent institutions that still have a lot of resources. How do these all integrate? Are these just different levers? Are they different layers? Are they targeting different people?

Charles Yang:

When I think about power, power is people. It is people who change policies, it’s people who write policies, and it’s people who are the ones implementing them. So if you think about stocks and flows of people, the Trump admin certainly has done very interesting stuff. Like with DOGE, for instance, bringing in a new class of tech talent that otherwise would’ve never, ever thought about government work.

I think this is what continues to amaze me about DC, and why I think it’s so beautiful: you will always meet people from every walk of life who just come because they really care about some issue. People you would’ve never expected are in the reserves or working on nuclear security or working in their local representative’s office. How you shape the mindset of the people who are coming in is key.

Danny Crichton:

There’s a whole hierarchy — social networks that run across the legislative branch, the executive branch, people in and out of government, programs that connect folks together in unique ways. Where do you start? Where’s the untapped lever of change?

Charles Yang:

It’s a good question. The ecosystems are all different: there’s Congress, the executive branch, the agencies. They are distinct sets of people, and their modes of operating or theories of change, the type of work they do, are all quite distinct.

With CIS, it’s really starting from where I am and then where we can go from where we started. That’s why, for instance, we did this bipartisan dinner series because I worked with a lot of folks who were Biden political appointees on industrial policy, and I knew some of the new folks who were coming into the Trump admin.

That’s sort of one example of where we started with just what we had and where we could go from there, and I think everything else was sort of opportunistically building off of that.

Danny Crichton:

I’m in a bunch of strategic planning meetings, which I’m guessing your think tank may have. But this gets at a very challenging topic, which is... As much as I work at a free-market think tank, we have plans that go for multiple years. They’re almost five-year plans because you have strategic direction, you have to match donors, you have a budget, you have dozens and dozens of staff. There’s a lot of work to build up a project, get it out there, get it into the hands of the right people, et cetera.

But then there are those inspirational moments of like, “Hey, there’s a policy topic. We can host a conference next month.”

What I am curious about, because you’re focused on industrial policy, which from my perspective is one of those things with a very long time horizon, is how you balance between planning and improvisation.

Charles Yang:

We are very actively testing out the can-you-just-do-things theory in DC. This is not a traditional way of starting a think tank or running a think tank, but we’ve been very opportunistic, and we’re seeing how far that can take us.

Danny Crichton:

Talking about people, you have this Knudsen Fellowship. Tell me about the selection process for that. How do you think about that expertise?

Charles Yang:

When I was at the Department of Energy working on industrial policy, I can tell you, for many of the manufacturing and critical mineral projects — projects with outcomes of $10 billion plus — decisions were made on the basis of the three or four people we had in the department who had real industry background. If those three or four people hadn’t come in, who knows where those billions of dollars would have flowed. I think that’s true of every kind of industrial policy program we’ve seen.

The pipeline of talent for people with actual industrial experience is very small. Frankly, it doesn’t exist. I was also inspired by a lot of the tech policy fellowships that have now sprung up and have been very successful in shaping AI policy and tech policy more broadly and bringing in technical talent, software engineers, AI practitioners into government. I was like, “Well, where is that for industrial policy?” It didn’t exist, so we made it.

In Dan Wang’s book (note, we had Dan on the podcast last year to discuss it), he talked about how engineers are given political responsibility early in their career, and given progressively more and more responsibility over time. So to your earlier question about how people learn, I think you learn by doing. Obviously, the political world is a very different one. I had to go through the learning process, but there’s no reason someone can’t do it. We try really hard to teach lawyers about industrial policy and how industry works, but maybe let’s just try an experiment where we teach some engineers how policy works and just see how that plays out. That is a big part of the Knudsen Fellowship, where we are taking a lot of founders and engineers.

Danny Crichton:

Policy and politics are different. But on the policy side, it’s mostly about trade-offs, and engineering’s fundamentally about trade offs.

Charles Yang:

Yeah.

Danny Crichton:

You are constrained by physical features, you’re constrained by budget, you are constrained by the talent available, the construction costs, whatever the case may be. If you’re building a building or building a software project, you work with infinite levels of trade-offs. That is exactly what government does, too.

Charles Yang:

100%. Oftentimes, the trade-offs are technical, and you really want an engineer in the room when you’re thinking about how the margins across different parts of a supply chain influence the type of capital that we use. I don’t think you learned that in law school.

Danny Crichton:

I mean, the lawyers have to be there at some point. But I do think, in many cases, programs haven’t been designed from the bottom-up in the right way because no one has thought about the users, the customers, the margin structures.

Charles Yang:

I will say – and it’s something I wanted to say earlier — that in my ideal state, we would probably have a lot fewer think tanks. We would just have the state capacity to really analyze tradeoffs for ourselves.

But that comment aside, I’ll just tell you what we’re doing for Knudsen Fellowship and you can infer. We see this as teaching engineers and founders how to engage with policymakers, and the policy memo is one of the basic primitives of that. So we’ll be publishing all the policy memos that come out of the fellowship on our Substack, but the point is really for them to learn how to engage a set of policy ideas and communicate them clearly.

Danny Crichton:

I was in a PhD program focused on industrial policy in 2014. Someone came up to me, a professor, and was like, “That is the dumbest thing I’ve ever heard. Industrial policy does not exist. It will never exist again.” I would’ve graduated in 2020, 2021, but I dropped out. And it’s very ironic because industrial policy seemed to come back right as I was looking to do it.

But one of the things I’m curious about is to what degree is this a field in which you need new ideas, new research, new topics versus, “Hey, we have this huge stack of ideas and a lot of it is about organizations and politics.”

Charles Yang:

I started a YIMBY group in my hometown in a previous life. The things they ended up doing are not groundbreaking, it’s simply doing the work. And that’s sort of the spirit, I think. Around national policy, there are still new ideas to be unearthed, but I agree that there aren’t that many. There aren’t 36 in a policy playbook that are all brand new. Sometimes it’s bringing back things that are old, but are now relevant once again. A lot of it I think should be learning from how other countries have done this successfully and how it applies to our political system.

And then, again, to the people-centered approach here, it is just getting the right people in the right places who are willing to drive outcomes and take responsibility within the broader policy apparatus or build connections between different political groups. People ask me sometimes, “What industrial strategy should we have?”, and usually my response is, “Almost any strategy will do just as long as we stick with it.” And the “sticking with it” part requires coalition building and getting people to share a common sense of goals and outcomes in the political system. That’s one of the key things we’re also working on.

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Prediction markets may be focused on conquering traditional sports betting powerhouses like DraftKings and FanDuel, but their viral hits so far are political and cultural. For instance, will Jesus Christ return before the end of 2025? As Bloomberg reported, that Polymarket bet paid out at a rate of 5.5% — better than a high-yield savings account.

Yet defining events and whether they take place or not is a complicated art. Controversy brewed at Polymarket this week after it announced it would not pay out bets on a U.S. invasion of Venezuela, contending that only control of territory would trigger the payout. Thus, Nicolas Maduro’s abduction is not classified as an invasion.

Here’s the challenge: the squishiness around words like “invasion” or “war” is intentional. Adversarial ambiguity is used by nation-states, corporations and even individuals to take action while avoiding some or all of their consequences.

With adversarial ambiguity, it’s entirely possible for the United States to take Greenland and announce it while Denmark never acknowledges the loss but rather acquiesces to the reality of it.

When did Russia invade Ukraine? Was it when the so-called little green men started maneuvering in Crimea and the Donbas in 2014? For Moscow, these unidentified Russian soldiers masquerading as an insurgency force offered plausible deniability in its diplomatic negotiations. For Washington, war is technically only official when Congress passes a declaration, which hasn’t happened since World War II.

Given the high potential costs of war — up to and including nuclear strikes — nearly all of today’s great power conflicts are hybrid or use some form of gray-zone tactics. For instance, in 2017, Russia was widely reported to be seizing Georgian territory by simply moving the border sign and fence between the two countries a few inches to a few feet at a time, day after day. Does that inch count as an “invasion” since it involves control of territory? It’s precisely the ambiguity that allows Russia to avoid a wider conflict.

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Over in the private sector, we see adversarial ambiguity in the insurance industry. Homeowners who survive hurricanes quickly learn that there can be a massive disparity in benefits between wind damage and water damage, the latter covered by the federal flood insurance program while the former is often covered under a private policy. What caused the damage to a home?

Insurance attorneys can spend years arguing over definitions and evidence in an attempt to transfer future payouts to the government or another carrier. We’ve seen a similar pattern in the aftermath of last year’s wildfires in Los Angeles, where lawyers continue to argue over what caused the damage to specific properties, just one of many reasons rebuilding efforts in the region have stalled.

Insurance companies repeatedly fight each other from different sides of the table, so there is an economic incentive to resolve adversarial ambiguity and make the industry more efficient and therefore lucrative. In response to high legal costs and legions of frustrated policyholders who may wait years to get their lives back on track, insurers have become increasingly receptive to the idea of parametric insurance. Instead of arguing endlessly over the cause of particular damage, parametric insurance pays out based on objective factors. For instance, a policyholder could buy a policy for wind speeds exceeding 120 miles per hour for over 15 minutes and would get paid even if their property had no damage. On the other hand, they would also not be paid if their house was destroyed but wind speeds were 119 miles per hour.

Such objectivity is elusive in politics, nor are there incentives for nations to cooperatively resolve ambiguity. Take “Will Trump acquire Greenland before 2027?,” which is currently trading at a 15% chance with a market volume of $3.3 million and is one of the bets the Polymarket homepage is pushing to me. There are four paragraphs of rules attached to the bet. The first is relatively simple:

This market will resolve to “Yes” if the United States officially announces that Greenland will come under US sovereignty by December 31, 2026, 11:59 PM ET. Otherwise this market will resolve to “No”.

The second paragraph is much more complicated:

Sovereignty is defined as the transfer of the majority of the territory of Greenland from its current status as an autonomous territory within the Kingdom of Denmark to being under the formal governance or jurisdiction of the United States, either as a state, territory, or other classification within the US system.

Territory here is interesting. Is it the physical land of Greenland, or does territory include Greenland’s territorial sea and exclusive economic zone? Since the bet pays out if President Donald Trump acquires “the majority of the territory,” figuring out the denominator is crucial. Jurisdiction is another open-ended and ambiguous component, but let’s move on to the third paragraph:

An official announcement made by the United States and Denmark that Greenland will come under US sovereignty will qualify, even if the actual transfer of sovereignty is yet to occur. Only announcements of official agreements or actions (e.g. executive order, signed legislation, etc.) will count - mere posts on Social Media will not.

Did you notice what just happened? We started the rules with “if the United States officially announces” and now we have “an official announcement made by the United States and Denmark” (emphasis added). Which is it? With adversarial ambiguity, it’s entirely possible for the United States to take Greenland and announce it while Denmark never acknowledges the loss but rather acquiesces to the reality of American military might in order to partially assuage popular anger. Practically every diplomatic negotiation will include such ambiguity as a way to usher parties toward an agreement. Let’s move on the fourth and final paragraph of rules:

The resolution source for this market will be official information from the governments of the US, Greenland, and Denmark, however a consensus of credible reporting confirming that Greenland has come under U.S. sovereignty will also qualify.

Oh my. “A consensus of credible reporting” is one of those beautiful phrases that harkens back to a halcyon era when adversarial ambiguity and weaponized truth were mere figments in the imaginations of the most inventive sci-fi authors. It reminds me of my article “Consensus Functions” from a few years ago, in which I describe why it’s become impossible to reach any form of democratic consensus in any context, and what we should do to push forward institutions like science.

Once again, we see a contradiction in the rules. “Has come under U.S. sovereignty” is not the same as “under the formal governance or jurisdiction of the United States.” Greenland could be under U.S. sovereignty but not formally under the jurisdiction of the United States.

The premium for Jesus’s arrival is not a commentary on religious belief, but rather the market pricing in the complication of determining that bet’s outcome and Polymarket’s fidelity in doing so.

I can point out half a dozen other asinine, pedantic problems. I realize I am closely reading what is otherwise a gamified gambling experience that’s meant more as entertainment than professional investing. Ultimately, Polymarket can cancel a bet and just refund every investor’s wager, and outside some aggrieved would-be winners, everyone can move on. Indeed, as a market, prices can include our relative beliefs about Polymarket’s judgment in resolving these bets. The premium for Jesus’s arrival is not a commentary on religious belief, but rather the market pricing in the complication of determining that bet’s outcome and Polymarket’s fidelity in doing so. The potential ambiguity of resolution raises the cost.

Prediction markets and sports gambling have massive negative repercussions for society as Charles Fain Lehman noted in The Atlantic and I pointed out in “America’s Gambling Fetish.” But one upshot is that more people will be exposed to the elite battles over words, terms and definitions that have made adversarial ambiguity a hallmark of great power politics. It’s not just about reading the rules, it’s about realizing that the whole point of many actions is to be able to ignore the rules entirely.

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South Korean President Lee Jae-myung has the toughest job on the world stage. He has to placate U.S. President Donald Trump and protect his country’s massive and growing trade surplus with the United States. Then he has to rebuild long-frayed ties with China while pressing the country to help him improve relations with always-be-firing-missiles North Korea (the latest salvo came Monday, complete with alleged hypersonics). Plus, Lee must balance the Korean public’s historical antipathy toward Japan while forging a relationship with Japan’s most nationalist leader in years in hopes of buttressing Northeast Asia’s axis of democracy.

But wait, that’s not all! Lee must also orchestrate a complex arrangement of deals with the Middle East to promote South Korea’s massive nuclear power buildout, including what could be a blockbuster bid in Saudi Arabia. That buildout is part of the region’s sovereign AI initiatives, a challenge Korea is facing as well as it competes against heavyweights from both West and East. Finally, there’s the ongoing awkwardness of South Korea’s defense exports to Ukraine, with the Korean defense industry growing to record highs even as it bans exports to active conflict zones.

This is the endless struggle of a straddle power without the ability to drop special forces on a presidential palace and ransom a country’s oil supplies. Such a position might be extraordinarily challenging, but it also presents equally extraordinary opportunities that can only be seized through a dauntless and ever-evolving strategy.

Thanks for reading Riskgaming by Lux Capital! This post is public so feel free to share it.

During the Cold War, political scientist Robert O. Keohane wrote a review article titled “Lilliputians’ Dilemmas,” which first appeared in International Organization before coalescing into a Foreign Policy essay. He discussed how small powers balance relations with big ones, arguing that the first and most important criterion for a small power to navigate the whitewaters of great power politics is that it “…must have a high degree of maneuverability within limits set by its own domestic politics.”

What does that maneuverability look like? Well, it’s constantly inventing new reasons to attract big powers to you on favorable ground. In one of his most profound lines, Keohane wrote “American policymakers are too busy to think up grievances for states that cannot manufacture their own.” In other words, straddle powers need to assiduously create complex conditions where they can secure favorable tradeoffs.

For instance, while Korea is at the pinnacle of a number of industries, AI memory is its most strategic asset right now. SK Hynix is one of the world’s most important manufacturers of high-bandwidth memory, which is required for the most complex AI computations.

The dizzying growth of the AI sector is pushing SK Hynix’s stock and that of competitors like Samsung Electronics to record highs. Over the past month, the company’s stock has jumped more than 35%, and it’s up more than 160% over the past 6 months. Roughly 80% of its employees are domestic, leading to a memory boom in Korean employment and bonus wages that amounted to $80,000 per employee this year (Korea’s GDP per capita is about $36,000). Samsung is right behind with a gain of 130% over the past six months.

Blunt decisions don’t increase strategic maneuverability but instead do the complete opposite, transforming a complex game of negotiations into a linear tug-of-war.

Korean memory exports may not be a rainbow unicorn like Nvidia’s next-generation Rubin chips, but they are indeed supply-constrained, which is leading to record global price increases. That gives the Korean government leverage in negotiations with the United States and China, since neither country wants to surrender an edge to the other by losing priority access to this essential ingredient in the race for AI supremacy.

A single dependency is still a linear negotiation, though, and that doesn’t offer strategic maneuverability (or frankly, a good Riskgaming experience). So let’s remember Keohane’s diktat about manufacturing grievances. Trump has manufactured myriad complaints about Korea, including its yawning trade surplus and its lack of American auto imports. Korea got a significant if temporary reprieve in the aftermath of Trump’s crackdown on a Hyundai and LG Energy Solutions battery factory in Georgia back in September, which saw hundreds of Korean workers deported as they built up American manufacturing.

Yet that factory is precisely the right model. Korea’s most potent strategy is to position itself as the sole enabler of American reindustrialization. While I was off on holiday in mid-December, a major deal was announced that has Korea Zinc upgrading an existing zinc refinery in Tennessee through a $7.4 billion joint venture funded by J.P. Morgan and the U.S. government directly. In the final cap table, the Defense Department would own a 40% stake, Korea Zinc would hold less than 10%, and other investors would own the balance.

It’s a massive project. The Tennessee site is the sole zinc refinery in America, and no domestic facilities have opened for decades. Korea Zinc will take over the existing facility from Singaporean commodities conglomerate Trafigura and upgrade and scale it up to global competitive standards. If the plan is successful, it should shore up America’s zinc supply chain in 5–10 years, improving the country’s resilience in a range of products from metal alloys, like galvanized steel, to chips, batteries and weapon systems.

Since the announcement three weeks ago, Korea Zinc’s stock has gyrated. Initial euphoria sent it zooming to its highest levels in a year, but the stock has since lost much of those gains. It dropped nearly 9% earlier this week when ETF provider MSCI announced the company didn’t qualify for inclusion in its indices due to the share sale required to fund the joint venture with the American government.

Deepening co-dependency isn’t just happening in critical minerals, but also shipbuilding. Korean defense prime Hanhwa closed its $100 million purchase of the Philly Shipyard a year ago, announcing a massive expansion and reinvestment plan that would see volumes increase 20-fold as part of the official — and I kid you not — “ Make American Shipbuilding Great Again” plan (we are definitely better marketers than those Soviet apparatchiks and their “fifth five-year plan” snooze-fests). Hanwha has highlighted that it will train American workers at its Korean facilities, with the goal of improving sovereign shipbuilding capabilities.

Hanwha’s investment complements other announced projects. HD Hyundai Heavy Industries (HII) signed an agreement back in October with Huntington Ingalls Industries (also HII, so obviously a match made in heaven and a nightmare for every journalist and analyst going forward) to rebuild American shipbuilding and invest in “distributed shipbuilding.” HII (the American one) is the shipbuilding company at the center of our first Riskgaming scenario, Hampton at the Crossroads, and it’s the company best known for constructing Gerald R. Ford-class aircraft carriers.

Last month, a three-way agreement brought together American defense prime General Dynamics alongside Korea’s Samsung Heavy Industries and DSEC to focus on next-generation ship design and manufacturing, particularly on the U.S. Navy’s Next Generation Logistics Ship program.

I can keep on going in this Nor’easter flurry of announcements. How many of them will come to fruition much less lead to positive economic outcomes? My cynicism says that there is little chance that more than a fraction of them will ever ultimately be realized. However, I admit I have recently been more impressed at the level of focus on reindustrialization from leaders like Pennsylvania senator John Fetterman, who stopped by last month.

Back to straddling. Korea is attempting to enmesh itself in a complex weave of co-dependent relationships, where it always has another lever to bolster its relative negotiating position against larger superpowers. That’s the right approach for a straddle power: you want to be differentially and strategically valuable enough to raise the cost of intentional or unintentional adverse actions.

That’s obviously the opposite direction of America’s current positioning. Trump’s unilateral cuts to organizations like USAID mean that relatively cheap and featherlight threads connecting the United States to the rest of the world have been burned to smoke. Our trade autarky and increasingly venomous perspective on high-talent visas mean that even the social and business bonds that accrue as American power are weakening.

This approach does give a superpower like the United States more raw power. As I wrote yesterday, we can indeed “just take it” and there’s not much most countries can do in response. Where that strategy goes wrong, though, isn’t when other countries acquiesce, but when they don’t. If and when a country refuses, Keohane’s manufactured grievance toolbox will be empty, and America will be left with only much more expensive strategic actions available.

It’s perhaps the most important lesson I’ve observed across dozens of Riskgaming sessions, and particularly the ones we have conducted using No Man’s Land, our scenario focused on AI capabilities in commercial and defense procurement. Blunt strategies are satisfying to players since they are psychologically thrilling and so much easier to think through. It makes us feel powerful to stab another player in the back, particularly if they can’t immediately respond. Long term though, there are high costs to these fleeting moments of dominant power. Brewing vengeance, for one. But more importantly, the increasing dearth of co-dependencies means that bad outcomes aren’t as easy to prevent in the first place.

The players that succeed in Riskgaming — no different than straddle powers like Korea and others — understand that it is precisely the dense layers of value that have to be unwound that stop undesired decisions by adversaries. Blunt decisions don’t increase strategic maneuverability but instead do the complete opposite, transforming a complex game of negotiations into a linear tug-of-war. It assumes that other players will tug back on that rope rather than ignore it in favor of new strategies and tactics (think hybrid war or gray-zone activities). Brash audacity makes for viral social media posts and the occasional fun selfie, but it’s ultimately deleterious to long-term strategic power.

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Even the sales-and-deals turn of Christmas has its origins in reciprocity. Exchanging gifts is about deepening the ties that bind us all, looking back to pay it forward. It’s the same foundational cultural trait that powers up the startup world too, the reason Silicon Valley can’t be replicated. Nowhere is there a higher density of richly talented and richly wealthy people who are willing to take bets on projects with nothing but trust that it will one day all work out.

That spirit of Christmas is dying. The world is shrinking, resources are getting scarcer and it’s harder than ever to get what you want.

Apparently, what we want is oil, and it’s not for anointing the messiah. This weekend, the United States scarfed up Venezuela’s Nicolás Maduro in a surgical military operation that must be hailed as one of the most extraordinary successes in special forces history. Thanks to the wonders of this strange cosmic universe, Maduro and I now share the same gentrified borough.

The Venezuelan people have suffered mightily under Maduro and his predecessor Hugo Chavez. For almost three decades, the two have impoverished one of the most entrepreneurial countries in the world — a country that also happens to sit on an extraordinary endowment of natural resources that would be the envy of all but a handful of nations. Upward of a million refugees — from a population of about 30 million — have fled the miseries and trepidations of an autocratic government and a shrinking economy. And that’s just to the United States — an estimated 7 million more have fled to neighboring countries.

You have what we want, and we are going to just take it.

That’s not why we captured Maduro, though, for that would be far too humanitarian and charitable for the new Christmas spirit. It was oil, spoken plainly by President Donald Trump. This is no longer the Bush administration and its soaring (if at times sputtering) rhetoric of freedom and democracy. You have what we want, and we are going to just take it.

It’s the international relations equivalent of California’s $950 theft limit, a marker that divides a misdemeanor from a felony and which led the City by the Bay to become the City that Locks Up the Toothpaste. There’s no loss prevention strategy in Venezuela, though, since there’s not much to lose, empty shelves being Bolivarian socialism’s lasting legacy.

Just take it. I wish this wasn’t a pattern in American life, but it’s commonplace. Ro Khanna, Silicon Valley’s left-progressive congressman and a man who could really use that locked-up San Francisco toothpaste, certainly understands the need to just take it. Over the holidays, in spat after spat after spat with venture capitalists, he vociferously defended a proposed wealth tax meant to fund whatever it is that California considers a functional government.

Apparently, after years of walking the Fisher Price-colored corridors of the startup wealthy with hat in hand asking for coins every biennial (a hat that was ultimately filled to the brim, I might add), he’s finally had enough with it all and has decided to just take it. One time only, of course! I guess he hopes we can all feel the warmth of collectivism during the holidays. Call it the Wrath of Khanna, the superior man who lost his mind to aggrieved vengeance and is now biting the hand that feeds him.

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Biting the hand that feeds you, or just pocketing it. I grew up in Minnesota, a land noted for its Nice people. It’s really hard for coastals to understand what it’s like to live in a society where your neighbors will always do the right thing, where charity — religious or otherwise — is paramount in the lives of most, and where calls for food donations or blood drive flyers have to include a proviso apologizing in advance that they won’t be able to accept everything that will be donated. That was just Minnesota Nice culture, which continued long after I transplanted to the golden foothills of South Bay.

That culture of egalitarian benevolence is now in serious jeopardy. Feeding Our Future was the name of the non-profit that’s at the heart of the multi-billion-dollar Somali fraud story that has spread like wildfire since my colleagues at City Journal published their deep dive right before Thanksgiving (an irony missed by most). Tim Walz, Minnesota’s governor and former vice presidential candidate, announced yesterday that he wouldn’t seek a third term because of the growing scandal, putting an ignominious exclamation mark on an otherwise decent man’s tenure.

Civilization isn’t the police and courts, it’s a glass house that’s wide open with a sign that says “no one is home” and nothing happens.

The response from my coastal friends has been sheer bewilderment at the stupidity of the Minnesota officials overseeing welfare distribution. Take it from a still-prideful home state lover — naivety is justly earned. Leaving your doors unlocked is only foolish if you believe it’s impossible to live in civilization. Civilization isn’t the police and courts, it’s a glass house that’s wide open with a sign that says “no one is home” and nothing happens.

Venezuela, California, Minnesota: these three stories are merely the leading headlines of a repeated pattern in our culture today. America’s enlightened ethos of can-do “just do it”-ism has become today’s darker “just take it” psychology of the Beltway robber, the Barbary petrol-man and the Bitcoin fraudster. Positive-sum economics has been slaughtered by the atavistic dominance of zero-sum or even negative-sum thinking. It’s every man, woman and child for themselves.

I just watched Park Chan-wook’s new film No Other Choice, a story about a down-on-his-luck blue collar worker who is laid off after 25 years at a paper company. He realizes that the perfect job still exists out there, but there’s only one position and a lot of highly-qualified candidates waiting to be interviewed. How far must he go to eliminate the competition, save himself and secure his livelihood?

It’s a thrilling, terrifying and at times downright hilarious romp. It’s also part of an increasing oeuvre of films that critique competition in modernity like Josh Safdie’s Marty Supreme, Bong Joon Ho’s Parasite and Galder Gaztelu-Urrutia’s The Platform. These films observe this zero-to-negative-sum culture, but they either direct the blame for it to structural factors beyond individual control or naturally assume this culture is part and parcel with modernity. Absolutes have become relatives. Rather than trying to outrace others in the great game of life, we at some point realized we can just take a hammer to the engines of our competitors and watch as they sputter while we drive at whatever speed we want.

Competition is inevitable in any resource-constrained environment. Earth is no exception, and even becoming a multi-planetary species won’t release us from this basic law of economics. It’s how we address competition and its implications that determine the tenor of civilization. We can cultivate competition and the human desire for victory to frenetically recombinate brilliance and push forward the frontiers of technology and productivity. Alternatively, competition can be extraordinarily corrosive, undermining the will to work or invest and dragging everyone down together (see Venezuela, Country of).

America’s most important strength was — and always will be — its precarious meritocracy of the market. Good products generally beat bad products because consumers can suss out a better deal. Good ideas overcome incumbents because Americans are optimistic and willing to embrace better futures. Good institutions supplant bad ones because efficiency and performance matters to generous but impatient funders.

Just-take-it culture is an economic game of musical chairs, but the chairs are made of wood and the participants have flamethrowers.

All those safeguards have washed away. Consumers are cynical and desperately fighting capitalism’s dark patterns and enshittification. Pessimism has replaced optimism, entrenching narrow ideas over the potential of tomorrow. We no longer expect our government, courts, non-profit organizations and civil society to be fair but rather capricious, something best avoided rather than engaged.

“Just take it” has a double meaning, one that extends back to The Melian Dialogue of Thucydides. It’s the claim of the victor to what’s not rightfully his, and the submission of the loser acquiescing to a tragic fate. Two sides of the same coin, a penny that is fought over even as it is taken out of circulation.

Our energy to fight for a prosperous system is drained as we battle each other. Just-take-it culture is an economic game of musical chairs, but the chairs are made of wood and the participants have flamethrowers. What looks like an ample supply of seating quickly burns in an inferno.

Our present moment reminds me of Koushun Takami’s Battle Royale, which I read during my very first Lux partner offsite, sitting in earthquake-prone California below a hotel ceiling with a very concerning crack. The novel, which became a movie and an entire genre of multiplayer video games, is about a group of school kids sent to an island in which only the last remaining survivor is allowed to walk away and live.

The most poignant moment comes during a scene at a lighthouse in which a group of students desperately tries to create a small island of civilization against the murderous impulses of their classmates outside. Their small society works for a time, yet zero-sum thinking will come for them, too, even as they rebel against it. You can’t be good in a bad system.

It is hard not to feel anguish as we gaze at the grand expanse of history; millennia of evidence accumulated across innumerable societies and civilizations proves that the simplest yet most potent answer to what makes a good life is that positive sums not only exist, they are almost omnipotent in their ability to ameliorate the worst aspects of human existence and give our lives meaning. We still have that culture in parts of Silicon Valley, and it’s still the default mode for most Americans, even as it’s getting harder to sustain.

Why do we give Christmas gifts? Often the monetary value of these offerings are normalized within families, and so it can feel circular on that great economic spreadsheet of existence. But even if the dollar values are the same, those gifts come attached with emotions and ultimately memories. No net value was exchanged, yet incalculable value was created and offered. The magic of Christmas is the magic of civilization and what’s made humanity such a force for good: a gift economy means always being surprised that something better is in store. If I have faith in anything, it’s that we can, even now, still just do it.

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The Best of Riskgaming 2025

For those counting, we published 104 newsletters, 47 podcasts and four new scenarios. We also hosted about 24 events including runthroughs of Riskgaming scenarios, community meetups and geopolitical dinners. In total, about 500 people got to join us live for an experience — definitely an upgrade thanks to Laurence Pevsner joining as my partner at the tail end of 2024.

As always, thanks for reading, listening and attending — your commitment has allowed Riskgaming to turn into a powerful institution for profound thought on some of the most complex issues facing the world today.

Highlights from 2025

• We hosted 60 senior leaders from the United States and the United Kingdom for a biotech summit at the U.K.’s embassy in Washington DC as part of Prime Minister Keir Starmer’s trade mission to the United States.

• I pissed off some Pennsylvania politicians with an op-ed in The New York Post on President Trump’s horrifying deal approving U.S. Steel’s acquisition by Nippon Steel.

• I pissed off some public sector labor unions and mayors with an aggressive essay in City Journal on the need to use AI to automate more of city government.

• Dozens of people in New York City and in Los Angeles played our biotech game Experimental Automata during Upfront Summit.

• We were profiled by Business Insider, Payload Space and The Wall Street Journal, complementing past coverage in NBC News and The Information.

• I learned that Senator John Fetterman is a complete giant compared to me:

• We hosted a senior delegation of Canadian political and business leaders in Toronto to play No Man’s Land.

• Laurence launched Gray Matter with sellout events in New York City and in California, doubling down on biotech gaming with our first live trading card game. Negotiations were fierce, and you know you have a winning game when it is impossible to bring the room back to order so you can give the final results. We’ll publish the game in early 2026.

• We hosted a prominent former U.K. defense (I’m sorry, defence) minister for a Riskgaming session (who is excellent at negotiation, I have to say).

• We had a great time launching Ian Curtiss’s Southwest Silicon to a distinguished group in London from the U.K.’s Foreign and Commonwealth Office, the Department of Business and Trade and other ministries as well as in DC with Eric Schmidt’s Special Competitive Studies Project.

• We hosted our first comedy revue with Truth Santa, which will always have a special place burned into my heart (note to self: never write a comedy show under deadline).

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New Riskgaming Scenarios for 2025

• Experimental Automata: A Global Investigation of the Future of Digital Health and Biotechnology — a salon experience for 60 that educates on the progress coming in human wellness and agriculture.

• No Man’s Land: The AI Singularity and the Future of American National Security — the most in-depth full-immersion game we’ve ever made. This game takes eight players through the challenging tradeoffs of balancing commercial and defense security in the AI industry alongside a zany storyline that has now come to fruition in the real world.

• Southwest Silicon: Water, Semiconductors and the Future of American Economic Security — a full-immersion game that simulates the strict tradeoffs facing water-parched homeowners, farmers and the emerging chip fab industry in Arizona’s Phoenix Valley.

• Truth Santa: A Lux Riskgaming Holiday Experience — our holiday comedy revue that we hosted in SF and NYC. You had to be there, but you can see some notes and photos on the game’s unlisted page.

Best Newsletters of 2025

We switched to Substack this year, which was a huge help in terms of user experience and that all-important growth metric. Here were my favorite pieces of writing this year:

• Make gray-zone war expensive again — Unusually for me, defense was not one of my main topics this year, but gray-zone warfare is a perennial interest of mine. The costs of hybrid war have declined dramatically, encouraging more state and non-state actors to use these tactics to tremendous effect. We need to raise the costs again.

• The machine burns down — A meditation on a massive data center fire in South Korea and E.M. Forster’s The Machine Stops. Our lives are increasingly handled by machines that are now central points of failure for our society. Catastrophe awaits without better resilience and chaos engineering.

• Open secrets harm society far more than AI or disinformation — It’s the age-old story of the emperor’s new clothes: Everyone knows and agrees on facts that can’t be said out loud. It’s a pattern of behavior profoundly corrosive to democracy.

• Should AI recommend God? — AI chatbots like ChatGPT respond from an agnostic/atheistic point of view that reflects Silicon Valley’s mainstream values. To what degree should popular religions be brought up as paths in these chats? Is proselytizing through technology immoral?

• Why is economic data disappearing? — The declining quality and transparency of American and Chinese economic data mean that the twin superpowers are increasingly weaponizing perceptions of their economies at the expense of reality.

• The taps are dry and the rivers are flooded — A meditation on rivers inspired by James C. Scott’s In Praise of Floods. We need to pay more attention to water security.

• We’re finally predicting the weather accurately — Small progress made annually can add up to massive change over time. Weather forecasting improves by about a day every decade, and that is radically enhancing industries from logistics and agriculture to air travel.

• Uncle Sam is a terrible board member — My screed against the U.S. government taking board seats at U.S. Steel. The government can’t be a robust board member when it doesn’t have economic alignment and fails in its fiduciary duties.

• Tech is turning the Middle East into a pivotal region — Our image of the Middle East needs a dramatic renovation. It’s now a bountiful land of opportunity, dynamism and surprise — and it’s becoming a powerful economic fulcrum in innovation.

• Riskgaming against a world on fire: A manifesto, of sorts — This is the longest post of the year and a summary of my goals with Riskgaming as we moved to Substack. We constantly strive to avoid easy solutions to complex problems while uncovering the deep interconnections that constrain and direct today’s events.

• Is Plagiarism Dead? — AI is radically changing the definition of creativity and ownership. What is the future prognosis of the norm against plagiarism as AI tools reshape what we think of as originality?

• Asteroids, xeno-kidneys, planes and H5N1 — A deep dive into different types of risks and why it is still so hard to mitigate long tail risks even when they are predictable.

• Should you wait to have kids? — New biological technologies under development will radically improve the health of children right from conception. In the interim, that dramatically raises the relative risk of having children the next few years.

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The Best Podcasts of 2025

For your listening pleasure over the holidays, we’ve posted a special podcast episode recapping the year titled “11 Clips That Defined 2025.”

• The inside story of the billionaires fighting for space — The Washington Post’s Christian Davenport on Elon Musk / SpaceX, Jeff Bezos / Blue Origin and the race to colonize the atmosphere, the moon and ultimately Mars.

• Why AI safety is like a bolt in a croissant — Former Popular Science editor Jacob Ward on the ethical dilemmas facing the big general-purpose LLMs from OpenAI and Anthropic.

• America’s degrowth lawyers need to learn from China — Dan Wang talks about his everyone-is-reading-it book Breakneck and what we can learn from China’s engineering state and America’s lawyer society.

• The CIA in the 21st Century — The epic espionage chronicler Tim Weiner talks about his new book about the CIA over the past two decades, from the challenges of the Global War on Terror to its renewed war against superpower adversaries Russia and China.

• Intel, chips and America’s future — Dylan Patel of Semianalysis and I debate Trump’s partial bailout of Intel and what it means for the future of industrial policy and chips.

• What’s next for European defense autonomy — With Russia continuing to wage a brutal war on Ukraine, I talk with venture capitalist Eric Slesinger on the prognosis for defense innovation on the Old Continent.

• How Jane Jacobs got Americans stuck — The Atlantic editor Yoni Appelbaum discusses his new book Stuck on how declining geographic mobility is limiting opportunity for millions of Americans — and how to get people moving again.

• Can we ever defend against agricultural warfare? — Alicia Ellis, Arizona farmer and global security professor at ASU, talks about a new domain for conflict: agricultural security and the food supply chains that keep people nourished.

• Europe needs national champions, now — An in-depth discussion of Europe’s present place in the world from BCA Research’s chief strategist Marko Papic.

• How can we make the internet fun again? — Renée DiResta is one of the original researchers of the internet, and she’s made it her mission to find the joy again in this once optimistic medium.

• How Russia is bringing the cost of global sabotage to zero — My favorite episode of 2025 was with Daniela Richterova of King’s College London. We discuss how Russia has reduced the cost of sabotage roughly to zero by taking advantage of the gig economy, cryptocurrency and increasing polarization in the West to find solitary allies on the cheap.

That’s the end of 2025 — as I joked on the podcast, I am sure 2026 will be so quiet that we will have to shut down for a lack of content. Okay I’m kidding, but let’s just hope there isn’t a nuclear war before we all return.

1.

First up, Europe may be a technological backwater, but it is the center of the geopolitical world. Here’s Laurence Pevsner chatting with Marko Papic, the chief strategist at BCA Research, about Europe’s adaptability in the face of threats like Russia.

Europe needs national champions, now

Pressure makes diamonds. That’s what I would say. I think our two oceans have made us fat and lazy in many ways, geopolitically at least… I think that pressure makes diamonds. I mean this is what human history tells us.

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2.

Europe, and particularly the European Union, seems like one big stream of regulatory ooze. Well, America is hardly immune to the same depredations as Yoni Appelbaum makes clear. In his book Stuck, he describes how housing growth has halted in vast regions of the United States, forcing Americans to stay in place rather than seek opportunity elsewhere. Here, Yoni talks about the history of zoning and some very Riskgaming-esque incentive patterns in local communities.

How Jane Jacobs got Americans stuck

One of the stories, one of the arcs I trace in the book, as you say, is that wherever you get a polity which gives a local community the power to police who gets to belong to that community, over time, it will tend toward exclusion, and there’s a very good reason for this. Even if it is populated by a band of angels, the politicians who are running that community, the members of that community will prioritize the interests of long-term residents over the interests of prospective residents.

3.

Democracy versus authoritarianism is a challenge we discussed repeatedly in 2025. To what degree in a democracy like America should we quote-unquote just build, at the expense of endless due process. One of the most popular books on the subject came from Dan Wang’s book Breakneck, which chronicled his six years in China through the Covid-19 pandemic. Here’s Dan on China versus the U.S.

America’s degrowth lawyers need to learn from China

As a meta point I would say, let’s think a little bit more about what it is that people really want. Maybe sometimes it is the case that they want to feel that the contract on how to build the subway and who is subcontracting, that process is fair, but maybe often what they care the most about is having that new subway at all.

4.

These debates about governance are high-minded, but we also live in a whole new world where terrorists have highly-scaled access to cheap drones and generative AI that’s revolutionizing their businesses. We talk defense the next few clips, first with Colin P. Clark, who is a Senior Research Fellow at The Soufan Center. He talks about how technology is radically lowering the barriers to global terrorism.

How Russia is bringing the cost of global sabotage to zero

Technology is changing the way that we look at terrorism, tremendously. I think it’s just the tip of the iceberg. And so there’s often a lag effect to these things. And in general, terrorists tend to be early adopters, right? Because technology can be what we call a force multiplier. So if you are a small insurgent group based in the Sahel in West Africa, and you’re now tinkering around with generative AI that can set it and forget it in terms of your propaganda, you’ve now freed up a significant amount of manpower hours to go do what terrorists do, which is plan, plot, and conduct attacks.

5.

It’s not just non-state actors like terrorist networks that are taking advantage of the enabling power of technology. Even large nation-states like Russia are updating their models, taking advantage of cheap freelancer labor to revolutionize the clandestine war behind Europe’s front lines. Next up, we have Daniela Richterova, a Senior Lecturer in Intelligence Studies at the Department of War Studies, King’s College London, talking about the “gig economy of Russian Sabotage”.

How Russia is bringing the cost of global sabotage to zero

We’re seeing a shift from these well-trained and tested agents executioners to basically amateurs who are being recruited in all walks of life from all sorts of countries. There was one from Latin America, a number of Eastern Europeans who were recruited, but also other nationals, some western nationals as well. And from what we saw, this recruitment happens online now, and that’s where the whole gig economy idea comes from. Google platform such as Telegram, these individuals are able to basically volunteer for a job.

They are told how much this job would cost, where this would take place. They’re not always told what the purpose is. They can bid for a job as if they were an Uber driver.

This post is public so feel free to share it.

6.

Russia is adapting its espionage playbook by cutting costs and outsourcing its activities to third parties. Meanwhile, China is fully embracing the digital- and data-centric world that we find ourselves in. The country’s aggressive counterintelligence program has cost the CIA and its assets dearly. Next, we have Tim Weiner talking about his new book, “The Mission: The CIA in the 21st Century” and how the agency is struggling to adapt its tradecraft.

The CIA in the 21st Century

Espionage in an age of ubiquitous technical surveillance is a whole new thing, and the American intelligence community has been desperately trying to stay at the curve or ahead of the curve in AI and particularly in quantum computing.

7.

Sabotage and skullduggery are changing rapidly in 2025, with countries taking advantage of new forms of hybrid warfare that allow them to sap their enemy’s capacity without attribution. One new domain of concern is agricultural security, which can be easily and wrongly confused with food security. Here is our Riskgaming scenario design consultant Ian Curtiss talking to Alicia Ellis, professor at the Global Security program at Arizona State University, about why ag security is an emerging new threat we should all be worried about.

Can we ever defend against agricultural warfare?

If you’re talking about agriculture, economic warfare or cyber information and narrative warfare, all of these things are not simple responses. Most of them don’t even traditionally fall under the purview of the Department of Defense, so who handles it? Especially when you’re talking about agencies that don’t typically think of it from a security perspective. So those hybrid threats are almost perfectly designed to not so much counter as slip past the way US systems are designed to respond to threats.

8.

Let’s pivot a bit from the world of espionage to the world of defense primes. War came to Europe when Russia invaded Ukraine in early 2022. The stasis that was the hallmark of Europe’s post-Cold War defense infrastructure has since flowed away, and a vital new energy has been restored on the Old Continent. Mirroring some of Marko Papic’s views on the dynamism of Europe, here is Eric Slesinger, founder and general partner of 201 Ventures, talking about European defense autonomy and its new wave of startups.

VC Eric Slesinger: "People are finally questioning why in the world does Europe have 17 different battle tanks and the United States has one"

There are things that European founders are watching happen in the US and doing differently. As an example of that, a lot of the M&A heavy capital intensive plays in the US, I think European founders are asking if there’s enough ground support for that to actually work in Europe. Are there enough acquisition targets? Is there enough capital to be formed to be able to do that strategy? I think that’s going to be a little bit different.

9.

Eric was mostly talking about defense startups and the funding required to get them to scale in Europe. Over here in America though, we have a bigger problem: how to protect critical companies like Intel and its fabs. The U.S. government swooped in to buy 10% of Intel’s equity this year, a shocking change to the traditional libertarian views of the Republican Party. Was it the right choice? Dylan Patel, founder and CEO of Semianalysis, joined to talk about the hard choices for the U.S. on semiconductors.

How compute and AI will create next-gen superapps

I don’t like state capitalism either. I think that’s a terrible idea. We win by being an economy that’s competitive and an economy that is capitalistic. And there’s certain ways to do state investment into industries that actually improves that, right? Look no further than Chinese auto. It is the most cutthroat competitive market in the world. Yes, there are some SOEs, yes, there were a lot of subsidies, but actually it’s extremely competitive, which is why prices are falling so fast and which is why they’re out-innovating any of the traditional auto OEMs in the world.

10.

We finally made it to chips! Artificial intelligence, of course, was the major theme of 2025, a watershed year when AI diffused into more and more workflows and it became clear that everything is going to change in the years ahead. But how will humans co-exist with AI? That’s the question Jacob Ward explores in his work, including his book “The Loop.” Here we talk about the cognitive harms of AI and surveillance, and how those harms might be ameliorated.

Why AI safety is like a bolt in a croissant

Once upon a time, if you asked your average cigarette smoker in 1957, “do you like cigarettes? Do you want to keep going with the cigarettes?” They’d say, “Yeah, it’s a positive refresher. I love this cigarette. This is a fantastic experience.” The enthusiasm we will have as people for the experience of the product is not going to be, I think, the right measure of whether that product is okay to be selling to people.

11.

Privacy is a major challenge on the web, and it’s not getting any better with AI. But that doesn’t mean smart builders and hackers aren’t trying to improve the status quo. To close us out for 2025, here’s famed internet researcher Renée DiResta talking about the 2026 midterms and what new tech like generative AI will mean for the future of elections.

How can we make the internet fun again?

My friend Katie Harpeth has this phrase, panic responsibly. You don’t want to say, “Oh, the sky is falling and everything’s going to be terrible.” I think this was for the 2024 election. I think it was important to say, these are the risks. This is what could happen. Recognizing that this could happen, how do we think about mitigating it if it does, or making the public aware that there’s potential there? I think that’s responsible risk management. Let’s just educate as many people as we can about this.

Well, from the economics of Europe and China, to the security threats of the gig economy and digital authoritarianism, to the next generation of hybrid war and onwards to chips, AI and the future of the internet, we had a whirlwind of topics to discuss on the Riskgaming podcast in 2025. I am sure 2026 will be so quiet that we won’t know what to talk about. For now though, enjoy the holidays, and listen again soon. I’m Danny Crichton, and my f’ing god, we’re done with another season.

Well, there is, at least in theory. A coalition of software and tech luminaries, joined by hundreds of supporters, recently launched the Resonant Computing Manifesto. They want software that is private, dedicated, plural, adaptable and prosocial — the antipode of the offerings available to us today. It’s a fresh vision, one desperately needed as LLMs rapidly democratize software engineering to everyone.

The Orthogonal Bet host Sam Arbesman and I jointly host this special episode with Alex Komoroske, founder of Common Tools, which dubs itself a new fabric for computing.

The three of us talk about the manifesto, how LLMs are changing software design, the same-origin paradigm, fully homomorphic encryption, remote attestation, and whether it is possible for software to be good and also be profitable.

This interview has been edited for length and clarity. For the full episode, please visit our podcast.

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Danny Crichton:

You have a new manifesto out on resonant computing. So for those who haven’t read it, I would love a primer on what resonant computing is and how the manifesto came together.

Alex Komoroske:

The core idea is that hollow things leave us feeling regret, and resonant things leave us feeling nourished. Superficially, they look very similar, but they’re fundamentally different. The tech industry is really good at providing and creating things that look good but are hollow inside — and large language models will take that an order of magnitude beyond what it’s ever been before. So it’s more important than ever before for us to make sure that the computing experiences we’re using are resonant. There are five specific qualities of resonant computing.

One is that they’re private, that you are the steward of your own data, and it’s used in ways that align with intentions and expectations.

Two, that it’s dedicated. It has no conflict of interest. It acts as an extension of your agency as opposed to somebody else’s agency.

Three, it’s plural. There isn’t one centralized power. It’s a lot of diffuse federated power structures.

Four, it’s adaptable. It’s something that lifts you up, it doesn’t box you in. It’s not like, oh, well, some product manager said I can only have these five options. No, it should be something that’s open-ended.

And five, it’s pro-social. It’s something that helps bring you in harmony with the world around you. It brings you into the world as opposed to allowing you to retreat from the world.

Danny Crichton:

When we think about where the technology industry’s been over the last 20 years, I can see a lot of platforms, experiences and apps that are in opposition to resonant computing. One of the challenges when I think about pro-social tech is, well, social media. People would say, “Well, social media was pro-social. It was designed to connect people.” It just didn’t really work out that way.

So how do you create a distinction between the past — at least the things people have been trying to build — and where you want the industry to go now?

Alex Komoroske:

I don’t know about you, but I joined the tech industry because of the hacker ethic. In the last decade or so, though, as the tech industry has consolidated, it’s gotten to this late-stage thing where all consumer minutes are sliced up between five or so aggregators. We’ve gotten to this overly optimized phase, and everything is hollowed out.

Much of the tech industry assumes that software is expensive to write and cheap to run. LLMs undermine both of those, it’s now possible to write shitty software for basically free but running it isn’t.

It’s not just about tech. I think it is also true in politics. And in business. We’re so focused on optimization that we’re inadvertently hollowing out the thing we’re optimizing. And so, to me, resonant computing feels less like a new thing and more of a reminder of when we did technology differently.

And now is the right time. Large language models undermine a lot of assumptions that are baked into the current tech industry. Much of the tech industry assumes that software is expensive to write and cheap to run. LLMs undermine both of those, it’s now possible to write shitty software for basically free but running it isn’t.

And that is very destabilizing for the tech industry. So, if we’re going to destabilize, what are the principles we want to come back to? What are the seedlings we want to grow?

Danny Crichton:

Tim Wu just launched his new book, The Age of Extraction, talking about how platforms are increasingly taking more of what we might dub in economic terms, “consumer surplus,” and trying to put it on the production side of the equation. There’s this very broad intellectual banner that says like, “Look, we’ve lost control over our computers. We’ve lost control of the customization.” We used to have HyperCard and you could do really fun, interesting things. You had this open sandbox where even as a nine-year-old, you could make little games and it was super exciting.

Now it’s locked down. I can’t even build my own computer, so to speak, as the parts aren’t even available. RAM prices are triple the cost. I’m fighting with Nvidia to get access to a chip. The forces arrayed against you are the richest, most powerful, most influential, most popular companies in the world. What do we do?

Alex Komoroske:

There’s a dozen or so of us who have been working on this, Sam here is one of them. But Danny, two things to your point. One, I’m so grateful we have large language models available as APIs, and we have multiple options. You could imagine a world where ChatGPT went big before OpenAI ever made one of their completion APIs public. You can imagine a world where they go, “Oh, shit, I’m not going to give away my primary thing.” And then, you can imagine Google and Anthropic being like, “Oh, it’s too dangerous to expose this to anybody else or whatever.” And that would be a very different world than we are today.

But we’ve got multiple providers, and they are all competing on cost and quality. There’s a number of amazing open-source models that keep on nipping at their heels, and at some point will surpass them. And that’s great, because it is destabilizing current power structures.

The second thing I’d say is this: I’m obsessed with the “same-origin paradigm,” which is the laws of physics that describe how our software, especially the web and apps, work. I think somewhat surprisingly, the same-origin paradigm, this fateful decision to slice up different origins into silos and then give the owner of that origin — the creator of that app, that software — full control over that data, was a very fateful decision in 1994.

It helped lead to significant centralization and aggregation. A lot of things we’ve seen in the tech industry are all downstream of that decision. To me, if we were able to transcend that model, add in new or alternate models that work for different use cases that otherwise aren’t supported, combined with LLMs — wow, that could really catalyze something big.

Danny Crichton:

One of the big things people talk about is no one wants to run their own server. No one wants to run their own VPN. Not just because it’s complicated, but also because of the knowledge required to be your own sysadmin.

Isn’t that why a lot of these centralized services start? You have an app, it is home-baked. It is literally running on ramen for the first week or two. There are eight users. And then, it scales up because it’s successful. It gets up to hundreds of millions and a billion people.

In his book Enshittification, Cory Doctorow really emphasizes this evolutionary challenge: platforms always start by trying to give as much of the users as possible. Then, there’s some sort of pivot point where they either want to get to profitability or they just want to extract more money.

Alex Komoroske:

Yeah, I wrote a note to myself that said the SaaS business model is downstream of convincing you to accumulate your data on someone else’s turf, and then they rent access back to you in perpetuity to your own data.

You could argue no one wants to be their own sysadmin, but we’ve combined not being a sysadmin with not owning your data. And that is what leads to this downside. Have we talked about confidential computing? I forget.

Danny Crichton:

Not on this podcast.

Alex Komoroske:

Nobody knows about confidential computing, and it is wild, because it can do things. It changes fundamental assumptions about where control can live — and who owns what. Confidential computing is hardware support basically baked into all the chips that have been deployed to public clouds in the last few years, including H100s. Now, if you run it in confidential compute mode, no one — even those with physical access to the machine — can peek inside, and that’s awesome.

The reason you haven’t heard about this before is because it’s really a tail need for the vast majority of things. If I’m running some dinky web service, and I’m running it in Google Cloud or whatever, I already assume that the Google SREs are not peeking into my thing. It’s against their terms of service, and also it’s actually quite difficult to do. The people who use confidential compute are primarily defense contractors running secret workloads or people in the finance industry doing highly sensitive calculations.

But, one of the things confidential compute can do as a party trick is actually the most interesting component: remote attestation. There’s functionality built in, where each chip has a private key that’s derived from Intel’s root key that’s burned into the silicon and impossible to remove unless you destroy the silicon. That key can then be used to sign an attestation that says the bearer of this attestation was indeed running in confidential compute mode. And here is the fingerprint, here’s the shot of the VM that was running.

The key test is, as you look more closely, does it work? Does it impress you? Is it something you like more the closer you look, or do you like it less? A resonant thing is fractally aligned.

You can pass this to somebody else across the network, and they can verify, “Oh, this is definitely signed by Intel. It’s definitely running in confidential compute mode. And it’s definitely running this software, bit-for-bit.” I’m simplifying, but this allows you to have a very high degree of confidence that your cloud provider is indeed running precisely the software they say they’re running.

This, in turn, allows you to construct a system you might call a “private cloud enclave” that allows you to have your turf in somebody else’s area. It’s like having an embassy. Technically, someone could break in, but that would be an act of war.

So this allows you to get the benefits of cloud computing with somebody else as sysadmin, but it’s still on your turf.

Danny Crichton:

I want to get to another piece of resonant computing: the qualitative factor. What makes something feel like I have expressive control over it, that I am the one who is the master of the piece of the software as opposed to the software disciplining me. How do you define what that would feel like? What kind of software do you think inspires you that way?

Alex Komoroske:

I think the key test is, as you look more closely, does it work? Does it impress you? Is it something you like more the closer you look, or do you like it less? A resonant thing is fractally aligned. Every layer, the more you peel back, the more you go, “Ooh, I didn’t even think to care about that, but I like that. “ Whereas hollow things, you pull back and you’re like … “Oh, wait, they can send arbitrary firmware updates, and it’s got a microphone and the terms of service say they can send it to any of their partners?”

Samuel Arbesman:

Danny, you were asking earlier about the pro-social nature of social media. Yeah, they were designed to be pro-social, but that’s very much on a single level. It’s like the first order of thinking. Once you get to the second, third order of thinking, you realize these things are not pro-social at all. They’re actively against empowering and improving social interactions. That goes along with this fractal thing.

Danny Crichton:

Apple is famously very expressive. But Apple would tell you, “Look, the only way to create a really expressive device that allows you to feel like its yours, it is private, et cetera, is we have to own it vertically integrated top to bottom. We own it all the way down to the chip at this point.”

Yet you have this value in the manifesto of pluralism, which sounds really great, but competition is in many ways what triggers some of the negative effects where people are trying to grab more of the surplus value of these products and platforms.

I’m curious: When you think about competition and vertical integration, is it possible to allow all these different layers to be plural, or does it need to be vertically integrated?

Alex Komoroske:

I think it has to be vertically integrated. You make a really good point about pluralism requiring competition in some form. I would argue that Apple fails the test precisely because they are not plural on these things. If you told people when the iPhone was announced that this was going to become the single most important computing device of all time, and it was going to be the vast majority of your computing, the idea that one company — which by the way, has consistently put a pretty heavy finger on what things they allow on that device — owns it would be insane. That’s an insane thing for us to allow as a society. iPhones are great. And also, the idea that one company that is very jealous gets to decide what goes on the iPhone is wild to me.

Samuel Arbesman:

Just taking a step back in terms of thinking about pluralism, we talk about this idea that a healthy ecosystem requires lots of different choices and things like that. Putting on my lapsed evolutionary biologist hat, healthy ecosystems are not just imposed or designed. They evolve over time into a thing that becomes healthy.

And I feel like that’s the argument being made: Yes, on the one hand, maybe some company can design a perfectly resonant experience in this vertically integrated way, but more likely than not, the resonant experiences we want in the tech world are going to evolve from the bottom up. And that’s why this plural principle is so important, because you need to almost cobble it together.

Danny Crichton:

Let’s project forward five to ten years. Your resonant computing manifesto has gone absolutely bonkers. Everyone has signed onto it. What does that world look like? What would companies be doing differently in terms of designing software, in terms of how they’re giving it to folks, in terms of the collaboration that’s built around it? What does it feel like?

Alex Komoroske:

My mental model is this: software today feels like a thing you go get at the big box store. You choose which of three basically similar options to buy, and they all suck. What if software felt instead like a thing that grew in your personal garden, something that was nourishing, that was specific to you. Your data comes alive in ways that help you accomplish the things you care about.

In that world, software doesn’t look like it looks today. Software does not look like having silos that do one particular task. Clay Shirky has this essay from, I think 2004, 2007 called “Situated Software.” Situated software is software that’s highly situated to a specific context.

To anybody else who looks at a piece of a spreadsheet you’ve modified, that’s situated software. It’s highly situated to your particular need. And if you show it to anybody else, they go, “That’s a piece of shit. It barely works. It’s ugly, it’s insecure.” But, to the person who made it, it’s perfect, that’s exactly what they need.

I think situated software, that’s what it will feel like in the future.

To me, this is totally compatible with business and business goals — products that are resonant are ones people love using, and they evangelize them, and they feel good about them.

Samuel Arbesman:

Just adding on to what Alex was saying: there’s this blending of consumer and creator. There doesn’t need to be a single place where there are people producing software, and then people just consuming it. In some ways, it’s a return to the earlier days of computing, which unfortunately was only available for a very small set of people.

My family’s first computer was the Commodore VIC-20. And during that time, one of the ways you got software was you would go to a magazine, and find these type-in programs where you literally had just pages of text and typed them in. And you could see this very clear relationship between the text you were typing in and the result on the machine. Sometimes it worked, sometimes it didn’t.

But that blending and blurring of creator and consumer was only open to a very small subset of people. And hopefully, that kind of thing will now be available to everyone.

Danny Crichton:

I think this works within capitalism. The biggest challenge right now is people are worried they can’t make money. They have to own more. They have to control more.

We all know how there’s no sustainability to open-source software because there’s no money. But I do think there’s a world where you can align the value that’s created — that still makes plenty of money for all the companies. It’s very much in line with the resonant computing mission. You’re also seeing this with the fatigue a lot of folks have with the systems and apps they use every day right now. They’re not being nourished. They’re getting tired of it. You see how many people are quitting various social-media platforms.

And so, I do think that there is a world here. It requires imagination and vision from a lot of leaders, which you have brought quite a few around the table.

Alex Komoroske:

Yeah, I agree. To me, this is totally compatible with business goals — products that are resonant are ones people love using, and they evangelize them, and they feel good about them. When you take a long enough time horizon, and have a broad enough perspective, resonant products are simply better products. They’re better from a business perspective, for shareholders, and what have you.

I have another essay about the optimization ratchet. We didn’t necessarily choose to get so hyper-focused on optimization. It’s just that as you get more efficient, the benefits of optimizing are concrete, short-term and very visible. The downside of optimization is that you’re losing adaptive capacity, you’re losing resonance. But what you’re losing is not nearly as obvious. And so, with each of these micro decisions, the universe tilts towards optimization, and you do more of it.

But you have to know that there’s something on the other side of the balance scale.

Samuel Arbesman:

As people see all these things in modern society, especially in the tech world, they’re very unhappy, they’re burned out with everything in big tech. In the absence of realizing that there’s another path forward, you just see technology as the problem. But it turns out it’s not. There are just certain ways of building technological systems and software that are very problematic. But, if you can provide a name and a framework for this other path, it shows that it needn’t be the default.

Don’t miss

The inside story of the billionaires fighting for space

Why AI safety is like a bolt in a croissant

How to be a polymath

All of this is ably documented in Christian Davenport’s new book, Rocket Dreams: Musk, Bezos, and the Inside Story of the New, Trillion-Dollar Space Race. A follow up from his massively popular book The Space Barons, the book chronicles the last seven years of the new space race with alacrity and intimate details.

Christian and I talk about how he transitioned from covering Afghanistan and America’s home front in the War on Terror to covering the crazy billionaires at the heart of the space race. Then they talk about Musk’s fight over the United Launch Alliance, why Blue Origin has been taking on more risks in recent years, NASA and the government’s growing dependency on private companies for manned spaceflight, what the next decade of the space economy will look like, and finally, how China is entering the picture in a frenetic way.

This interview has been edited for length and clarity. For the full episode please visit our podcast.

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Danny Crichton:

Christian, you just published a new book, Rocket Dreams, focused on Elon Musk, Jeff Bezos, and their billionaire race for the stars. Tell me how you got into the space world.

Christian Davenport:

I was at the Washington Post during 9/11, and at the time, I was a metro reporter covering a suburb of Washington DC. A lot of the people in the communities I was covering were being called up to go over and fight in Iraq and Afghanistan.

I like to say I was the first reporter to be embedded on the home front. If you’re a reserve component, you don’t go back to a huge military base where you’re surrounded with people like you, you take off your uniform and you go back to civilian life. I wrote a lot about that. But then, in 2014, I got assigned to cover this press conference at the National Press Club because some guy named Elon Musk was suing the Pentagon for the right to be able to launch national security payloads — national security satellites for the government — on his rocket. I don’t think I really knew who Elon was at the time.

Elon was filing a lawsuit against the Pentagon even as he’s trying to get the Pentagon to be his customer.

Danny Crichton:

Yeah, he had a much smaller profile back then for sure.

Christian Davenport:

Right. The whole premise of the news conference was a bit insane. He was filing a lawsuit against the Pentagon even as he’s trying to get the Pentagon to be his customer. He wants to work for them, and the way he’s trying to do that is to file a lawsuit.

But at the time, a company called ULA, the United Launch Alliance, which is made up of Lockheed Martin and Boeing, had this monopoly on all national security launches and were just bringing in billions upon billions of dollars and charging the government a lot of money for that service. Elon’s point was, “Don’t give me the contract, but at least allow me or others to compete for those contracts.”

I left the press conference and I wrote my story, but then I started researching SpaceX and re-usable rockets and Elon and what they were doing and ultimately decided we needed to be paying attention to it.

Danny Crichton:

Your book centers on the competition between two big billionaires, and that’s the crux of the story. But there’s also this massive legacy of NASA going back to the 1960s and the Apollo program and everything else up to the present day. I’m curious how you balance between the government and NASA’s public mission versus that ego-driven ambition that’s coming from the private sector?

Christian Davenport:

That tension is at the heart of the book. And it’s growing and evolving. It’s a push-pull, because governments have had exclusive dominion over human space flight and space exploration for decades — since the dawn of the space age. Yeah, they’ve always used contractors, even going back to Mercury, Gemini and Apollo. But now, you’re seeing this erosion, where the private sector is going in and taking parts. In part, that’s the way NASA designed it. That’s the way the government designed it. They decided to cede some of these responsibilities to the private sector.

If you’re a young engineer today, are you going to go work at NASA or are you going to go work at SpaceX?

You have it starting from outsourcing missions like flying cargo and supplies to the International Space Station, which evolved into flying astronauts to the International Space Station, which has now evolved to the flagship program, Artemis. We’ve outsourced the spacecraft that’s going to land astronauts on the moon to the private space sector, outsourced the spacesuits. Those are going to be built by the private sector. The rovers, some of the technologies for power generation, all these sorts of things are being outsourced.

So what is the government’s role in this? It’s setting the direction, having oversight of these companies. But I think you’re seeing a huge effect, where so much of the talent and expertise has gone into the private sector. If you’re a young engineer today, are you going to go work at NASA or are you going to go work at SpaceX?

Danny Crichton:

Right. We see this in our own portfolio. We have a bunch of space tech companies and many of them are alums of SpaceX. Theoretically, in a different generation, they would have been at NASA. Instead, they’re building rockets, they’re optimizing the fuel systems on these vessels, and they’re very effective at it. On one hand, they’re very public-minded and they really do believe in these missions. On the other, the economics, as we all know, are really tough, especially when the government shuts down, NASA is adrift with an administrator coming back and forth.

Most of Rocket Dream is focused on two companies: SpaceX and Blue Origin. SpaceX always gets 97% of the coverage. Part of that is Elon Musk, who is just a larger-than-life figure. But we just had a big breakthrough with Blue Origin a few weeks ago, where it had the successful orbital launch and booster landing of its powerful New Glenn rocket, carrying NASA’s ESCAPADE Mars mission and it landed the reusable first stage of the rocket on a drone ship.

In your book, you describe SpaceX as very ambitious, very experimental. Blue Origin has been this middle ground. They’re not plotting, they’re trying to slowly build up capability, they’re much less flashy, a little bit more grounded. Did the news this last week from Blue Origin fit with that narrative?

Christian Davenport:

That’s a great question. I should be thankful to Blue Origin for landing the booster, because actually, I think the book sets it up perfectly, so it’s great timing.

Blue Origin just has not been as successful. But in the last two years, you really have seen a change in their metabolism and, I think, risk tolerance.

You’re right, SpaceX gets 97% of the coverage, because they do 97% of the work. They’re launching a rocket every two days. They’re the ones winning the contracts, from the Pentagon and NASA, they’re really doing it.

Blue just has not been as successful. But in the last two years, you really have seen a change in their metabolism and, I think, risk tolerance. There were several inflection points. One came in 2016, when Jeff Bezos was saying to his team, “They’re pulling ahead of us, SpaceX. We need to catch up and we need to go after everything that they go after,” and he urges his team to do it. They were supposed to win this big moon-lander competition, but they ended up losing.

And that leads to the second inflection point. Remember, for Jeff Bezos, there are a few failures in his career at Amazon here and there, but nothing like this. No defeats like losing this contract to SpaceX. They just change, I think, as a company and they say, “Let’s forget NASA. We tried bidding and offering NASA the solution we thought they wanted, but now, we’re going to build what we want and we’re going to innovate and we’re going to come up with something completely different.”

The architecture for their moon-lander today uses all these innovative new technologies. It’s essentially a reusable spacecraft that stays in orbit around the moon and then a service spacecraft goes back in between the Earth and the moon to service it, to refuel it. The barrier to entry for that is so difficult. It’s so much more risky. But it’s also the SpaceX way.

Danny Crichton:

When you think about the venture capital industry, we generally try to invest in companies we’d want to exit in a decade — that’s the lifecycle of a fund. One of the interesting things here is that these are old companies from a Silicon Valley perspective. Blue Origin is actually older than SpaceX. It started in 2000. SpaceX, I think, was about 2002, 2003. They’re almost entering their third decade, and you have two CEOs who have many other things going on.

How much does Elon and Jeff’s attention drift onto and off of these companies, and how does that change how those companies function?

Christian Davenport:

I think you see it perhaps more at Blue Origin than at SpaceX. Famously, Jeff was working at Blue Origin on Wednesdays and one or two Saturdays a month. He has said that since he left Amazon, Blue Origin is his top priority. He moved to Florida, probably for tax reasons, but also I think he likes being closer to their Cape Canaveral facility. He’s focusing a lot more attention on Blue.

What happens to Blue Origin when Jeff goes away, retires? Or what happens to SpaceX? Can those companies exist and thrive without them?

Elon is just Elon. He could be doing a million different things, but when he’s dialed in to SpaceX, he is dialed in. I think there was a lot of discussion, particularly around the time where he was embedded in the White House and doing DOGE, whether SpaceX was a little bit distracted.

I, frankly, heard both things. I heard that there wasn’t the focus, but on the other hand, I think a lot of the people were glad to have a little reprieve from Elon. The other thing I think about with those two is what happens to Blue Origin when Jeff goes away, retires? Or what happens to SpaceX? Can those companies exist and thrive without them?

Danny Crichton:

Jeff’s 60+ years, so still spry. But there’s this balance between their time and attention and skill and recruiting and the cachet they bring these companies, and then there’s the capital aspect: they’re putting their money in, they’re putting their dollars to work.

Christian Davenport:

You’re exactly right. Blue Origin has existed essentially like a nonprofit with one benefactor, and that’s Jeff Bezos. He’s said that, every year, he cashes out a billion dollars in Amazon stock and plows it into Blue Origin. Elon funded SpaceX to the tune of about a hundred million dollars early on, but it’s been able to exist and thrive with government contracts and leveraging that alongside private investment to build the Falcon 9, build Dragon Spacecraft, and use those vehicles to then go into commercial markets and build another line of business.

That’s a very good business; it could have survived for a long time doing that. But Elon disrupts himself. Enter Starlink. If SpaceX moves from being a rocket company to becoming an internet provider and a satellite provider, then the upside could be 10x, or maybe potentially even more. They’ve launched 10,000 satellites and have 8,000 operating, but now they’ve got a satellite production line, where you can change the guts of the satellite. It doesn’t have to be internet and communications. It can be who knows what? Remote sensing? Spy cameras?

We know the National Reconnaissance Office has a contract with SpaceX to use that satellite line, whereas Blue Origin is just beginning to do that. That launch the other day, that was a launch for NASA. They have a lot more lined up, but in terms of the upside that Elon and SpaceX have, there’s no comparison.

Danny Crichton:

Two years ago, Ronan Farrow had a piece in The New Yorker. Basically, the U.S. government used to have the unique capability to put astronauts in space. Now it’s just becoming the strategic setter of the country’s direction as opposed to actually owning the operations.

Up until recently, that has basically been a monopoly on the SpaceX side. They are the only ones with the capability. Increasingly, and this was the Farrow point, SpaceX has the ability to just say no. And if it says no, so to speak, the government doesn’t really have an alternative way of solving some of these sorts of problems.

Does Blue Origin catch up and say, “Look, now there’s an oligopoly. We can form at least a duopoly, which will put pressure on SpaceX to reengage”?

Christian Davenport:

I think NASA’s idea, from the very beginning, was that if space was going to go commercial, the United States would need a growing commercial space industry. And so, the question was: Do we have a commercial space industry or do we have SpaceX? For a long time, we’ve just had SpaceX.

But now, you’ve got so much of the national space enterprise in the hands of one company, essentially one person, it’s not healthy. You saw perhaps one of the most dramatic illustrations when Elon and President Trump had their little spat. Elon threatened to take the Dragon Spacecraft away from NASA, and that’s the only way NASA can fly its astronauts anywhere.

A lot of people thought it was reckless and dangerous, and it sent off alarm bells. I know people at NASA and the government were reaching out to companies like Blue Origin, Rocket Lab, Relativity, Stoke Space, and other providers to say, like, “You guys got to hurry up.” And I think the government will bend over backwards to bring those companies in. Even if they’re not the lowest bidder, just by virtue of not being SpaceX, they’ll be able to get a foothold.

Danny Crichton:

One of the big concerns in American politics and the economy is short-termism. We’re always on this quarterly earning cycle. Yet we’re talking about companies that have been around for several decades and two individuals in particular who seem to have extensive long-termism around solving these problems. Why so much long-termism here? What sustains them on this compared to so many others?

Christian Davenport:

For Jeff, it’s a very important mantra and very key to understanding how he thinks. He’s got a thousand-year clock that chimes like once every hundred years or whatever it is, which is a symbol for long-term thinking and solving problems. Space is just hard. The barriers to entry are extreme, but they both think it’s really important, and they’re thinking about it in terms of what their legacy for their life is going to be?

There’s also this tit-for-tat and the competition between them and the rivalry, but the fact of the matter is they’re actually very similar in their thinking about space, and their long-term thinking about space and lowering the cost and increasing access.

As Jeff said, when he founded Amazon, you could found an internet company in your dorm room, because you had the infrastructure. Phone companies had laid down the cables for the internet. The Post Office could deliver books. An invention called the credit card that could take people’s money. He could build a business on that infrastructure.

But if you want to build a business in space, the infrastructure isn’t there. It is starting to get there, but I think that’s why they talk about the long-term vision and building that infrastructure. And then, once you can access space frequently, what does that open up? What about data centers in space? What about space-based solar power? Commercial space stations? Mining celestial bodies?

In space, we can explore. But when do we change from exploration to expansion, to holding the territory and commercial space station?

Danny Crichton:

Even on our side, we’ve led a $25 million series A into a company called Reflect Orbital, which is putting space mirrors into orbit. Five or ten years ago, this would have been ridiculous. Now, with the advent of Starship, hopefully in the next cycle here, the economics of space — the economics of payloads — change. This is the bet we’re making in the company and the team. On the other hand, I’m still not a complete believer of data centers in space. Chips heat up, and it doesn’t seem like we’ve found any solution to that.

To your point though, in 2025, what I am seeing is that we’re pivoting from this world of how to get stuff up. That’s increasingly a solved problem. The next 10 years to me is like, “Well, what do we do with this?” You’ve done two of these books covering basically a 20-year period of space flight and commercial space flight history. Is there a third book coming in 10 years? What do you predict is going to happen?

Christian Davenport:

I’m thinking about this a lot, and I do think of it in terms of a series. The analogy that I use is, westward expansion and the pioneers. I think we’re in the covered wagon stages. We’re going, we’re accessing the territory, we can get there, but we’re moving into homesteading. As a Space Force general put it to me, we spent the first 50–60 years of the space age trying to get to space. Now, we focus on getting through space, moving through space, maneuvering, staying there, changing orbits, traveling, refueling, creating businesses up there and doing all that.

I think once we get to that point, there will be a book there, many books there, about the homesteading aspect of it. That’s how I see it.

I was talking about my thesis with someone who’s much smarter than me and they said, “No, no, no. Before you get to homesteading, you have to have the cavalry.” You need the rules of the road. I’ve been thinking a lot about national security. A lot of people think Space Force is a joke. It is not. The threats from China and Russia and others are real. They can hold our assets in space at risk. What we have up there — GPS, just for an example — is incredibly important, and not just for Uber Eats, DoorDash and Waze. The timing signal on a GPS satellite is used for every ATM transaction, every trade on the New York Stock Exchange.

Those satellites, the 31 of them, 34 of them… they are vulnerable. China, Russia, North Korea and others have shown they can take them out. That’s a real risk. Anyway, that’s what I’ve been thinking about.

Danny Crichton:

There’s also the cyber aspect. These are digital systems, they’re hackable. In many cases, they’re not easy to patch. And so, once a nation-state actor investigates and identifies a vulnerability, it’s not necessarily easy to close.

And also, with all these thousands of objects in orbit, there’s the issue of space debris. I think it was an MIT study that came out earlier this week that said there will be so much space debris coming back down to Earth, that one plane every decade will be hit and blown up, and we have no solution for this problem whatsoever.

So to your point, there’s a huge amount of stuff here that has come together all of a sudden that didn’t exist 5, 10 years ago.

Christian Davenport:

Yeah, and the regulations, as you pointed out, are just not there. When you talk about space debris, the U.S. government can track it and they can tell satellite operator A, like, “Hey, you may collide with satellite B,” but it can’t order them to maneuver. Some of them can’t even maneuver. We just saw a Chinese spacecraft apparently hit with a piece of debris. I joke that if the problem of space debris gets really, really bad, then it’ll be like a moat protecting Earth from any incoming aliens.

But in general, what China’s been able to do in space is remarkable. There is no differentiation between their civil space program and their national security space program. It’s not like in the United States. Really, China’s space program is run by the military. And what you are seeing now, by the way, is — and this is a Space Forces term — Chinese and American satellites “dogfighting.” Not over a 10- or 20-minute period, this is days, even weeks, where they are flying by at very close proximities, which in space is miles apart, but doing it in a way where they gain tactical advantage.

Jeff says, “Oh, you want to know what Mars is like? Go live on the peak of Mount Everest.” It’s actually like living on the peak of Mount Everest in Chernobyl.

Danny Crichton:

You titled your book “Rocket Dreams,” and I want to emphasize the word “dreams.” NASA, the intelligence community and the Pentagon — one of the reasons these were all separated is they had different dreams. NASA was focused on scientific exploration, the Pentagon focused on strategic and tactical advantage. You have space races between countries, where they are trying to gain advantages over each other. You have billionaires who are competing on ego or from a commercial perspective. There are so many different types of dreams.

But to me, there’s this balance between dreams like “Hey, we’re going to have space stations in orbit” — those already exist, it’s just scaling up — versus that long-term dream of going to Mars. We may never achieve it. Which one do you think is motivating folks better?

Christian Davenport:

On the Mars side, that’s part of what Elon does, and he’s good at it. He’s trying to make space cool again and driving this vision. That’s why he cares about aesthetics. That’s why he cares about the space suit looking cool. The spaceship has to look cool because he wants public support.

But the fact of the matter is this, as someone said to me: Imagine the first human landing on Mars, and imagine it goes perfectly. Seven minutes of terror, entry, descent and landing — this moment of absolute triumph is also a moment of pure peril in its crisis mode from day one, because Mars is just so difficult.

Jeff says, “Oh, you want to know what Mars is like? Go live on the peak of Mount Everest.” It’s actually like living on the peak of Mount Everest in Chernobyl. It’s not a pleasant place to go. I think it would be astounding.

We romanticize it so much. But spaceflight is really, really hard. It’s incredibly dangerous. To think that we’re going to go to Mars and not kill a lot of people, I think it’s foolish. I think this vision of having big space stations in orbit, where you can come back to Earth, I think that’s a long way off, but it seems potentially more suitable for human beings.

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Jacob Ward — the former editor of Popular Science, long-time tech correspondent, podcast host of “Rip Current” and the author of the popular book The Loop — is skeptical. Via his own personal experiences and reporting, he sees AI’s addictive qualities and its lack of safety as a serious challenge for regulators and society as a whole. He analogizes this challenge with the cultures of software and hardware engineers, where software is about “if we ship, then we’re going to sort it out” and hardware is about how “scale compounds your problems.”

We talk about biases and decision-making, the connections between AI and casino gambling, why LLMs are like experimenting on people in the wild, how to think about regulating edge cases, ex-anti legal frameworks, Nita Farahany’s idea of cognitive liberty and why product enthusiasm is not a substitute for safety.

This conversation has been edited for length and clarity. For the full version, please subscribe to our podcast.

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Danny Crichton:

You’ve described your work as the “Black Mirror” of Thinking, Fast and Slow. So for listeners who haven’t read your book, The Loop, or know your body of work, talk a little bit about your main thesis.

Jacob Ward:

Sure. I was in a documentary production PBS did. We got a couple million dollars from the National Science Foundation to create a kind of crash course on Daniel Kahneman’s work and the constellation of research that came out of it. That show was called “Hacking Your Mind.” And for me, it was a totally life-changing experience. Not only were we meeting all of these incredibly important thinkers in behavioral science, but I was also kind of a guinea pig.

I was the prototypical person who had no idea how automatic my thinking was.

The conceit of the show was here’s a guy who thinks he’s immune to all of this stuff, right? He makes his own choices and has no biases. And then they subjected me to test after test after test, and I did exactly as anybody else would have done. I was the prototypical person who had no idea how automatic my thinking was. I realized how much of it was out of my control and based on these heuristics that Kahneman and others had identified.

At the same time, I was also a technology correspondent for Al Jazeera and at NBC. In my day job as a tech correspondent, I was bumping into company after company after company using these pre-transformer models, ML and neural network, human-reinforced-learning systems to try to identify behavior in people and predict (and sometimes influence) their choices. Some companies were trying to help people lose weight or save money — there were some positive paternalistic ideas there. But in many cases, I was finding people who were working for big gambling companies and trying to shape people’s behavior that way.

As I learned more and more about transformer models — which then came along and made the current LLM language model possible — I suddenly realized, “Oh, we’re about to enter a world in which you have all of these behavioral science findings creating a kind of manual for how people make choices.” And then you also have a whole world of pattern recognition systems that can pick apart that data in ways that human researchers would never be able to and come to predictive conclusions about how we’re going to behave.

And if I knew anything from 20 years as a correspondent studying how businesses make choices, it was that people with the best of intentions wind up preying on human vulnerabilities if they start to run out of funding and need to come to a minimally viable product as quickly as they can.

People with the best of intentions wind up preying on human vulnerabilities if they start to run out of funding.

Suddenly I was like, “Wow, this is an emergency.” Where a lot of people worry about the “Terminator” possibility, I worry about the “Idiocracy” possibility. I worry we are going to wind up amplifying our most ancient, primitive instincts and become, if we’re not careful, a more primitive version of ourselves.

Danny Crichton:

When I think about the last 15 years, I see two sides of the coin. So on one hand, you have a neuro-side focused on gamification and how you take insights from psychological behavior. For example, the idea of offering variable rewards to which humans respond very, very well. And then on the other side, about 15 years ago, Nicholas Carr was working on The Shallows. More recently, Shoshana Zuboff wrote about surveillance capitalism, and Karen Hao came out this year with Empire of AI.

So there’s two sides. One is like, “Here’s how to make money — a guide for how to do it effectively, efficiently.” And then there is an increasing number of books on the shattering of the intentional commons and the inability of folks to focus.

When you think about all this in 2025, three years after ChatGPT came out, how do you start to think about the impact that’s having?

Jacob Ward:

One of the things I’m most concerned about is that there’s some very dated assumptions about how problems with something like this get worked out. What I come back to all the time is the idea that people experimenting on other people in the wild is kind of the essence of a lot of these large language models. The companies behind them have the idea that we’re just going to get it out there. If we ship, then we’re going to sort it out. And that has to do with, I think, people running these companies who came from software. They are people who believe scale solves your problems — over time, scale will work out the bugs.

Hardware people instead will tell you that scale compounds your problems. I once interviewed the CEO of Midjourney and was asking him about some of the problems he’d been seeing on his platform, how much porn was being developed, how much violent porn was out there, all kinds of stuff. And he basically said, “Well, I take this attitude that people are generally good and we have to act on that assumption and we’ll sort of work it out over time.”

And then he said, just sort of thinking out loud, “If I’m somebody who makes muffins — I’ve made 10,000 muffins — and somebody gets food poisoning, am I supposed to stop making muffins?” And there was this awkward silence between us. I was like, “Yeah, dude. Yeah, you’re supposed to stop making muffins. That’s what the FDA would say. You’ve got to find out what’s wrong. An inspector is going to come to the facility. That’s the whole point.”

They are people who believe scale solves your problems… over time, scale will work out the bugs. Hardware people will tell you that scale compounds your problems.

I mean, I’ve gone to big industrial bakeries. There’s one here in the Bay Area called Semifreddi’s. I went on a tour of the Semifreddi’s factory. I was there for an inflation story, but at the end of the assembly line, he showed me the metal detector all the products go through. And I was like, “Metal detector? Why do you put croissants and granola through a metal detector?” He’s like, “You’ve seen all of the machinery we have here. Well, a bolt is eventually going to come loose and fall into the dough. And if even one person cracks a molar on a bolt in a croissant, then that’s the end of my business.”

So you’ve got people who are in the business of putting stuff out to 800 million people thinking edge cases are such a small percentage of the total, it doesn’t matter. But meanwhile, you’ve got people who make croissants for a living saying, “The edge case is my responsibility. It’s my fundamental responsibility to make sure that never happens.” And that disconnect for me is where I get really worried.

On Monday this week as we record this episode, OpenAI released a big report on the mental health effects we’re seeing, and they said that 0.15% of their users are developing an outsized emotional attachment to the chatbots. Similar numbers of people are developing or showing signs of psychosis and mania in their interactions with ChatGPT. And 0.07% of people are talking openly about suicide with this stuff.

You could look at that and be like, “Oh, well, that’s a tiny fraction of a percent.” But with a product that reaches 800 million weekly users, that’s 500,000 people a week exhibiting suicidal ideation. That’s more than a million people a week exhibiting signs of psychosis. We have to be thinking about the circuitry of human decision-making as if it is hardware, or at the very least we’ve got to meet the standards of food, because this stuff is really, really important. It’s going to change how we think.

Danny Crichton:

At the same time, we’re also dealing with the fact that, in these cases, we have a single piece of software with a very specific ethical, moral — we’ll even call it religious — point of view. It is Western, it’s built in Silicon Valley. It is generally imbued with the norms of the builders of these platforms, but it’s now deployed everywhere.

I wrote about this a couple of weeks ago — why doesn’t your chatbot recommend religion? It’s not going to do that because it’s been trained not to, even though maybe someone should be counseled that way. So I do think there’s this open question of ethical quandaries as you scale up.

Jacob Ward:

The new fashion for the heads of these companies is to say, “Well, now we’re just a reflection of humanity. And humanity has a bunch of problems. We can’t be expected to solve those problems.”

I think you wind up in a place where you sort of say what a lot of the social media companies say, which is, “Well, at this point, it’s gotten to be so big and unwieldy. We can’t truly take responsibility for it anymore.” The new fashion for the heads of these companies is to say, “Well, now we’re just a reflection of humanity. And humanity has a bunch of problems. We can’t be expected to solve those problems.”

But now that we’re seeing real effects, I’d like to think that attitude could change. And it’s going to be incumbent for it to change; we haven’t even begun to see how powerfully this stuff is going to seal people off socially, seal people off psychologically. It’s going to invite some new assessment of what “harm” legally means.

In this country, we only like to think about financial harm and physical harm. We regulate against money losses and death pretty well, but we don’t like to regulate anything else, and we certainly don’t like to imagine that people are anything short of entirely responsible for their choices. I’m a former drinker who, when I see “drink responsibly” on the bottom of alcohol ads, it makes me crazy. There’s no such thing. That’s why I had to quit drinking.

One thing — and I’ll be curious to hear your perspective on this Danny — is how ready these companies seem to be to own the short-term horrors that are going to be visited on people on our way to get to some utopia with AGI that’s curing cancer for us and taking us to Mars and the rest.

It’s weird to hear companies say, “Oh, there’s going to be enormous job loss, and tons of scams are going to come out of this.” It’s this weird ex-ante public relations tactic. That promise of utopia is the new excuse that, I think, people are using now to avoid responsibility for the short-term stuff. They just say, “It’s going to be worth it when we get there.” I’m curious what your perspective is on that.

Danny Crichton:

What’s interesting is that AI is the first time I’ve seen all the alignment teams and the super alignment teams with many, many people focused on this. It goes beyond PR, at least in my view. There are too many people out there who are very deeply passionate about this subject. Now, I don’t know if they’re always empowered to do what they need to do.

And unfortunately or fortunately, there’s an immense amount of competition in the AI modeling space. It feels like you’ve just got to keep running to maintain your edge. You can’t take this step back and ask, “how many jobs are going to be affected?”

I bring this up in a lot of the policy work that I do. People can transition, they can retrain, they can do a lot of stuff. We did that to some degree in manufacturing, but it was not fully complete. And that has led to populism and a lot of today’s challenges. That was a 30-year transition. This one is going to happen at a speed we’ve never seen before. 30 years will be three years. There is no world in which people can retrain that fast, and that’s what worries me — just the speed.

We’re just on the threshold of figuring out all the implications for these technologies. But I am curious about this alignment piece. It doesn’t feel like window dressing, but also doesn’t seem like anyone’s really willing to shut things down at this time.

Jacob Ward:

That’s right. I mean, I have a new book project I’m working on. The tentative title is Great Ideas We Should Not Pursue. It is a tongue-in-cheek look at points in human history in which we have had a great idea and said, “let’s not do that.” So I’ve been looking for examples of places where we’ve held back or in some way slowed down the commercialization of a thing because we thought maybe it wasn’t a good idea. It’s really hard to find examples of that!

There’s a disconnect between how powerfully this stuff is going to work on our civic lives and how little civics the people making it seem to have studied.

Jennifer Doudna and the CRISPR revolution — they signed a one-year moratorium on experimentation on human germ lines. That was one example, but that was also one where no one was beholden to financial interest. That was an innovation that came from inside public universities.

Meanwhile, I think about the disconnect between how powerfully this stuff is going to work on our civic lives and how little civics the people making it seem to have studied.

I was at a meeting once — this was pre-transformer models, or right when they were coming out — and I can’t name names, but early founders of some of the big companies were at this meeting. One of them was presenting the idea of a human-reinforcement learning program, where they were having human workers complete some sort of fill-in-the-blank sentences. And he said, “There’ll be sentences like this: ‘I would never blank with a coworker because that would be unethical,’ that kind of thing.” And after they have done enough of those, he then said, “we will arrive at a set of universal human values, and now I’ll take your questions.” And every hand in the room goes up. Some of them are trembling, they’re so outraged.

The first person was this political scientist. She says, “Okay, I actually have three questions. What’s universal? What is human? And what are values?” And the whole meeting implodes. And that was it, because he’d presented this freshman’s idea of how political science works and how morality and philosophy work with the keys to the car.

I think everyone who makes this stuff has good intentions. To my mind, there are really good long-term intentions that are easily distracted by the need to pay off vast capital expenditures. And as a result, I don’t think these companies and good intentions are going to save the day here.

Danny Crichton:

As we’re recording this today, Character.ai said that they will start to do identity checks for anyone under the age of 18. So there’s a sense that at least children should not be part of this story. I’m curious: How do you think about precautionary principles around harm?

Jacob Ward:

I’ve mostly just been thinking about the big dumb hammer of litigation — and that discovery in litigation will reveal some of the techniques being deployed and create a little bit of coalesced legislation around those. Now, I just have to always remind everybody, there’s no federal data privacy regulations in this nation. We have no rules about that stuff.

When you look at a regime like Australia’s, which is a real ex-anti legal framework, we’re going to get out in front of these harms rather than sort them out afterward. But it is so powerful and important to remember that the models are being built by that guy who was in front of that room of social scientists. This is a guy who’s trying to build into the system an ex-anti regime of values that are going to govern this stuff. Anthropic has gone on to create this constitutional AI. They have a similar notion. There’s this obvious idea that they’re going to have to pre-build some rules into this stuff.

The enthusiasm we will have as people for the experience of the product is not going to be the right measure of whether that product is okay to be selling in the first place.

Well, if the technology is going to be pre-building all this decision-making, then we have to pre-build some decision-making around the legal liability. That’s going to require real civic debate; what do we agree or disagree on about what’s right and what’s wrong? There are some really smart thinkers about this. I feel like I mention her name in every public appearance I make, but Nita Farahany is a Duke law professor who wrote a book called The Battle for Your Brain. She has this whole idea about cognitive liberty and how we need to enshrine it as a civil right. It sounds very, very gray, but she’s a sharp legal thinker who’s created a real framework for assessing this stuff. She has a whole framework for figuring out the difference between harm and manipulation — and where they overlap.

And I think we’re going to have to get into that world in the same way we eventually had to deal with cigarettes. If you asked your average cigarette smoker in 1957, “do you like cigarettes? Do you want to keep going with the cigarettes?” They’d say, “Yeah, they’re a positive refresher. This is a fantastic experience.” The enthusiasm we will have as people for the experience of the product is not going to be the right measure of whether that product is okay to be selling in the first place.

Something similar is going to have to happen here. We’re going to look at people and be like, “Oh, it’s working on your circuitry.” The loop that this has set you on is something beyond your control. It feels good to you, but that doesn’t matter because it’s causing you to make all these terrible decisions. And here are the ways in which these companies have clearly made these choices internally to pursue this kind of behavior.

Once we get to that point, you’re going to start to see big financial losses. And that’s where I think the rubber is going to meet the road in terms of people changing how they conduct business with this stuff.

Danny Crichton:

I like this idea of the lack of humanistic thought in tech circles. I’ve been in the tech industry arguably since my first startup experience in 2007, so almost 20 years.

I do think people had a much more well-rounded education in an earlier generation. One argument would be, particularly in AI, people had to go deep into their degrees. They had to get a master’s degree.

When did you have a chance to walk over to the English department and read literature and understand the human condition? So I wonder how you’re thinking about bringing that back into the equation.

Jacob Ward:

Man, I wish I knew the answer to this question. I’ve been in these weird conversations with people, where I sort of want to say, “Hey, bring me on as an advisor. I can help you.” I’ve had a couple of companies come to me and say, “Hey, I want to do this the right way. I’ve read your book and other books like yours, and I’d like to try and be on the right side of history.” I’ve had a couple conversations like that, but I really feel like that hydrophobic sand that you put it in water and it can’t get wet. I feel like that around here.

But I like to assume, as the people of the generation you’re describing have kids, they’ll sort of look at the world that their creation makes possible in a new way. I mean, all great breakthrough thinking about how humans evolved, for instance, has to do with long-term generational thinking. So we have to get into long-term generational thinking.

We’re not built to think beyond the circle of our campfire. And that’s where some of our higher functioning needs to come into play. But it doesn’t feel good, and it doesn’t make you money in the short term.

Unfortunately, that’s not the way we are incentivized right now. It’s not even how our circuitry is supposed to work. I got to spend some time with a tribe in Tanzania. They’re one of the last remaining tribes on earth that lives the way we all did 60,000 years ago. They have no last names, they have no property, there’s no marriage, and they are nomadic. They don’t raise crops or anything.

And one of the most amazing things about them is that they have no word for any number beyond five, because why would you need that? In the way that our brains are built, you don’t need to be thinking more than five moons in the future. You don’t need to count the number of people in a group. Once it’s more than five, you can just say, “It’s lots of people.” There’s three pieces of meat or there’s enough for everybody.

And this is the whole thesis of my book — we’re not built to think beyond the circle of our campfire. And that’s where some of our higher functioning needs to come into play. But it doesn’t feel good, and it doesn’t make you money in the short term. And so that’s the essential problem I think we’re going to be facing in this generation and the next.

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