Latest Posts (20 found)
Stratechery 2 days ago

2026.32: Earnings and Learnings

Welcome back to This Week in Stratechery! As a reminder, each week, every Friday, we’re sending out this overview of content in the Stratechery bundle; highlighted links are free for everyone . Additionally, you have complete control over what we send to you. If you don’t want to receive This Week in Stratechery emails (there is no podcast), please uncheck the box in your delivery settings . On that note, here were a few of our favorites this week. This week’s Stratechery video is on Who’s Afraid of Chinese Models? . Earnings Exposure. From a content perspective, earnings can be overwhelming, especially when they all drop on the same day. Sometimes, however, the juxtaposition is clarifying. That’s exactly how I felt this quarter: Meta , Microsoft , Amazon and Google are all spending astronomical amounts of money building infrastructure from AI. Wall Street’s reaction, however, differed markedly, based on the cost of the frontier (or not), the potential for immediate monetization (or not), and the clarity of vision (or not). We tied all of these strings together on an in-person episode of Sharp Tech . — Ben Thompson OpenAI’s Answer to Apple.  Last month Apple sued OpenAI and alleged that hardware chief Tang Tan, along with other Apple vets in the OpenAI hardware division (but not Jony Ive!), had stolen trade secrets as part of the company’s efforts to develop competing devices. Ben covered the initial complaint well with an Update in mid-July ; this week, though, OpenAI  told its side of the story  and presented evidence that undermines Apple’s narrative. I loved Thursday’s Dithering episode reiterating the implications of Apple’s arguments for the tech ecosystem and and the stakes of all this that are easy to forget: Apple, by the terms of its own lawsuit, is trying to kill OpenAI’s hardware division. — Andrew Sharp All About LeBron in Philly.  As you’ve probably heard by now, LeBron James stunned the NBA two weeks ago when he announced he’d be joining the 76ers. Next to a slew of underwhelming free agency options, he chose a team that will present him with young and old personalities to manage, on-court chemistry questions to answer, genuine Finals upside, as well as some wonderful downside potential in a city that’s internationally renowned for booing. We hit all of it on Greatest of All Talk: first with an emergency episode that we recorded two weeks ago (you can hear our disbelief an hour after the news broke), and then with a longer, 45-minute discussion this week . Two weeks later, I’m still shocked we’re here, and thrilled as a basketball podcaster. — AS Meta Earnings, Meta’s Timing Problems, The Financial Tail — Meta’s earnings were a bit disappointing; future promises about AI products were more disconcerting. Microsoft Earnings, Microsoft vs. Meta, The Efficiency Payoff — Microsoft’s earnings were compelling because they showed a clarity of strategy, lower costs, and a tangibility of application. The reason why is scarier. Google Earnings, The Frontier Case, Amazon Earnings — Google’s earnings seemed to confirm the Anthropic hedge; it was Andy Jassy who explained why their — and Amazon’s — capex was justifiable. Vibe-Porting and Meta Enterprise OpenAI Responds ADSL Made the Modern Internet Possible A Memory-Maker Makes History; New Robot Rules; The Open Weights Debate Rages; End of July News and Notes Six More Questions on LeBron in Philly, Revisiting the 2016 Draft, The Top 5 Dinosaurs Microsoft’s Plan for Platform Survival, Meta and the Market’s Permission, A Lack of Situational Awareness

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Stratechery 4 days ago

Google Earnings, The Frontier Case, Amazon Earnings

Google's earnings seemed to confirm the Anthropic hedge; it was Andy Jassy who explained why their — and Amazon's — capex was justifiable.

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Stratechery 5 days ago

Microsoft Earnings, Microsoft vs. Meta, The Efficiency Payoff

Microsoft's earnings were compelling because they showed a clarity of strategy, lower costs, and a tangibility of application. The reason why is scarier.

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Stratechery 6 days ago

Meta Earnings, Meta’s Timing Problems, The Financial Tail

Meta's earnings were a bit disappointing; future promises about AI products were more disconcerting.

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Stratechery 1 weeks ago

Vacation: Week of July 27

Stratechery is on vacation the week of July 27. There will be no Weekly Article or Updates. The next Update will be on Monday, August 3. Sharp Tech , and Greatest of All Talk   will also return the week of August 3.  Sharp China  and  Asianometry  will continue to publish, and there will be one episode of Dithering next week. The full Stratechery posting schedule is  here .

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Stratechery 2 weeks ago

2026.30: The Copium Wars

Welcome back to This Week in Stratechery! As a reminder, each week, every Friday, we’re sending out this overview of content in the Stratechery bundle; highlighted links are free for everyone . Additionally, you have complete control over what we send to you. If you don’t want to receive This Week in Stratechery emails (there is no podcast), please uncheck the box in your delivery settings . On that note, here were a few of our favorites this week. This week’s Sharp Tech video is on Meta’s maddening messaging . Chinese Models and Frontier Futures . Kimi K3 is a very good model — so good that everyone from Wall Street to the U.S. government is suddenly worried about the U.S.’s position in AI. In fact, the threat isn’t new — and the frontier labs advantage is still real. In this week’s Stratechery Article and Sharp Tech episode I break down what has and hasn’t changed in AI, and explain why the biggest danger is U.S. policy around cybersecurity. Solving that problem will require understanding and accepting the reality and nature of Chinese competition, even if that leaves OpenAI and Anthropic to fend for themselves. — Ben Thompson What Happened to Hugging Face? As Sharp Tech’s resident normie I’m often baffled by the lingua franca of frontier technology, and this week’s controversy around OpenAI and its cybersecurity snafu introduced several terms that have amused and confounded me for years: everything centered the “Hugging Face” platform and the concept of “sandboxing,” and we were revisiting “the paper clip problem.” Thankfully, Wednesday’s Update synthesized the story in a way that was a bit more legible for the rest of us. Come to understand what happened, and stay to learn where OpenAI appears to have erred and why this mess is arguably reassuring with regard to alignment fears around LLMs.  — Andrew Sharp The NBA And Its Second Apron Bet.  If you’ve been online for the past month and even half-paying attention to the NBA, you’ve probably encountered complaints about the league’s imposition of a “second apron” salary threshold that’s effectively functioning as a hard salary cap. Contenders are being forced to part ways with homegrown stars, others teams have limited room to improve, and fans almost unanimously hate these changes. With the offseason winding down, this week’s Sharp Text explains precisely what the NBA is trying to accomplish , why I hate it, and the stakes for the league as it bets on parity in the shadow of shrinking local TV money, slowing growth, and heavy reliance on national TV revenue.  — AS Who’s Afraid of Chinese Models? — Everyone is worried about Chinese models, but the frontier labs will be fine; we need to enable open U.S. alternatives. Netflix Earnings, Is Netflix Washed?, Additional Notes — Netflix’s earnings were fine, and befitting a mature company whose most exciting days are likely behind them. OpenAI Hacks Hugging Face, What Happened, Alignment and Paper Clips — OpenAI accidentally hacked Hugging Face, but the takeaways are more encouraging than people realize. Two More Cents on the NBA’s Second Apron Era — The problem with the second apron is that it’s working. Plus: July reading recs! DST and Kimi Android, AI, and the EU France Sold Its Nuclear Steam Turbine Champion. Then Bought It Back. Kimi Madness; Xi’s AI Vision and US Questions; Trump’s Election Data Claims; The Connected Vehicle Security Act Summer Top Fives: Coaches or GMs to Get a Beer With and State Flags An OpenAI Model Escapes Sandboxing, Intelligence Will Be a Commodity Market, The Chinese Model Conundrum

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Stratechery 2 weeks ago

OpenAI Hacks Hugging Face, What Happened, Alignment and Paper Clips

OpenAI accidentally hacked Hugging Face, but the takeaways are more encouraging than people realize.

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Stratechery 2 weeks ago

Netflix Earnings, Is Netflix Washed?, Additional Notes

Netflix's earnings were fine, and befitting a mature company whose most exciting days are likely behind them.

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Stratechery 2 weeks ago

Who’s Afraid of Chinese Models?

Listen to this post : There’s a story I tell about my first day in STRT-431 at Kellogg School of Management, the introductory class that every first-year MBA was required to take; I leafed through the readings and case studies and was dismayed that there weren’t any tech companies on the docket. Me being me, I spoke to the professor after class wondering why, and was told that the goal of the course was not to necessarily learn about specific industries, but rather to uncover broadly applicable universal principles that could be applied to any company in any industry. I did not, as I usually tell the story, find this very satisfactory: to me the nature of tech, particularly the fact that software and distribution had zero marginal costs (and zero transaction costs), was something fundamentally different; putting in zeroes in formulas tends to wreak havoc! I soon realized, however, that that was my opportunity. The fundamental insight undergirding Aggregation Theory is that zero marginal costs leads to fundamentally different value chains than people once expected from the Internet: centralization and scale in a world where controlling demand mattered more than distributing supply. What is fascinating about AI, however, is the extent to which those old universal principles are coming back to the forefront. That was never more apparent than this past weekend, when arguments raged on X about the implications of Kimi K3, another open weights model out of China, approaching the state-of-the-art in terms of capabilities. The long and short of it is this: marginal costs are back in a big way, both in terms of short-term implications of state-of-the-art free models, and in terms of the long-term structure of the industry. One of the most common misconceptions undergirding discussion of open weights models is that they are cheaper — free, even. After all, you can just download the weights, and skip the time and expense and capabilities necessary to create your own model. That is, of course, true, but the “free” in this case is a reference to the amount you need to spend on research and development; R&D is a fixed expense that is independent of the revenue you generate. If you spend $1 million in R&D, it doesn’t matter if you do $100 thousand in revenue or $100 million; you still spent $1 million on R&D (it does, of course, impact your profitability). What is related to revenue is COGS — cost of goods sold — and COGS is real for AI in a way it hasn’t been for software for a very long time. Specifically, running inference on a model — whether that model be Kimi or Fable — costs money, and the amount of money an AI provider spends on inference is, at least in most business models, directly correlated to revenue. To reuse the above example, generating $100 million versus $100 thousand in revenue will likely require 1,000x COGS. In concrete terms, if it costs 50 cents to generate the tokens that drive $1 in revenue, then $100 million in revenue will have $50 million in COGS; $100 thousand in revenue will only have $50 thousand in COGS. The point in terms of open weight models is that they are not free to serve. Kimi K3 costs $3 per million input tokens, and $15 per million output tokens; that is cheaper than Sol’s $5 per million input tokens and $30 per million output tokens, but that might not even be the right measurement. Nvidia CEO Jensen Huang has described what Nvidia is building as “token factories”, and from Nvidia’s perspective that framing makes sense. Nvidia GPUs are model agnostic: they generate tokens, and do so in the fastest and most efficient way possible. That leads to measurements like tokens-per-second, time-to-first-token, tokens-per-watt, token cost, etc., and Huang argues that these metrics will be the basis for decision-making. This is a framing that definitely made sense during the first paradigm of AI, the ChatGPT era, when tokens were delivered straight to the end user. The second paradigm of AI, however, the reasoning era, confounds this measurement. Reasoning entails an explosion in chain-of-thought tokens, and different models need different amounts of reasoning tokens to arrive at the right answer. Kimi, for example, reportedly uses significantly more tokens than Sol, rendering its price advantage moot. Agents introduce a similar dynamic: some models are more efficient than others in terms of the number of tokens they need to execute agentic workflows. What this means is that tokens are not a commodity. The defining characteristic of a commodity is that it is fungible: a gallon of oil is a gallon of oil; a ton of copper is a ton of copper; a bushel of wheat is a bushel of wheat. A token from one model, however, is not the same as a token from another model. What is fungible is what is constructed from tokens, which is to say intelligence. In other words, if both Kimi and Sol generated the right answer, then that answer is fungible; the difference in tokens generated to get to that right answer is a contributor to a difference in COGS. The COGS for intelligence is a function of a few different factors: The reason this matters is that we are rapidly approaching a state in which intelligence for many economically beneficial tasks is in fact a commodity. Anyone building a basic CRUD app , for example, can likely do so using models from multiple providers. And, in a commodity market, the route to profitability is not through charging higher prices — again, you can (or will soon be able to) make the exact same app using multiple models — but rather through having a superior cost structure. It’s worth stepping through the mechanics here, because, as I noted a few months ago in Amazon’s Durability , the dynamics of commodity markets are not something people in tech are generally familiar with: The key thing to understand is that the marginal cost of producing the commodity differs by supplier. What this means in practice is that the supplier with the worst cost structure ends up selling the commodity at their marginal cost (if they can produce at all); the profits of everyone else depend on the extent to which their cost structure is better than the marginal supplier. As an example: Let’s assume the price elasticity is such that there is demand for 25 units of the commodity at $20. That means: This isn’t precisely right: the reason why Supplier C will bear the shortfall is because Suppliers A and B will be able to slightly undercut them in price, which will of course affect demand (which is elastic), but it makes the point. Supplier A has a great business, Supplier B has a good business, and Supplier C is going to go bankrupt. Bankruptcy risk is where fixed costs come back to the forefront: Supplier C has both fixed costs (like potentially R&D spend) and also may have taken on debt to finance the equipment necessary to produce the commodity. It can’t price its commodity with these costs in mind — remember, the market-clearing price approximates the marginal cost of the highest-cost unit needed to satisfy demand — but those costs can absolutely drive the supplier out of business. And, if that supplier goes out of business, then prices go up, until another supplier decides to enter (or the other suppliers expand). Let’s bring this back to models. Right now, none of the above analysis applies because demand exceeds supply for frontier models, and supply is limited by a lack of compute. This compute shortage doesn’t just mean that a compute supplier like Nvidia makes very large margins, but also that Nvidia’s customers, like SpaceXAI, can turn around and resell compute at high margins as well to a company like Anthropic. Anthropic, meanwhile, can pay the markup because they can sell tokens with a higher markup still. It’s not just excess demand that gives Anthropic great margins, however: Anthropic and OpenAI likely have among the lowest costs per unit of frontier-quality intelligence, thanks to model capability, serving scale, and token efficiency. They are serving models at a particular capability level for months before their competitors, and are simultaneously applying the best models to optimizing those costs. It’s also worth noting that the market is not yet treating intelligence like a commodity: demand is for Anthropic and OpenAI specifically, and much less for models that aren’t as good (thus SpaceXAI and Meta selling capacity to Anthropic); one way to think about the push for optimizing cost is that that is a function of defining jobs-to-be-done by intelligence level, such that intelligence buyers can create a market where intelligence is commoditized. In the long run, however, whoever is on the frontier is the best placed to dominate non-frontier markets as well, which are just the frontier minus n-months, i.e. months in which the frontier model makers have been optimizing their cost of serving. All of this is to say that I think the reaction to Kimi and Chinese models generally is pretty over-blown, at least from an economic perspective. Right now there is a price umbrella that is downstream of the lack of compute; I highly doubt that Chinese models are cheaper to serve on a marginal cost basis, they just seem cheaper because Anthropic and OpenAI are so supply constrained that they are charging far more than they would if there were sufficient supply to meet the demand for intelligence. Why, then, do the model makers in particular seem so panicked about Chinese models? First, I think the frontier labs are anchored in a world where training costs dominated their financial modeling. As long as training consumed more GPUs than inference, it was critical to maximize inference revenue to help fund the next training run, which meant charging very high prices for inference. Going forward, however, I expect the inference market to grow much faster than training costs (and that includes the assumption that training costs will continue to skyrocket), which means they really can make it up in volume. It wasn’t clear this would be the case as recently as eight months ago, but the agent paradigm unlock is so massive that frontier labs should have more confidence that they can not just survive but thrive with lower prices (once they have sufficient compute). Second, intelligence isn’t in fact a perfect commodity, in part because applied intelligence makes itself smarter. Specifically, whoever is running inference is also collecting data, and that data goes into making the next iteration of the model better. This is, on one hand, all the more reason for the frontier labs to lower prices and increase usage as more compute comes online; on the other hand, this is why companies like Microsoft are increasingly obsessed with helping companies run their own models. That is much more viable if Chinese models are a viable alternative. Third, the other way that frontier labs can not only differentiate from Chinese models but also from each other is by continuing to integrate up into the customer experience. It’s striking the extent to which Claude Code and Codex are proving to be quite sticky; whichever harness you start working with is likely to be the one you stick with, and that figures to be even more the case with non-technical users. And, in the long run, this imperative to move up the stack does mean that frontier models are absolutely a threat to software providers, including Microsoft. On the flipside, the extent to which software companies who currently own the customer experience have access to competitive models is the extent to which they may be able to resist the encroachment of the frontier labs. Finally, the ideological angle of Anthropic in particular is impossible to ignore. This is a company that believes only it can be entrusted with AI, and the existence of open weights alternatives strikes a fatal blow to that presumption. Kimi isn’t the only new Chinese model; from Bloomberg : Alibaba Group Holding Ltd. shares rose as much as 5.4% on Monday after the company launched a preview version of its flagship Qwen3.8 Max model, describing it as second only to Anthropic PBC’s Fable 5. The Sunday release came only days after startup Moonshot AI unveiled a powerful new offering that’s roiled markets and triggered concern in the US about China closing the gap on global leaders like Anthropic and OpenAI. Qwen3.8 Max has 2.4 trillion parameters, joining Moonshot’s Kimi K3 in the heavyweight class. With 2.8 trillion parameters, K3 rivals top offerings and Alibaba is setting similarly high expectations. Developers can now access Qwen3.8 Max through Alibaba’s coding platforms, including Qoder. Alibaba plans to make the model open-weight soon, expanding access beyond the preview release. Interest in these made-in-China artificial intelligence systems and models is so high that Moonshot was forced to pause taking on new subscriptions late on Sunday to manage overwhelming demand. The fact that Qwen3.8 Max will also have open weights is notable. Alibaba stopped releasing weights for its leading edge models earlier this year, but appears to have reverted that change; I suspect that shift was related to last week’s Xi Jinping speech about AI that doubled down on the open weights approach: We should adhere to the principle of openness and win-win and boost innovation-driven development. As a new engine of world economic growth and an accelerator for the shift of growth drivers, AI is moving from the digital world into the physical world. We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing. We should facilitate technological innovation, industrial development and scenario-based application of AI. We should make coordinated advances in the transformation and upgrade of traditional industries, the cultivation and growth of emerging industries and forward-looking planning for future industries, so that all sectors and businesses can benefit from AI. The strategy for China is obvious: commoditize your complements. Note that Xi explicitly ties openness to AI “moving from the digital world into the physical world”; the physical world is the world dominated by China, and the country’s lead in areas like robotics is going to massively benefit from widely available AI models. Along the same lines, China does not want the U.S. to gain an asymmetric advantage in AI; to the extent that China can weaken the U.S. frontier labs while strengthening any and all potential U.S. adversaries so much the better, and it can benefit from the innovation that will attach itself to an open ecosystem. By the same token, don’t expect China to do anything about distillation attacks on the frontier labs. I think it is mistaken to attribute all of the success of Chinese labs to distillation, but it’s just as much of a mistake to pretend like distillation doesn’t give Chinese labs a big advantage. That advantage has really come to bear in the last year as post-training reinforcement learning has become increasingly crucial to model performance. Instead of having to fashion reinforcement learning environments from scratch, Chinese labs can simply use frontier labs models as teachers, allowing for rapid improvement at much lower costs (this is not the only reason why Chinese models are cheaper to develop, but it’s a big one). What is interesting is that one of the most important use cases for Chinese models in the West is itself distillation. Thinking Machines, for example, which just released an open-weight model, relies on Chinese models to solve the cold start problem for reinforcement learning. Dean Meyer and Konstantine Buhler wrote an excellent article on X explaining that distillation means that Western open weight models are fundamentally disadvantaged relative to China: Distillation does not explain China’s entire open-model lead. Chinese labs have world-class researchers, substantial compute, strong pre-trained models, software-hardware codesign, and rapidly improving post-training capabilities. But distillation compresses the costly final gap between a strong base and a near-frontier system. Even if distillation represents a smaller share of a Chinese model’s total capability, it represents a meaningful share of its advantage over American open models. New enforcement mechanisms will make large-scale distillation harder, slower, and more expensive for Chinese companies. However, enforcement will not eliminate distillation backed by state actors. Every Western frontier advance therefore creates another teacher for Chinese labs. Western builders must either reproduce those capabilities independently or wait to learn from Chinese models. This gap gives Chinese labs a recurring structural advantage over Western companies. This is a point that bears repeating: because U.S. open weight model makers must follow the frontier labs’ terms of service, they (1) are worse than Chinese alternatives and (2) end up distilling the distillation, just with a detour through Chinese labs. Wouldn’t it be better if western open weight model makers could go to the source? To that end, here’s an even more interesting question around distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? In fact, this paradox is the solution. I believe that open weight models are good for innovation (and, per the above, I think that labs on the frontier will be fine), but it’s a problem to be dependent on China. The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else. This entire Article has been an exercise in defusing overreaction to Kimi K3 specifically and Chinese open weight models generally; however, there is one reason to be concerned, and that is cybersecurity. Consider this story from The Stack : Hugging Face said its production infrastructure was breached by an “autonomous” AI agent system early last week. The platform’s security team were initially stymied in their incident response (IR) by unnamed US LLM frontier model guardrails “which cannot distinguish an incident responder from an attacker,” they said. So Hugging Face’s defenders turned instead to the open-source GLM 5.2 model from China’s Z.ai lab – running it on their own infrastructure to analyse the 17,000+ logs, or footprints, that the attackers left behind. That’s a striking public admission for the New York-headquartered Hugging Face, which lets users collaborate on models, datasets and applications, and which this summer hit the $100 million ARR mark. In an incident report, the company recommended that defenders “have a capable model you can run on your own infrastructure [our italics] vetted and ready before an incident, both to avoid guardrail lockout and to keep attacker data and credentials from leaving your environment.” It’s difficult to overstate how wrong-headed the Trump administration’s panicked response to Anthropic’s release of Fable was, particularly since it exacerbated Anthropic’s worst tendencies in terms of assuming only they can be trusted with powerful AI. In a world with only one AI, it might make sense to reserve the most powerful cybersecurity capabilities for the U.S. government and trusted allies; however, that’s not the world we live in. There are and will be models eminently capable of mounting cybersecurity attacks on existing infrastructure, and those models will be — already are — widely available. The best defense — the only viable defense, in fact — will be to make sure defenders have access to the best models as well. Right now defenders are effectively banned from using Fable or Sol for cybersecurity because of Trump administration directives; that means the best alternative is using models from a country which has been trying to weaken our cyber defenses for years. This is insane! The better course is clear: first, loosen Fable and Sol restrictions on cybersecurity, and second, ensure that U.S. open weight model makers are on an equal playing field with China. Yes, the frontier labs will kick and scream about this, but the Administration should realize that listening to their histrionics has led the U.S. to a position where U.S. companies are dependent on China for their defenses. Let the frontier labs win by being better; don’t let them define safety or security, or pull up the ladder of humanity’s collective knowledge. China is already hard enough to compete with; letting them carry the standard for openness and innovation is simply giving away our biggest advantage. Model footprint: The weights and runtime state determine how much expensive memory and how many accelerators are required to host each serving replica. Inference efficiency: Architectural choices (e.g. Mixture-of-Experts) reduce computation per generated token. Memory efficiency: Architectural choices can reduce KV cache requirements, allowing more concurrent requests and better GPU utilization. Serving efficiency: Batching, scheduling, prefix caching, and other inference optimizations maximize utilization and share work across requests. Token efficiency: The fewer tokens required to reach a correct answer, the lower the inference cost. In commodity markets, everyone charges the same price, because everyone is selling the same thing; that price is determined by supply and demand. The demand for a commodity is a function of price elasticity: the cheaper the commodity, the more demand there is for it, and vice-versa. The supply for a commodity is a function of the marginal cost of producing the commodity. Supplier A can produce 10 units of the commodity for $10 each Supplier B can produce 10 units of the commodity for $15 each Supplier C can produce 10 units of the commodity for $20 each Supplier A will sell 10 units of the commodity for $20, earning $10/unit Supplier B will sell 10 units of the commodity for $20, earning $5/unit Supplier C will sell 5 units of the commodity for $20, earning $0/unit

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Stratechery 3 weeks ago

2026.29: Mainframes and Main Characters

Welcome back to This Week in Stratechery! As a reminder, each week, every Friday, we’re sending out this overview of content in the Stratechery bundle; highlighted links are free for everyone . Additionally, you have complete control over what we send to you. If you don’t want to receive This Week in Stratechery emails (there is no podcast), please uncheck the box in your delivery settings . On that note, here were a few of our favorites this week. This week’s Stratechery video is on A Script for Mark Zuckerberg . The End of the Mainframe? IBM’s stock experienced the worst day in its history, which is saying something considering the company has been a public stock for 115 years. The product most synonymous with that history is the mainframe computer; mainframes defined the first wave of IT , and they are so useful and essential that IBM’s customer base is largely the same as it was half a century ago. Now, however, mainframe sales and the software that runs on them are faltering; management blamed AI spend, but as I argued in Wednesday’s Update , the real concern for IBM is that AI’s ability to port the essential backend programs that run on archaic technology will mean those missed sales never come back. — Ben Thompson The Continuing Adventures of OpenAI.  The fun with OpenAI never ends, and the middle of the summer is no exception. In Monday’s Update Ben parsed a new lawsuit from Apple that looks like more smoke than fire, and in Tuesday’s Update he doubled back to cover the revamped ChatGPT app on the Mac and what it signals about the company’s priorities going forward. On this week’s episode of Sharp Tech , and both episodes of Dithering , we discussed all these topics, as well the reports of OpenAI’s new hardware product — an ambient speaker, with robotic components — and why that idea sounds like a great first experiment in the hardware category.  — Andrew Sharp Is Netflix Washed? It was only last December that Netflix was set to buy Warner Bros. Discovery, dominate Hollywood in perpetuity, and compete with YouTube using a massive library of hit franchises and HBO IP. That idea was abandoned in February and it’s been a rocky year ever since, including another hit to the stock on Thursday and Friday. I wrote about all of it on Sharp Text this week , where I marvel at how disposable the original content has become and argue that the attempts to retain attention by mimicking YouTube (and possibly Tubi?) have left the biggest premium platform in the world looking more mortal than ever.  — AS Apple Sues OpenAI, Apple’s Real Problem — Apple is suing AI for stealing trade secrets; there is one guilty employee, but this mostly feels like lashing out. The OpenAI Super App, ChatGPT = Codex, Whither Chat — OpenAI has refashioned Codex as the new ChatGPT; is the company abandoning the chat category they pioneered? IBM Misses, IBM’s Mainframe Moat, IBM’s Many AI Problems — IBM announced preliminary results that spooked the software market generally; this is a story, however, specifically about IBM and its mainframe franchise. Is Netflix Washed Now? — Watching Netflix as the platform exits its prime. Apple Sues OpenAI OpenAI Hardware The Cochlear Ear Miracle K-Shaped Economic Data And Its Implications; Ma Xingrui News; Closing Window for Open Source AI?; The SCS and International Law Everyone at Summer League Is Thrilled, The Summer of Second Apron Angst, How to Be an NBA GM The Continuing Adventures of OpenAI, Apple’s Trade Secrets Lawsuit, Q&A on Mainframes, Meta, Daylight Savings Time

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Stratechery 3 weeks ago

IBM Misses, IBM’s Mainframe Moat, IBM’s Many AI Problems

IBM announced preliminary results that spooked the software market generally; this is a story, however, specifically about IBM and its mainframe franchise.

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Stratechery 3 weeks ago

The OpenAI Super App, ChatGPT = Codex, Whither Chat

OpenAI has refashioned Codex as the new ChatGPT; is the company abandoning the chat category they pioneered?

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Stratechery 3 weeks ago

Apple Sues OpenAI, Apple’s Real Problem

Apple is suing AI for stealing trade secrets; there is one guilty employee, but this mostly feels like lashing out.

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Stratechery 1 months ago

2026.28: XBOX On the Rocks

Welcome back to This Week in Stratechery! As a reminder, each week, every Friday, we’re sending out this overview of content in the Stratechery bundle; highlighted links are free for everyone . Additionally, you have complete control over what we send to you. If you don’t want to receive This Week in Stratechery emails (there is no podcast), please uncheck the box in your delivery settings . On that note, here were a few of our favorites this week. This week’s Asianometry video is on TOTO: From Toilets to E-Chucks . A Word from Mark Zuckerberg*.  I was delighted to see Ben insert himself into the CEO chair at Meta on Tuesday and write a script for Mark Zuckerberg as he tells the story of Meta and its AI investments in 2026. That article traces past Meta mistakes as well as those of investors who doubted the company, all to frame current investments in AI and the massive opportunities that remain central to the Meta’s future. A combination of history, analysis of the future, and fun, it’s a perfect summer read. As for a summer listen, we doubled back on all of it, plus Meta’s Muse-Spark release, for this week’s episode of Sharp Tech .  — Andrew Sharp Pulling the Plug on XBOX? It’s been years since there was good news coming out of the XBOX division at Microsoft and that trend continued this week, as XBOX CEO Asha Sharma announced plans to eliminate 3,200 jobs, or around 20% of its staff over the next 12 months. Wednesday’s Daily Update explores how Microsoft arrived at this point and why, in particular, the Game Pass initiative that was the last great hope for XBOX has been a failure. I’m not a gamer, but Ben’s rendering of the XBOX story — and the Game Pass story — is a great case study of both internet economics and management mistakes (and analyst ones!). — AS Toilet Talk . Look, I get that’s a little weird, but if there is one brand of household appliances that I cannot imagine living without, it is in the bathroom. Specifically, I absolutely love my Toto toilet, and was delighted that Jon made a video about the company on Asianometry . Here’s the twist: the reason why Toto is a subject of interest isn’t their toilets, but rather the fact the Japanese company also plays a critical role in the AI supply chain. — Ben Thompson A Script for Mark Zuckerberg — A script for what Mark Zuckerberg should say on Meta’s next earnings call. XBOX Cuts; Bundling and the Internet Solvent; Transaction, Coordination, and Sunk Costs — Microsoft’s Xbox division is conducting big layoffs, as the company deals with abject failure of its Game Pass strategy. Muse Image, Grok 4.5, Alex Karp on CNBC — The battle for verifiable data is increasingly defining the AI race, from Meta to Grok to the frontier labs. Online Insanity and Its Counterpoint — What we can and can’t achieve in response to paranoia and extremism online. The New ChatGPT App The Debt-Fueled Collapse of China’s Top Machine Tool Maker RCA and the Vacuum Tube’s Last Stand A Missile Test and New PLA Generals; The CITIC Plane Crash; America’s Taiwan Interests; Guo Wengui Jailed and Ezra Jin Released A Tale of Two Cities and Jaylen Brown, Minnesota’s Bet on LaMelo, Peterson Arrives and Mitchell Cashes Out Meta and Its Messaging Problem, The XBOX Reset, Q&A on Token Costs, American Soccer, Starlink in Nature

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Stratechery 1 months ago

Muse Image, Grok 4.5, Alex Karp on CNBC

The batter for verifiable data is increasingly defining the AI race, from Meta to Grok to the frontier labs.

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Stratechery 1 months ago

XBOX Cuts; Bundling and the Internet Solvent; Transaction, Coordination, and Sunk Costs

Microsoft's Xbox division is conducting big layoffs, as the company deals with abject failure of its Game Pass strategy.

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Stratechery 1 months ago

A Script for Mark Zuckerberg

Listen to this post : The setting: Meta’s earnings call in early August, 2026. The speaker: Meta CEO Mark Zuckerberg . Good afternoon everyone, and welcome to Meta Platforms’ Second Quarter 2026 Earnings Conference Call. Our remarks today will include forward-looking statements, which are based on assumptions as of today. Actual results may differ materially as a result of various factors, including those set forth in today’s earnings press release and in our quarterly report on Form 10-Q filed with the SEC. We undertake no obligation to update any forward-looking statement. I know it’s weird that I, Mark Zuckerberg, am doing the Director of Investor Relations job, but anything is possible when this speech is made up. What follows isn’t actually me: it’s what Ben Thompson of Stratechery thinks I should say on this call. I know that Meta and myself are facing a lot of questions about AI, particularly the amount of money we are spending on capex. Our core business is an asset-light cash generation machine, so why are we spending tens of billions of dollars on AI? To answer this question I want to give you a quick recount of our history, what I’ve learned, and why I am so confident that we are doing the right thing for our future. So let’s get to it. Facebook was, as you know, the digital representation of Harvard’s analog Face Books. What was clear from the very first day we went live was the extent to which humans are, first and foremost, interested in other humans. People would spend hours clicking around to people’s pages. To put it another way, our first algorithm was human curiosity. What truly super-charged Facebook usage, however — and which transformed the Internet — was the feed. Now, instead of actively surfing to friends’ pages to look for an update, we showed updates to you in a single feed on your homepage. You might remember that we got a lot of heat for this decision, including protestors outside our office in Palo Alto. The lesson we took from that, however, is one that has guided us to this day: first, the revealed preference of users, as captured by data, was that they loved the feed: engagement skyrocketed. Second, we learned to trust our own — my own — product intuition, and that conviction has served us well over the years. Another critical moment in our early history was the shift to mobile. We didn’t get this right in the beginning — more on that in a moment — but what was quickly apparent is that more access to Facebook meant more usage of Facebook. I can’t emphasize this point enough: when humans can connect to humans, they do, and when they can do it more conveniently and in more places, they do it more often. Finally, I would be remiss to not mention Instagram. Obviously Instagram has been a major part of our growth over the last 15 years — and, I would add, we have been a major part of Instagram’s growth. To that end, an important thing to understand about Instagram is the extent to which it has evolved . Just because we gave our users what they wanted at one particular moment in time does not mean we can afford to sit still: more bandwidth first meant more pictures in Stories, and then video in Reels. Instagram has gone from strength-to-strength precisely because it has changed as technology has changed. We — I — haven’t done everything perfectly. We’ve taken our arrows through the years for lots of things that frankly aren’t our fault, but are rather the reality of being the primary communications platform for all of humanity, and humanity is flawed. I’m proud of the efforts we have made to ameliorate humanity’s worst impulses while enabling some of our best tendencies, including that desire to connect. Rather, my mistake is itself a very human one: for many years I have resisted embracing what Facebook — now Meta — is, and spent too much time trying to emulate some of the tech titans who came before me. Specifically, I have been obsessed with becoming a platform. The first manifestation of this error was the initial shift to mobile I referenced above. When Facebook was primarily a browser app I invested heavily in trying to build a platform, with things like Facebook Games, payments, etc. We had some success there — some of you on this call might have played Farmville back in the day — but when mobile came along we mistakenly tried to hold onto web technologies that supported my vision, and were years too late in investing in a truly native smartphone experience. The reality — and this is hard for me to admit — is that Apple saved us from my mistaken obsession. Mobile Made Facebook Just an App, and that was Great News . Instead of diminishing the Facebook experience so that we could feature third-party developers, we had to cede that space to Apple and put our own content front-and-center. It turns out that was what people wanted the most; in fact, they wanted it so much that they willingly scrolled through and clicked on the most compelling ad units ever. And make no mistake, we paid back our debt: Facebook built the App Store just as much as Apple did. My second error was Reality Labs. While in recent years I have framed our acquisition of Oculus and virtual reality as a necessary response to Apple’s attempt to handicap our business, the truth is that I invested twelve figures into this technology because I thought it was cool, and yes, because I wanted to own a platform. I do think we’ve made compelling strides in this area — and we’ve created technology that is going to matter in the long run — but I now recognize that part of the reason I am delivering this mea culpa right now is because I burned a lot of credibility with investors with all of the losses Reality Labs has endured with very little to show for it. My third error was not in trying to make Facebook something it was not, but rather failing to appreciate what it had become. While I was thinking about platforms, I took it for granted that connection was enough for the core business; in fact, Facebook had evolved into entertainment , at least in its public-facing forms (I will take credit for the acquisition of WhatsApp and realizing that Messaging Was Mobile’s Killer App ). This was an insight that TikTok figured out first , and it was a blindspot for me . What I’ve come to realize is that all of these mistakes are symptoms of what has been my biggest failing as CEO: all of you on this call have appreciated our ad business more than I have. I’ve been very blessed as CEO to have excellent co-workers who have over the years developed the world’s best digital ad business, while I frankly haven’t taken as much interest as I should have. My failure to appreciate our ad business is another lens through which to examine my mistakes: This neglect as CEO left us badly exposed in our disputes with Apple. I firmly believe that Apple’s characterization of digital advertising was unfair, dishonest, and self-serving . What I failed to do, not just in that bruising battle but in the years leading up to it, was make the affirmative case for ads generally, and Meta ads in particular. It’s easy to see how the Internet has made it possible for an entirely new category of entrepreneurs to create products that uniquely serve the tremendous capacity of humans to manufacture an infinite array of desires, growing the economy to the benefit of everyone; what’s harder to appreciate — in part because I haven’t made the case — is that the only way to connect those creators to the consumers who love them is digital advertising. We don’t serve ads like Google — or Apple in the App Store, or Amazon on Amazon.com — that in many respects function as a tax on search; we show people products they never knew existed, but that immediately generate desire and, ultimately, happiness. In short, I believe that we are a force for good in the world, not just because we connect people to each other, but because we connect entrepreneurs with customers in a way no one else does. Forgive the long preamble, but this is necessary context for me to properly explain why AI is so important to Meta, and why I am making the right choice to invest so heavily in both talent and infrastructure. First, when investors compliment our asset-light business, what they are complimenting is the fact that our business is purely digital. Everything digital, however, is firmly within AI’s cross-hairs. It may seem odd to begin my AI pitch by highlighting terminal value risk, but today is about honesty: every single digital company on earth faces an existential threat from AI, and we are no exception. Meta must invest in AI because a failure to do so would cost us far more in the fullness of time, particularly now that we’ve seen the very real risks entailed in depending on a third-party . Second, AI makes our business better — and by “our business”, I mean ads. AI is more than LLMs: it is machine learning, and we have been using machine learning to improve our ads business for years. More recently, we have developed GPU-dependent algorithms that have significantly improved our ability to not just target ads but also recommend content, which keeps people entertained longer, which lets us serve them more ads. And, looking forward, LLMs themselves will transform advertising, not just by generating copy and images, but by predicting the ads and content that people want to see. Every single one of these improvements goes directly to our top line — and remember, because advertising enables us to offer our products for free, the capacity to increase our top line is unbounded by price elasticity. Third, the single most important indicator that our business is on the verge of a step-change in growth is when we dramatically increase inventory. This is something investors regularly get wrong: back when we added Stories, investors panicked about falling prices-per-ad without realizing we were increasing inventory we could grow into. Five years later, investors made the exact same mistake with Reels . Those were the two best opportunities to buy Meta stock — or any stock, really — in history. We are facing an even larger opportunity over the next several years. AI makes every pixel monetizable , which means we are looking at the largest inventory expansion ever. Yes, it will take a few years to realize this opportunity, but the technology is there. More importantly, what I’ve come to realize as I’ve embraced our status as an entertainment provider and ad purveyor is that — our nature as a digital business notwithstanding — we are remarkably well-placed to thrive in an AI era. Remember what we learned about humans: they are obsessed with other humans, and they want to connect with them; that obsession and desire are only going to increase as we interact more and more with AI. AI is going to make our properties more essential, not less. Moreover — and here I must issue one more mea culpa — AI is a productivity tool, but productivity is not the end-all-be-all of the human experience. I have talked over the last year about building superintelligence that helps you get things done, but that’s a business story. What we can uniquely do is give people the experiences they want — from connection to entertainment to shopping — when they are off the clock. The fact that we are investing in AI but not selling solutions to businesses is actually one of our biggest advantages . Oh, and by the way, AI might actually lead to new hardware paradigms. I admit I was wrong to spend so much time on virtual reality, but that did lay the groundwork for a unique opportunity to develop devices that make much more sense in a world where we want to access AI everywhere, not just on a phone in our pocket. I know that many of you on this call have doubted my investment decisions before — and I understand the consternation about Reality Labs in particular. However, keep in mind that when our stock dipped in 2022, one of the big reasons was because of our aggressive capex spending, which went primarily to GPUs; ChatGPT came out a month later, and that decision to spend heavily with Nvidia looked incredibly prescient in hindsight. That prescience, however, pales in comparison to the payoff that will accrue to anyone with the foresight to build data centers and buy compute over the last several years, and for years into the future. We don’t have the luxury of waiting until the future is invented and then investing; we need to invest now, especially when the opportunity in front of us — with ads specifically — is so apparent. That noted, we are in a truly unique time, when there is a real market for selling compute on the spot market. To that end, we are going to sell access to a portion of our compute infrastructure on a short-term basis, with the ability to claw back that compute at any time. This will accomplish two important things: first, the proceeds from these rentals will fund an even larger build-out going forward. We will need this capacity in the future. Second, rental prices will provide a hurdle rate that will focus and discipline our decision-making. Let me expound on this point, because it brings this entire opening statement together: I now realize that my obsession with platforms and productivity has frequently led us astray, and that I have given insufficient appreciation to our advertising business and failed to embrace the reality of what Meta is. I truly believe we have compelling reasons to invest in AI — arguably the most compelling reasons — and the fact that the market doesn’t agree is my failure. To that end, making our compute available for rent means that we can only take it back if we can make more money on it ourselves; the only way we can do that is by leaning into what we are good at, not what I have spent too long wanting us to be. To put it another way, our best product decisions have been intuition validated by data and revealed preference; that’s how we’re going to approach AI. We will build, because we must, but we will let the market decide who gets to use it: I’m confident my newfound religion on ads will result in all of that compute being used by us to make more money than we can ever make as a permanent cloud provider. We are not out here to make chatbots or compete with OpenAI and Anthropic; they can fight for work and productivity and charging subscriptions and replacing humans. Our goal is to celebrate humans, to connect them, to entertain them, and to enable commerce among them. We need compute to do this at scale, and I know it will pay off. My commitment to you is that we will structure our business so we have no choice but to do just that. We’ve done it before, and we will do it again. And with that, over to Susan. Building a platform is antithetical to building an ad business. A platform’s goal is to feature third-parties; an advertiser’s goal is to capture attention for itself. Investing in an entirely new technology, including developing hardware, fundamentally limits our addressable market ; an advertiser’s goal is to maximize its market size. Entertainment is the best possible category for an advertiser to own: people willingly give entertainment their attention, which is exactly what an advertiser wants to sell.

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Stratechery 1 months ago

Summer Break: Week of June 29

Stratechery is on summer break the week of June 29. There will be no Weekly Article or Updates. The next Update will be on Monday, July 6. Dithering ,  Sharp Tech , and  Sharp China  will also return the week of July 6.  Greatest of All Talk  and  Asianometry  will continue to publish. The full Stratechery posting schedule is  here .

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Stratechery 1 months ago

2026.26: Summer Vibes

Welcome back to This Week in Stratechery! As a reminder, each week, every Friday, we’re sending out this overview of content in the Stratechery bundle; highlighted links are free for everyone . Additionally, you have complete control over what we send to you. If you don’t want to receive This Week in Stratechery emails (there is no podcast), please uncheck the box in your delivery settings . On that note, here were a few of our favorites this week. This week’s Stratechery video is on Anthropic’s Safety Superpower . A Vibe Coding Adventure. It is thrilling to be an analyst in the age of AI, particularly because the questions seem so weighty. Are software companies doomed? Will white collars work exist in a decade? Might chip policy lead to war in the Taiwan Strait? All valid! And, at the same time, fretting about the future can foreclose an appreciation at how incredibly awesome this technology is, and that the possibilities really are endless. You can do anything — even organize your garage. That might sound silly, but technology, for all of its importance, is also fun, and I’m having a blast . — Ben Thompson Apple in Europe (but not Siri AI).  It was a footnote to Apple’s announcements at WWDC two weeks ago, but as expected, the now-fully-function Apple Intelligence products — aka Siri AI — will not be released in Europe because of the company’s ongoing battle with European regulators over the Digital Markets Act. On Dithering Tuesday, Ben and Gruber had a great 15-minute discussion about how maddening the situation continues to be, but I also appreciated the end of Ben’s Daily Update on Tuesday , which covered the same topic and explained why Apple’s own policies may well be what creates the long-term competitive changes the EU hopes to see.  — Andrew Sharp A Midsummer Mailbag on Sharp Tech. Every time a major holiday approaches, we try to celebrate on Sharp Tech with an extended mailbag that, thanks to the listeners, tends to be a lot of fun. Ben and I did that again for this week’s episode , and in addition to thoughts on the future of the memory chip market and more of Ben’s experience with vibe coding, we hit questions on our daily caffeine intake, Sam Altman’s PR strategy, data centers in the ocean, and how to improve international soccer. Come for both substance and pre-vacation goofiness, and whether you’re traveling next week or not, happy 4th of July!  — AS Apple Price Increases, Apple Intelligence and the E.U. — Apple is (finally) raising prices, but they’re not shipping Siri AI to the E.U. Memory Chips and China, Microsoft and Chinese Models — The big three memory makers may come to regret opening up the door to Chinese memory makers; Microsoft, meanwhile, is very incentivized to use Chinese models. My Vibe Coding Adventure, The App and the Experience, Ten Takeaways — My experience and reflections on vibe coding an app that I plan on actually using regularly. An Interview with Figma CEO Dylan Field About Design and AI — An interview with Figma CEO Dylan Field about building Figma, and why he believes AI gives the company a tailwind. Hopes, Fears, and the Wizards — A window into Washington Wizards fandom during a very big week, after a very long decade. No Siri for EU Price Hikes Embedded Memories: The Next Generation Party Building and Xi’s Dominance; Memory Chips and ASML Accusations; Germany’s Puzzling Push for Plaza Accords Draft Week Winners and Losers, Miami Gets Giannis and Boston Gets Awkward, Micah Nori and a Blazers Experiment A Summer Break Mailbag: Memory Mania, Vibe Coding, Mafia PR, Caffeine Intake, Garages, and How to Fix Soccer

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Stratechery 1 months ago

An Interview with Figma CEO Dylan Field About Design and AI

Good morning, This week’s Stratechery interview is with Figma co-founder and CEO Dylan Field . Field was a Thiel Fellow who dropped out of Brown in 2012 to start Figma. Figma was born of a technical breakthrough that leveraged WebGL to deliver powerful graphical capabilities in the browser; the browser made Figma collaborative, what I call the operating system of design . Figma has had a fascinating road: the company accepted an acquisition offer from Adobe in 2022, but due to regulatory resistence the latter was forced to abandon the merger in late 2023. Figma instead IPO’d in 2025 , and after skyrocketing to a valuation of $56.3 billion, has since crashed to a market cap of less than $10 billion, less than half of Adobe’s offer, thanks in large part to a market narrative that the company is an AI loser. I talk to Field about all of this, including his background, Figma’s differentiation discovery process, and the nature of creativity versus design. We get into the AI question, which the market views as a headwind, but which Field sees as a tailwind. To that end, the occasion for this interview was Figma’s Config conference and Field’s keynote where he explained how Figma’s Canvas was the natural intersection between design and AI. As a reminder, all Stratechery content, including interviews, is available as a podcast; click the link at the top of this email to add Stratechery to your podcast player. On to the Interview: This interview is lightly edited for clarity. Dylan Field, it feels like this interview has been in the works for years, but welcome to Stratechery. DF: Thank you, appreciate you having me, and big fan. Let’s start with your background. Where did you grow up, how did you become interested in technology? I always love these stories, especially the first time I talk to someone, and I think yours is a particularly interesting one. So give me the story. DF: I grew up in Penngrove, California, which is near Petaluma in Sonoma County — but not Sonoma, it’s critical to make sure people know where Penngrove is. My mom was an elementary school teacher, my dad a respiratory therapist, both not especially tech-savvy, but my mom early on realized that a computer would be useful for me to stop bugging them with questions and bug the computer instead. So I was lucky enough to get a — I think it was a Compaq Presario — when I was like five the family got one, and then I proceeded to really hog it. I’ve pretty much been interested in technology as far back as I can remember, I was very eager and excited to learn how to program, but didn’t necessarily have the ability to get my hands in a compiler for a while. It took until I got through some scholastic program, a BASIC compiler, to actually get properly started. I’ve also always had a, maybe not as much ability as I’d like, but a deep fascination with mathematics and just really everything in the world. And so this is just a fascination with the technology — like, how does this thing actually work, and how can I make it do what I want? DF: It was always more about product and design and about what technology will look like in the future and how to get there, rather than “I can really master the technology and have it under my control”, that was never really my vibe. What were the sorts of things you imagined you wanted to make as a kid, when you have this computer you want to figure out? DF: Walking around as a kid I was probably thinking less about the computer and more about, “Why can’t I teleport?”, or, on the flip side, going to SFO the first time and seeing they had these magical faucets where you put your hand in front and the water comes out and you didn’t have to touch anything — and I was a germaphobic kid — I’m like, “Why can’t the entire bathroom be automated?”, it’s just so obvious. Or, before I even learned how to properly read and write, “Why can’t I talk to the computer?”, stuff like that was more what I was excited by. Are you encouraged or discouraged by the progression of bathroom technology over the years? DF: Encouraged. Toto ‘s wonderful. Yes! It’s funny, because Toto is in the news because they make a certain sort of ceramic that’s used for AI stuff. I’m like, “Look, I’ve known about and been a Toto fan and supporter for many, many years”. DF: (laughing) I didn’t know that. Well, the other critical design invention here, which is very underappreciated, if you’re leaving a bathroom and you can use your foot to pull open the door, that is an underappreciated progression. Oh, there you go, that makes total sense, I can’t say I have that in my bathroom, but I do have a Toto Washlet toilet, they are well worth it — the only problem is you’ll be spoiled for life and won’t be able to live without it. So you end up at Brown — not what you’d think of as a technology school, it’s next door to RISD, which is a design school, so there’s an angle to where you ended up. What was the path to getting there, and the path to leaving as a Thiel Fellow ? DF: During high school I was probably a little overconfident, thought I could do anything and was beyond bright, and the world quickly proved me wrong, “Okay, there are people far smarter than you”. But due to that identity, I thought maybe MIT would be the place I want to go, then I toured MIT and it was a cloudy day, midterms, and I went, “No, this isn’t for me”, and looked at other spots. One person I’d talked with a lot was Danah Boyd — I met her through O’Reilly Media — and she was a really brilliant, thoughtful person, and she said, “You’ve really got to think about Brown”, and I kept randomly meeting Brown grads as I was doing this East Coast college tour, very randomly, and they’d all sit me down for an hour and tell me, “You’ve got to apply to Brown, and if you get in, you’ve got to go”. I ended up applying to Olin and Brown on the East Coast out of ten schools I visited, I was thorough, I didn’t get into Olin, which I thought was my first choice at the time. And then Brown, I was very surprised but thrilled to get in. What did you think you were going to study at that point? DF: Computer science and math, I did formally declare that as my concentration, but I didn’t get as far on the math side as I would have liked — did more CS classes, and also took advantage of Brown’s amazing open curriculum, where you can go very broad, I had some incredible classes in areas that are not technical at all. So where did the Thiel Fellowship come into the story? DF: It was the fall semester of my junior year. I was aware of the Thiel Fellowship — I’d seen it online, thought it was kind of a weird idea, but interesting. I got introduced to it by Elizabeth Stark , who now is, I believe, leading Lightning , she introduced me to one of the Thiel Fellows at the time, Dale. It was this weird one where he was 25 minutes late to a 30-minute meeting at Starbucks — we met for five minutes, but then he just kept texting me, “You’ve got to apply to the Thiel Fellowship”, very similar to the Brown story. I ended up applying after speaking with my now co-founder, Evan Wallace . Evan was the most brilliant person around — a year above me at Brown, my TA for multiple classes, and truly a genius, someone who’s also just fundamentally kind, humble, wonderful. I was like, “Man, I’ve done some internships now, there’s no one better to start a company with”, and if Evan were down for that instead of any number of jobs he can get when he graduates, I’d learn more from it than anything else — I can always go back to Brown, so I should at least explore it, and he surprisingly was down to explore it with me. So I applied to the Thiel Fellowship with a drones idea — which I think now is best being done by BRINC . Evan was just not down for that direction, he was down for WebGL and graphics, and I was psyched by that too, that’s the direction we headed. Tell me about the drones idea and the pivot to the WebGL angle, because it ties into the question I asked at the beginning — what were you pursuing? Was it the technology, or the end state? I think that’s an interesting through-line here. DF: I’ve always been excited about a lot of things — creation, creativity, design, even before I knew what to call design, which was most of my life at that point, I’d only recently learned what the word “design” meant, despite having done a lot of design. For me, I saw the act of starting a company was also about asking the question, “Why now?”, there are so many “Why now?” answers you can give, it can be societal change, cultural, technological, regulatory. But we were technologists at our core, so we made a big long list of all the technologies that were changing at the time and gradually crossed each one off, we came up with two finalists. One was drones, this is the end of 2011, the other one was WebGL. I think we would have totally failed at drones anyway, it’s extremely hard. You look at Zipline , BRINC — these are amazing companies, and you really have to chew glass to get through that, we wanted to do something where we felt we had a technological edge and insight others did not. And what was the technical edge and insight about WebGL? This is obviously the foundation of Figma — you can do incredible graphical things in the browser, which to that point had all been on dedicated desktop applications. What was the insight that made you think this might be possible, even if it was just barely possible? DF: To be clear, right after applying for the Thiel Fellowship with the drones idea, I ended up working at Flipboard as a design intern, using design programs all day long. We had this hammer with WebGL looking for a nail, we didn’t find the, “Let’s go build design environments and help designers”, for a while, it took a little bit. What was exciting was that Evan had done a lot of early work that proved out that WebGL was way more capable than anyone else was thinking at the time. Other folks then were going, “WebGL is this weird toy that Mozilla is making, it’s probably not as important as just using your local, non-browser tech”. Right, if you use an application that can actually leverage regular OpenGL and your GPU, why a browser? DF: Exactly. The only other company that seemed on to it at the time was Onshape , actually. We looked around and went, “These guys get it”, and pretty much no one else did yet, no one took it seriously. So due to Evan’s work, we started to really explore that and go, “How can we take tools that people expect to be desktop-bound and local, bring them to the browser, and do it collaboratively too?”. We were very inspired by Google Wave — rest in peace, it was a really cool product. I grew up in Google Docs, playing MMOs and stuff like that, so I think our frame of reference, even if we couldn’t articulate it then, was just different — obviously the browser enables all of that. You viewed the browser as a first-class operating environment in a way that probably older people did not. DF: Yeah, exactly. In the early days of Figma I’d say, “Just like Google Docs”, and a lot of people were like, “Yeah, well, I use Word — why would I use Google Docs?”, and I was like, “Well, I’ve only used Google Docs my entire life”. And then, “Well, I guess there was that time in middle school…”, and they’re going, “Wait, how young are you?”. Well, let’s talk about what Figma is. I’ve written about Figma in contrast to Sketch , which is more of a single-player experience — this idea that Adobe left this huge window open for actually designing apps. Mobile apps come along in particular, an exploding market, actually placing all the screens, how it all flows together, they didn’t have a product for that. Sketch comes in and fills that gap, but it’s still an application on your computer, and you’re saving files that are v1, v2, v5000. Figma, by virtue of being in the browser, got collaboration for free — it’s a multiplayer experience. When did that possibility become clear? You mention the collaboration aspects, but as I understand it, you were trying to get WebGL to work first, and then realized this is good for collaboration. Is that the right sequence, or did you have the benefit of being in the browser — meaning multiple people could work on something at the same time — all along? DF: I would say from day zero, Evan and I were talking about it, and we were both trying to be very rational. On collaboration, we wanted to talk with users and see, “Do they need it?”, and basically everyone said, “Not only do we not need it, we don’t want it”. Right, there was a lot of asking jockeys if they wanted cars. DF: Well, I think it was more an identity thing of, “I’m a designer”, and there was a lot of agency influence on the design process at that time — this kind of grand reveal where you just work in the corner. Oh yeah, you own it, it’s on your computer, you’re doing it, and then you go into the meeting and show it. DF: No one sees it until it’s perfectly ready, then you show a few results, maybe give them three, the first two are kind of not what you want, but the third, “Oh, the contrast is so great”, and everyone goes with it. So that agency mindset and identity, as well as imposter syndrome, honestly, because design was just emerging from this phase where people saw it as, “Make it pretty”, versus, “Make it work”. This is a key element of how we build product, build software, do media and advertising, and people were just starting to appreciate it with all the Apple ethos of the time and great consumer products coming out. So we had the insight from the start, but it took us a while. Eventually, as we built it out and started fully using Figma to build and design Figma, it was immediately clear there was no way we could launch without collaboration, because it just felt wrong. If you’re in Figma and I share a doc with you, a link, and you’re in it too, and I make a change and your browser force-reloads, and you make a change and my browser force-reloads, it sucks. So it was a, “We have to do this thing”, and it was not trivial at the time — it took quite a long time to build out. Evan was a key part of that, as he was with a lot of our foundational technology, it was a key condition for our launch in 2016. Is it ironic that Apple sort of created the conditions for you in raising the stature of design and that being the controlling factor in development, even as their whole tech approach is counter to you, not really supporting WebGL, being all-in on applications? It’s kind of interesting. DF: I don’t think Apple’s tech approach is counter to us at this point. At this point. But they were all-in on, “You use apps, that’s what they’re for”, this idea that you’re going to collaborate on the web — I’m not saying they hurt you, I’m just saying there’s a reason Figma only worked in Chrome for a long time, for example. DF: Apple reasonably was concerned about battery and device performance, and took a very vertical approach as they do with everything, and also was patient — just like we’re seeing now with them. When it became the right time, they added in collaboration to many other surfaces and figured out how to make it work with the cloud but I think they showed the importance of design to the world in a way that had never been so vocal before, and it raised the level of the conversation. You could argue Microsoft at the same point was also really leaning into design, but they weren’t as vocal — they didn’t have Steve Jobs talking about “Design, design, design”, they had “Developers, developers, developers”, it’s just a different tune. Yeah, that’s interesting. Is there any context, looking back now, where Figma makes sense for one person? Or is it really a product that only makes sense if you view it in this context of collaboration? DF: A ton of people that use Figma use it individually, and I think it’s critical that you build tools that work for someone individually, that they can then graduate into a collaborative stance and use with their team. But you have to get the single-player experience right and then let it evolve to multiplayer. So when you started going to market, what was your selling point? The tool itself, the accessibility, or was collaboration the key from the get-go? DF: When we first did our closed beta, multiplayer collaboration didn’t yet exist in the product. It did have sharing, and that was very powerful — you had this one space to view your designs with your team, and people were doing that in very team-oriented ways. But early on, things like our improvements on vectors, or the simplicity and quality of Figma, were more the differentiators — and then design systems with a unique component approach, and then multiplayer, and then many other things. We also got a lot of minimalists in our early user base — folks who believe in the cloud and believed in minimalism, because we didn’t have all the features. It was interesting just to see that early base of users and how successful they were — two of our earliest customers were Coda and Notion — just kind of wild that those were two of the first customers we had. I don’t even think Shishir [Mehrotra] at Coda knew that at the time — I once brought him in to talk with the team about platform strategy stuff, and I mentioned this offhand as an intro comment, and he’s like, “I was what?”, so it was a fun group to be around. How much do you think Figma has evolved with your customer base, as opposed to Figma actually influencing your customer base and how they evolve? Did your customer base naturally become collaborative and realize they needed Figma, or did Figma introduce them to working in a more collaborative manner that they hadn’t considered because the tools weren’t there? DF: There was definitely a period of adaptation, some people got it right away, for others it was over time. Our first big marketing moment — I remember there was a site, Designer News, sadly I think it’s offline now, and there was a comment on the launch thread, “If this is the future of design, I’m changing careers”, or someone said, “A camel is a horse designed by a committee”. But we went deep on anyone who had really positive or really negative sentiment around Figma — great, let’s learn from all of it and adapt as we need to, while also having our own points of view and pushing for them. Customers have always been inspiring to us, we’ve tried to take feedback from everywhere — support tickets, in-person conversations, formal research, sales, social media — for a while, social media was a great signal, it’s not as good a signal as it once was. Our user forums, everything, and data analytics. As you get there, you form a picture or view of the world, you play anthropologist and understand what people truly need and sometimes the moment just changes. FigJam , for example, was a product we introduced right after the pandemic started, I’d always wanted to make a whiteboarding and diagramming product — I saw that use case in the wild, it was significant, I felt we could make a simpler tool. But rightfully, the team was skeptical, always going, “Is this the right time? We have a lot of other stuff to do to make Figma great”, that debate stopped with the pandemic, when our user base wrote in en masse and said, “Please, please give us this product”. We need a whiteboard, yeah. DF: Yeah. We started seeing that use case everywhere — people treating Figma like a shared space and the shared-space part of Figma is something we’re doubling down on. Was that the real turning point, “This is where work is done”? I’ve called Figma the operating system of design , in that everything sits on top of it and below it, but it’s the common layer, does that resonate? Is that the moment that became much more real? DF: It was happening already in many ways, we were doing it ourselves, seeing it with our customers, but the pandemic is when everyone started telling us, vocally, “Lean into this”. There’s so much more that’s possible now as we bring more mediums to the Canvas , more expression to the Canvas, and let people truly get what’s in their heads onto one shared Canvas — to collaborate, but also riff, see a bird’s-eye view, and directly manipulate. AI is great, prompting is great, you should be able to do it in Figma — and you can now, with our agent , but you can’t filter all of creation through the lens of AI. If you have an idea, or many ideas in your head, you need to get them out directly too and also you have to iterate to get to an exploratory place. Too much emphasis right now is put on “I’m working with the AI, the AI wants to go a certain direction, and I’m going along with it”, it’s almost like, “Is the AI using you, or are you using the AI?” — sometimes it’s unclear. AI is a tool people can direct and work with, it can resolve tedium, but you also have to push, you have to be the out-of-distribution force, because AI is trained on the distribution, and the most interesting, differentiated work will be out of distribution by definition. So I have questions about that, I have questions about AI, and questions about Canvas, which is a big focus of what you’re talking about at Config this week. But I want to do a quick side tour, because I must, another very famous single-player design company, as I mentioned, is Adobe. The Adobe acquisition was announced in September 2022. I’d written — we don’t have to spend too much time on this, obviously it didn’t happen, so in some respects it’s not that important — but by that point— DF: Yeah, but it felt like it didn’t happen for a long time, those 16 months felt like an eternity. That’s right, which I do want to ask you about, get your point of view on. But one thing I’m curious about, I actually remember where I was when this happened, I’d written several times at that point about generative AI, particularly images , the AI question loomed very large to me when that news came out. But that was still a few months before ChatGPT had launched, so this was more burbling under the surface. To what extent was AI part of the Adobe conversation? There’s a very plausible story that it wasn’t part of the conversation at all — you were the operating system for design, the operating system can disintermediate all the products that sit on top of it, which from Adobe’s perspective was a strategic problem. They had a huge hole in this space, Sketch had already taken that whole space on the single-player level, so I thought it was an obvious acquisition for Adobe, aside from all the AI stuff, just looking backwards. Which interpretation is correct? DF: Probably both. I think Adobe was super excited about AI and understood its potential and importance, we had plenty of conversation about that, but it was not, I think, the impetus or driving factor for me though in making the call of, “Do we sell or not?”. I had no idea, would AI would 1/10th, or 10x, or 100x our business? I was in my head trying to play it all out, and as we’ve seen, it’s hard to play these things out. You kind of know what’s coming, but knowing when it’s coming, and the second-, third-, and fourth-order effects — that’s hard. And this is pre-ChatGPT, so imagine trying to play out the next five, six, seven years from that point, that made me much more receptive to a conversation. That makes total sense. For Adobe, I don’t think it was the controlling factor — again, you just made tons of strategic sense for them. But for you, it’s like, “$20 billion is very certain and everything else is very uncertain”, that makes a lot of sense. DF: Another contributing factor was that I was excited about the opportunity to think about Adobe’s Creative Suite from first principles, and go back to the user’s problems. Yeah — it’s missing the layer that Figma provides, the thing that actually ties it all together. DF: There’s so much expectation from users of any software that’s been around a long time. There’s a need that reinforces itself to “Add, add, add”, versus thinking, “Okay, we’ve learned a lot — how do we reinvent from the start and think about things in a new paradigm?”. Looking back now, AI is clearly going to be — and already is — a tailwind for our business, it’s TAM-expansive in huge ways I probably never anticipated at the time, it’s also interesting from the Adobe frame, because I’d challenge the way you framed it earlier. DF: Adobe acquired Macromedia , and through that got Fireworks — and Fireworks was really the predecessor to Figma and Sketch, but not a focus for Adobe. They had different Labs projects, but this was not their core, their core was creativity — for Figma, our core has always been design, those were different when the Adobe conversations were happening. Explain that, because I think I see what you’re saying, but people would usually conflate them — creativity and design. DF: The even bigger question, for the philosophers and art-theory folks, is, “What’s design?”, “What’s art?”, how do you differentiate design versus art? It’s muddy, but design has an aspect of problem-solving, it also has creativity. Art, I think, is a lot of things — you can get endless definitions of design and art — but I think of it as trying to take an emotion, idea, or concept and communicate it to someone in a way that really affects them. That’s not best framed as problem-solving, whereas design is. How about this definition: art is an expression that it’s meant to be consumed by the end user, and design is meant to serve the end user. DF: Well, I don’t even know if you should define art as being for an end user. Yeah, good point. DF: For me, one of the definitions I lean on is that design is where problem-solving meets creativity. Figma has always had people using the platform for creative use cases. But now you fast-forward to 2026, and design, creativity, media, in some ways art and in some ways not, and advertising — it’s all kind of merging together, it’s all one thing in a way I wouldn’t even have said in 2025. If you believe we’re in an attention economy — you experience this every day — and you believe you have to have a differentiated voice and really have a point of view in your work to stand out, and you think the way people judge software is the design, that’s the differentiator, but you also have to grab someone’s attention, design and brand are so connected. It’s all really coming together in such an interesting way, because of these second-order effects of more creation happening in the first place. A phrase you’ve mentioned, you said it earlier in this conversation, you’ve said it plenty of times elsewhere, is that AI draws from the middle of the distribution, and to be differentiated you need to be at the tails. That makes sense, but it’s funny because it conflicts with — go back to that user comment that’s deleted from the Internet, “Collaboration is the death of design”, do you see any tensions there? You talk about Adobe, creativity, tied to single-player, the genius of one person, versus, “We’re a group of people collaborating to get a design out the door”. How does that not end up in the middle of the distribution too? DF: It’s more of a mindset thing for any design team are they trying to do the safe thing, are they tryigng to go for the least common denominator where everyone agrees it’s a good idea? Or are they trying to be daring and bold and take risk? What we’re going to see over the coming years is the market rewarding the risk-takers. And I wouldn’t say it’s enough to be at the tail of the distribution — I think you have to be out of distribution. Is that possible? Aren’t you on the very edges of the tail? Fair enough. DF: I think every email I get from your mailing list is out of distribution. Well, thank you. I appreciate it. DF: If you can get one of the AI systems to replicate your judgment and framework-building, I would love to see it. I would both love to see it and hate to see it, so I guess it cuts both ways. DF: Sure, I might love to see it in terms of wanting to know how you did it. Well, it’s interesting for you, obviously. You mentioned a few minutes ago that AI is a tailwind for your business, I think it’s safe to say the stock market by and large does not agree with that, yet you’re there producing incredible results — you had a great quarter last quarter , your biggest beat yet. Do you feel you’re in the middle of trying to prove a negative here? What are the drivers of your business? Do you have some sympathy for the people in the market who are skeptical of you, or do they just not get it? DF: Markets typically have a narrative they’re attached to, and the narrative can shift — and maybe it’s still not the nuanced narrative that matters, but this happens all the time. Markets are so impressive as a force, and I just don’t think it’s worthwhile to try to argue with a market narrative. Are they normal distributions, and you’re trying to operate outside the distribution? DF: (laughing) I like that frame. I just think that you show up, you do great work, you focus on the inputs, you educate to make sure people understand, and eventually that’s either appreciated or not, depending on how the narrative is going. Right now the narrative is one of AI winners and AI losers, I don’t even think that’s nuanced enough, if I think more globally about software, there are many software companies and strategies that will work that are not necessarily companies and strategies that people would necessarily call AI winners today. I think about network effects. Are you a network effects business? DF: Collaboration definitely has properties similar to network effects, so in some ways, yes. And if you look at network effects not just in the social sense between people but also for marketplace liquidity — that is absolutely a network effect in itself, just to have liquidity in a marketplace, I would say that’s an AI winner. If you look at the long tail of customers that are non-technical — I invest in companies occasionally, and one of them is Ambrook , an accounting-for-farmers company. I don’t think a lot of people in ag [agriculture] will be vibe-coding their taxes, they’ll care very much to have a human in the loop, for the certainty that this part of their business is going well and they don’t have to worry about it. I really believe Ambrook can provide a phenomenal solution there. I also think liquidity of data matters — you need equity of data to create context, and context creates capability, if that’s self-reinforcing, you can get to a place where you have a virtuous flywheel that really helps in the age of AI. Explain this in the context of Figma specifically, why does this provide a tailwind for you? DF: I won’t go too deep, since it’s strategy, but the more activity people do in Figma, the more we can, with their permission, understand their needs and serve them better with capabilities. If we do that right, that’s a way to continually improve the experience for the customer and make it so they can do even better work, faster, in Figma. How are you thinking about the models that undergird your various AI offerings? DF: You always want to be in a place where models are swappable. We’re in an explosive, wild period of models constantly shipping, I went to bed last night and saw Sakana’s new release — I haven’t played with it yet, recording on Monday June 22nd just for reference. I didn’t expect that, coming out with their ultra model and their approach and just seeing the progress these labs are making, sometimes in a discontinuous way, is incredible. Right now we use a range of models and do some stuff first-party— And these would be based on open-weights models? DF: Some on open weights, some on very small things we’ve worked on. Overall, I think that there’s a big story around local inference that will happen in the future, as well as open weights and different models are good at different things, it’s incredible. Is it fair to step back and say — from your perspective, which echoes a Microsoft perspective , or lots of other companies in a similar position — yes, models have to be swappable, customers don’t want to be locked in, but there’s also a self-interest position, you need to keep this data to understand customers better, and you need to not be giving that data to the models, who at the frontier need to not be swappable. Do you feel they have no choice but to come up into your space? Is there a perspective where Claude Design comes out and it’s like, “Yeah, of course that’s coming, because they have to own the consumer”? DF: I think if you look at Anthropic right now — it echoes what we’ve seen from OpenAI over the past year, where there was a period when OpenAI was just building and releasing stuff in every area. And they, to their credit, have pivoted hard, made some hard calls, pulling back on Sora . That’s not an easy call after you do deals with major media players and have a huge launch and people are really enjoying the product, Sora was really cool, but going all in on code seems to be the right move for them right now, and it’s very respectable that they’re doing it. Anthropic’s going through a similar pattern, we’ll see what lasts and what ends up persisting. That’s an interesting way to think about it. Did you feel pretty betrayed about the design thing — particularly when one of their executives was on your board ? DF: It’s complicated. Let’s put it that way. Fair enough. I think it’s one of those things you could definitely see it coming. Tell me about Config. One of the products you’re going to announce is Code on the Canvas , tell me about that, and how it fits into the overall way you’re thinking about AI. DF: Maybe to frame it up to start and dispel some of the stuff out there in terms of the way people talk about this — people on social media love to frame the “versus”, they’re always talking about code versus design, like they’re two different things. To me, the work is not just vectors — it’s vectors, images, prototyping code, because you don’t always want to work in production, and production code, and production code needs to be across all your surfaces, web, desktop, all your mobile devices, new screen types, etc. All of that is relevant to your process, and all that process is design. So it’s super important to see it all as an “and” rather than a “versus”, I just want to make that clear because otherwise nothing else will make sense to folks. If you think about it as an “and” and go all the way into what that means, then basically what you end up with is, “How do you bring these different mediums, these different materials, together in one place where it’s easy to go back and forth and get the benefits of each?”. For design representations like vectors and images, I think there are many ways those are very helpful — especially vector-based formats, for direct manipulation and precise control, in ways that code, which is structured, is not as easy to manipulate and mold. But code is also incredible, it’s got expressivity, full fidelity, it acts the way it will in production — hopefully, a prototype might differ from production — and you can have state and logic but you’ve really got to bring these things together. So what we’re doing, based on the work we’ve done on Make , either from Make or by creating on the canvas yourself with code — essentially a code layer. You can have Code on the Canvas that pulls in from design if you want, and go right back to design — make changes and reconcile them back to code. We’re trying to make that all work seamlessly together, so you have a breadth of exploration while also having the collaborative aspects of the canvas and that bird’s-eye view. Is one way to think about this that the question is that you can you eat development before development tools eat you? DF: I think less that way, because my conceptualization of the moment we’re in is one that people are so eager to try so many different tools and materials — in some cases we’re going to be the best place to use those materials, in Figma, in other cases you’ll want to go elsewhere — and you might even want to come back to Figma afterward. I’ve been thinking about this, the vibe-coding stuff is amazing, particularly in its ability to build scaffolding and get the functionality of an app and the user experience these tools build is hilariously horrible — it’s so bad, you really have to put much more of a heavy hand on it. When you talk about a phrase you’ve been saying regularly — that when execution is cheap, design and creativity are the edge, that’s very resonant to me in that actually conveying properly to the AI what you want is still a difficult challenge without it over-interpreting and over-assuming and spitting out a UI that makes no sense, and the design’s not just wrong at a pixel level, it’s wrong at a conceptual level. I guess the question I have, and what I think you’re getting at with Code in the Canvas, correct me if I’m wrong — is that you guys owned the handoff between designers and developers where Figma was the common level where you could communicate back and forth, what’s happening, how it’s working. To some extent, if the developers are doomed, God bless them, designers rule the world — but did you accidentally erase your whole point of differentiation, which is owning that handoff between those two pieces? I don’t know if that makes sense, but it’s an angle I’ve been thinking about here. DF: I don’t think developers are doomed, and I do think designers will rule the world. (laughing) Both can be true! DF: But I need to go all the way back for a second, when we started Figma, the first five years or so in market, a big part of our story, but also the ecosystem around us, was prototyping. And prototyping was not always with code, some companies tried that approach, but it didn’t really work at the time, because despite all the debate of, “Should designers code?” — debates that happen every year or two on Design Twitter, we would constantly see that designers did not all want to learn or take the time to code. Now we’re in a world where it’s easier for designers to put their ideas into code. If you look at the prototyping aspect alone, in the Canvas, whether you’re working with production materials or prototyping, you need to be able to riff and explore and try things, and design representations are just one part of that, so is code. We’re also doing more launches at Config that add to that story. Motion, for example . Yep, huge focus on this. You bought Weavy now you’re calling it Weave . DF: Weavy, and now Weave, yeah. I love talking about Weave , it’s so cool. But Motion is actually coming from a hybrid of Figmates and a team we acquired called Modyfi . It’s something folks have always wanted — a timeline they can use in the Canvas and of course the challenge is how to do that in a way that doesn’t get in your way if you’re not trying to do Motion work. I think we’ve done a great job balancing those tradeoffs while providing a really powerful motion tool that’s much more intuitive than other approaches of the past and it’ll allow people go far more into expression, because it’s very hard to prompt and say, “I want the curve of the animation to be exactly like this”, the work we’re seeing folks do, even internally, with this motion tool is so incredible — I’m just totally wowed. We’re also going hard on shaders , going all the way back to the WebGL conversation. It’s ironic, we were built with shaders all this time, but we didn’t give people using Figma the power to express in shaders. Now you can add shader fills and effects, and that unlocks a parametric option space to really explore this whole universe of effects, images, fills, and properties — and that’s even before interactive shaders, which add a whole new dimension, that’ll come soon. We’re excited to bring all these materials to the Canvas so people can fully express and explore. And yes, if we do it right, it’ll be something they can then push to production — whether that’s pulling from Figma via an MCP , or more in the future, connecting to your codebase. We’re doing that with Make local right now, but we have much more to prove out there. I’m curious about that, because how do you think about customer acquisition? Back in the day you’d imagine starting, “Oh, Figma, this tool I’ve heard about, I’m going to make a design, and now I’m going to find a developer to code it”, now people can just get started with a ChatGPT or a Claude, and then it’s like, “Oh, this is really hard to design UI elements”, how do I back into something? How do you make sure you’re there if people are starting with coding in a way they maybe didn’t previously? DF: I see people starting everywhere — that includes Figma, but also all sorts of other tools and places, and I see them ending everywhere. I see them ending in Figma to do the final iteration, ending in LLMs or other services. What I think is essential for us right now is providing enough value always that the path to a great product is through Figma. Yes, optimally you can do that entire path through Figma as well, that’s a standard we should hold ourselves to. But we’ll continue to see people use a range of tools for a while, because these models are so underexplored. If we were to pause all development on models, a total moratorium, I think you’ve got like five years of catch-up on the application layer before the capabilities are understood and expressed through software. Every time I use these models, I find new capabilities. Even there, though, is still the key for Figma is that it’s still the place people can work together? And that’s something AI hasn’t really solved , it’s kind of a one-on-one experience, but you need to figure out how groups can get jobs done. DF: One area is groups working together to converge, I think groups coming together to diverge is also really important. Teams being able to work in all sorts of ways in the future is critical and also what are the things you’re always going to want as a team that are fixed, and what are your degrees of freedom? There’s so much we can lean into on collaboration in ways we’ve never been able to before, and make that single-player experience even better — because if we land all that together, you’ve got the collaborative layer, but also Figma is the place where you can just make anything you want. That sort of leads to my question, which is, is the real Figma danger not that AI becomes multiplayer, but that individuals with AI disrupt multiplayer companies? And that’s why you still have to be relevant to the individual as well. DF: I think it’s kind of a dark future if that happens, it’s one where folks are probably feeling pretty lonely — it’s also one where the tunnel vision you have when you’re building with AI is really becoming a problem for teams, I’m hearing this from design leaders everywhere. There are different phases of AI adoption at these companies, the first phase is often, “We’ve got to use AI, let’s figure that out”, the second is like token-maxxing leaderboards — some extreme behaviors. The third, after they get people to adopt, is often “Okay, here’s your token budget”. In that second phase especially, where people go really wild with AI, it’s hard to get them to change their behavior after. A lot of people have this total tunnel vision of, “I’m building this one thing”, and they get really attached to it. That’s the opposite of the breadth of what a great design process offers. If you’re going through the design process, it’s not that you should slow down necessarily, but you should go broader, and you should think. It’s essential that you actually think — not just wear a thinking cap, you need to be able work through yourself and have a mental model not only of the user and the experience you’re creating, but also cultural impacts, the broader system you exist in, what the user is expecting, all sorts of things. Going fast in the wrong direction is not progress, it’s a dead end, and it’s even worse if you’re collaborating, trying to bring five designers together and each one is viscerally attached to their one direction — now you’ve got design gridlock and you’re talking past each other. So it’s imperative that we move away from this tunnel vision and toward the openness the Canvas represents. Maybe there are other ways too, but we’ve got to get away from tunnel vision. On a personal level, how much do you feel constrained by the path dependency of having already built Figma? If you started out tinkering with tech as a kid, or even with the WebGL stuff, you ended up with a company. Do you ever have a part of you that’s like, “I’d just like to tinker with this tech again and not worry about whether it’s an existential crisis for this huge company I built”? DF: I’m constantly tinkering. It’s my antidote to the non-verifiability of design — because there are verifiable domains and non-verifiable domains. Design is taste, culture, aesthetic, it’s constantly shifting, user experience is something designers can argue about in design crit for as many hours as you give them. Unverifiability is the moat — that’s a good metric. The more something’s been argued about on the Internet, the longer a future it probably has. DF: (laughing) The more you’re oriented toward questions than answers, I think it’s a good sign — it’s going to be harder for models to achieve it in a way that’s high-craft. And as a builder of Figma, that’s where the complexity and the interesting parts lie. The word of the year — not just this year, but 2025 as well — is evals, evals, evals. But how do you write the right evals for non-verifiability? Aren’t evals, in some respects, counter to taste? DF: Depends on how you do them, and who’s writing them, there are ways. It’s hard for LLMs to do well on aesthetics and user experience, like you said, and being surrounded by non-verifiability — when I go home and I’m finally unwinding at 11 o’clock, about to go to sleep, I’m not reaching for Netflix, I’m reaching for some model, and I’m exploring verifiable tasks, actually. Because I want to push the models on the unverifiable side we talked about all day long, but what can we do where it’s really verifiable and they have spiking capabilities? Like vibe-mathing, for example, which oddly creates empathy for our vibe-coders. Because I vibe-math, and as someone who never went as far as I wanted to in pure math and wasn’t as good as others, I don’t know all the concepts the LLMs might be spitting out at me, so I have to learn as fast as I can — which is not fast enough, because the LLM is going through all sorts of stuff. It’s a great tool for learning, and super fun for discovery. And looking at the internals of models, how they work, understanding what you can and can’t determine, is also extremely interesting. It’s all applicable in weird ways to Figma — you never know how. Even early stuff I did around understanding how to get models to have a broader range of outputs, and prompting strategies, I don’t think there’s one definition of the word “jailbreak”, but the things that got the models to open up more, exploring that direction, has really led me to understand models better, which benefits Figma in weird ways. It’s super interesting. We didn’t get too much into the aftermath of Adobe, or the IPO, that sort of thing — but you talk about unverifiability and uncertainty, and that’s been the Figma story often, through things outside your control. It’s been interesting to observe, it really is quite an adventure of a company in many respects, really a unicorn. DF: It’s been a blast, continues to be, and with the world shifting quickly, you can see it as chaos, or as opportunity — or both. Are you glad you’re independent, or do you kind of wish… DF: Oh, at this moment I’m very glad to be independent, we need to operate at such a speed and be able to pivot so quickly to make sure we update our priors. Like the opposite of how you started, right? You started out with a two-year slog to even get this working. DF: Totally. It’s so important now to constantly adjust as an org and make sure our processes support that, there are tons of things to do to improve there. But when people come to Config — which will be, as of the time this is released, I think happened yesterday, time’s weird on podcasts — I’m so excited. It’s going to be 10,000 designers in one place, and I get to spend time with the community and show them the stuff we’ve been working on. I think they’re going to love it and there’s tons more we’re working on, so stay tuned. Very good. Dylan Field, nice to talk to you. DF: Thank you for having me. This Daily Update Interview is also available as a podcast. To receive it in your podcast player, visit Stratechery . The Daily Update is intended for a single recipient, but occasional forwarding is totally fine! If you would like to order multiple subscriptions for your team with a group discount (minimum 5), please contact me directly. Thanks for being a supporter, and have a great day!

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