The $10 Trillion Token Economy — Alex Atallah, OpenRouter & Anjney Midha, AMP

Summary

• Anjney frames the Stripe–OpenRouter deal as a security story, not merely a payments one. His thesis: tokens are “a new type of unit of value being streamed across the internet,” with the token economy reaching roughly $5 trillion in five years and a roughly $10 trillion token-flow figure over ten years. Alex says OpenRouter blocked 10× as much dollar volume last month as the month before. Anjney’s analogy is Stripe Radar: “Stripe really is a security company today,” and the next wave is agentic — “bad behavior perpetuated by AI agents is about to hit us like a tsunami.”
• The “just a wrapper” dismissal was the mispricing of the cycle. VCs called OpenRouter “just a marketplace… just a thin layer, just a proxy”; Anjney refused to re-educate them, invested, and, he thinks, about a month later Matt Murphy marked it up by 10× at Menlo. The other big objection — “big model wins” via scaling laws — was challenged by Alex, who argued that a Google-level LLM monopoly would be “the Dutch East India Company times a quadrillion in magnitude,” while the economics of creating competitors are more decentralizable.
• Frontier labs are structurally weak at distribution, which is OpenRouter’s moat. The first Claude checkpoint was completed a year before release; the Claude 1 blog post’s three developer examples were friends of the team (a Discord bot, Anjney’s wife’s startup Juni Learning, and Notion), and Anthropic took more than 12 months to its first $10 million in revenue — versus OpenRouter telling Black Forest Labs, “the day you launch, we can send a million developers to you.” Today the platform has over 10 million developers; Mistral’s first release, by contrast, was a torrent magnet link with no API.
• The growth inflections map to model launches — and OpenClaw was the latest step-function. Prose apps dominated until Claude Sonnet 3.5’s mid-2024 coding leap, then a repeating swing: frontier launch → invoice shock 30 days later → open-weights alternative about three months later. End-2025’s OpenClaw brought non-developer users and a heartbeat architecture (“you don’t want to pay a lot for a heartbeat”) that made the auto-router rocket; OpenRouter now does roughly 10 trillion tokens a day, growing about 9% week over week.
• Alex’s forward call: generalized inference resale is likely to give way to discrete tasks. Inference gateways are fraud magnets, and pressure from labs and inference providers to “do a commit and then bring your inference elsewhere” will be high. Companies may therefore shift to discrete intelligence tasks, Datadog-style event pricing, and bring-your-own-inference, which OpenRouter can support.
• Fusion is a documented change of mind. The early-2024 “mixture of models” prototype was deleted because the fused result was sometimes worse than the best model — the frontier leader was too far ahead. By 2026 the top three or four models had gotten closer together, while remaining notably divergent, and when every model judged Alex’s fused architecture plan better than the individual outputs, OpenRouter rebuilt it as Fusion.
• Focus is the shared operating doctrine. Alex says “even in the age of AI, focus is underrated.” Anjney’s proof: Anthropic started “$10 billion behind OpenAI” with a day-one seed memo to “responsibly commercialize an AI pair programmer,” excluding shiny image and video detours, and became “a trillion-dollar company within 5 years.”

Deep dive

1. Pub-sub products and the $600 Alpaca moment

• Alex’s early-2023 framing, resurfaced by swyx: products sit at the intersection of publishing and subscribing to data, and marketplaces are pub-sub with SKUs — but “humans consume in a very discrete, ad hoc way,” while agents and inference consumers “are consuming continuously and they’re changing the SKUs that they consume from all the time.” OpenRouter is therefore “a blend between a normal API experience and a marketplace”: model slugs and the auto-router as continuously subscribed SKUs.
• The founding trigger was Alpaca: at the end of 2022, OpenAI was “the only game in town”; when Llama came out in January 2023, it outperformed GPT-3 on one or two benchmarks but “wasn’t actually an engaging model.” Then a Stanford team spent $600 on synthetic-data fine-tuning, and Alex, using it on an airplane, “could not discern a ChatGPT versus an Alpaca result.” Conclusion: “we have a whole new way of monetizing data for the first time” — compress your data into a model and sell it — implying an immediate ecosystem that needed a marketplace.
• There was no home base for LLMs; Hugging Face was closest but lacked closed-source models, usable inference at the time, and data on who was using what and why — “those differences felt really critical to me.”

2. Discord’s moderation refusals sold Anjney on open weights

• As Discord’s VP of platform, overseeing 250 million monthly active users and early GPT-3.5 access, Anjney’s use case was almost in-context moderation: feed each server’s custom norms to the LLM instead of relying entirely on a 5,000-plus-person outsourced moderation team. But post-training guardrails kept refusing — a Harry Potter fan community’s moderation was refused over trademark rules; a detective-story chapter with a killing got blocked mid-draft.
• Discord asked OpenAI for the weights for reliability at scale; the answer was: “Sorry, guys. That’s not how this works. We’re a closed-source company.” That was Anjney’s realization that enterprises “would need some kind of control plane or management system to orchestrate these open models.” About six months later Llama arrived; six months after that he led Mistral’s Series A. On hearing of OpenRouter’s launch, he thought: “These worlds are going to collide.”
• Alex drew the same lesson from the user side: “There must be some choice out there so that I can switch to another model” when refused — “that tension also drove me for a marketplace.”

3. “Big model wins” and “just a wrapper” — the objections that aged badly

• The biggest fundraising objection was that scaling laws plus network effects would produce a Google-style monopoly and “you’ll just be fighting for scraps.” Alex pushed back: LLMs are not merely a user interface but ways of building entirely new businesses, so a winner-take-all outcome would be “the Dutch East India Company times a quadrillion in magnitude” — and is less likely anyway because good competitors are “much more decentralizable” to create.
• Anjney inverted the objection entirely: “The scaling laws were never, in my mind, a bug. They were always a feature” for why OpenRouter would be valuable. As an early Anthropic investor who had also invested in Mistral, Black Forest Labs, and Luma, he already saw a multi-model ecosystem emerging.
• His frustration with peers — “just a marketplace… just a thin layer, just a proxy… a wrapper” — got the response: “I don’t have time to debate you. We’re just going to invest.” About a month later, Matt Murphy at Menlo Ventures marked it up by 10×. swyx’s wry addition: “It’s all wrappers, all the way down to bare metal.”

4. Labs train checkpoints, then crickets — distribution is the gap

• Anjney’s pattern across the labs he was funding: research teams think in capabilities, spend “sometimes billions of dollars on training,” and then “a checkpoint would be done and there’d be crickets.” The first Claude checkpoint was completed a year before release; the Claude 1 blog post’s three developer examples — a Discord bot, Anjney’s wife’s startup Juni Learning, and Notion — were all friends of the team. Anthropic took more than 12 months to reach its first $10 million in revenue.
• Contrast: Google DeepMind can “deploy a new checkpoint to a billion devices” overnight, while OpenRouter could tell BFL, “the day you launch, we can send a million developers to you.” Today Alex counts over 10 million developers on the platform. Mistral’s first checkpoint went out as torrent weights — “they just put up a magnet link,” with no API.
• Alex’s marketing metaphor: the model market is “a big, dark room” where users feel around for black-box products — “models are not products where you can just enumerate all their features onto a web page” — and the company shining the light must be a neutral third party.

5. Crypto was the dress rehearsal; Discord was the petri dish

• The Axie Infinity server — where Alex said about 10% of the Philippines was present, with several billion dollars in NFT volume running through Discord and coming from OpenSea transactions — taught both men the community flywheel: “users sharing links is a really clear indicator that something important is going on.”
• Midjourney’s David Holz, an Anjney friend from Magic: The Gathering, proved the model. The standalone web app had “terrible retention” because a blank canvas “was paralyzing” for people who had never used AI, but inside a Discord server users copied each other’s prompts and reached about 10 million monthly active users. The path from launch to a $100 million revenue run rate was less than eight months. The best-of-four image picker was the feedback loop — “the RLHF feedback loop.”
• Anjney’s hindsight: “Crypto ended up being kind of like a dress rehearsal for generative models” — except nobody ever had to ask AI’s use case; “I can create anything I can imagine.” When Stable Diffusion launched, “now other people can build their own Midjourney,” but they needed an API rather than having to host the weights themselves. OpenRouter supplied the kind of distribution and infrastructure that this created demand for.
• Why OpenRouter itself is not Discord-centric, per Alex: LLMs need codebase integration, governance, and data policies — “a lot more than a Discord server can provide.”

6. Window AI, the co-founder from GitHub, and the iframe that impressed Stripe

• Before OpenRouter, Alex built Window AI, a Chrome extension on Plasmo exploring a MetaMask-for-AI, bring-your-own-model pattern — “not the right form factor,” but the learning was that this had to be an API with a discovery surface: “I need graphs… I need to be able to explore both as a human and as an agent.” Plasmo’s creator, Louis Vichy, started contributing code and became OpenRouter’s co-founder.
• The character reference Anjney keeps retelling: on an OpenSea–Discord integration call, seven people were aligned on bouncing users out to OpenSea, and Alex — “the guy who doesn’t work for Discord,” whose company benefited from the bounce — alone objected and proposed an embedded iframe checkout inside Discord. “Alex put that user experience first. And I was like, that’s special.”
• His conclusion doubles as the deal logic: “Nobody should be surprised why Stripe decided they had to buy OpenRouter — it’s a really rare combination of people who understand the machine-learning community, the developer experience, and the end-user experience.”

7. Mixtral’s price war and humans as the first router

• Mixtral 8x7B was, to Alex’s knowledge, “the first time an open-weights model was called [the] best model in the world” in serious terms — hype included — and the messy inference landscape let providers compete on price, with the price falling by about 80%, per swyx. Alex called it “the first clear example of a provider marketplace working in a way that adds value to developers.”
• Anjney’s NeurIPS memory: asking Guillaume how he felt post-launch and getting, “In his typical French fashion… it’s an okay model, it’s not that good.” Guillaume’s diagnosis was that the MoE’s speed made people think it beat GPT-4 — “sometimes when they’re faster, you think they’re smarter.”
• swyx’s synthesis was that this was “the start of humans as router”: ask the fast model first, then upgrade manually on failure. swyx said he had not thought of it that way, but that it made sense; Alex connected the idea to eventual automatic routing.

8. Why Arena and OpenRouter had to be two companies — and why focus wins

• Alex’s structural answer: “One company is taking data and selling it and the other company really can’t by default.” OpenRouter cannot see prompts or completions unless an organization opts in, while Arena’s business model is oriented around the labs. Anjney, who was Arena’s first CEO for five months while helping Anastasios and Wei-Lin spin out of Berkeley as the “AI Reliability Institute,” adds that the highest-expectation customers were completely different: a post-training researcher versus a developer shipping applications.
• Alex’s doctrine: “Even in the age of AI, focus is underrated and critical” — not just for product quality but because “the world knows what your focus is” and maps problems to brands.
• Anjney’s proof case: Anthropic started “$10 billion behind OpenAI,” and its seed memo from day one was to “responsibly commercialize an AI pair programmer” — to the exclusion of shiny image and video models — yielding “a trillion-dollar company within 5 years.” swyx’s pushback that Claude Instant and Claude 2 were marketed on prose and long context drew the reply that the main internal evals were always focused on coding, including long-horizon agentic programming.

9. Roads not taken, and the resurrection of Fusion

• The unlaunched prototype was fine-tuning as a service in consumer form: feed it YouTube videos, get a model that talks like the person, aimed initially at “AI Steve Jobs.” swyx’s skeptical read was that it was “just a glorified RAG bot” and that people ultimately want to find the source video. On fine-tuning as a router-native way to export off OpenAI, Alex chose neutrality — partner with inference providers rather than compete; Anjney thought Alex was “maybe a little bit early,” because the segment is now growing extraordinarily quickly.
• MoM (“Mixture of Models”), the early-2024 fusion prototype, got its code deleted: “The fused result was a little bit worse, sometimes the same as the best model… because the best model was so far ahead of options two and three.”
• The 2026 reversal: Alex theorized that RL had “expanded the surface area of creativity” inside each lab. The top three or four models had gotten closer together, while remaining notably divergent and all capable of inserting interesting ideas. In Alex’s personal experiment, every model judged the fused architecture plan better than the individual output it had produced. “Spot-check: pretty good. We should benchmark this,” and that became Fusion.

10. From prose to OpenClaw: the leaderboard as a movie of AI

• Growth inflections track model launches: through May 2024 the focus was prose — DreamTavern was a top app, and its creator now runs product at Cognition, the company behind Devin. Then Claude Sonnet 3.5 made “an incredible leap forward in coding,” changing which apps built on OpenRouter. The recurring swing was: usage surges, users see invoices 30 days later — “Whoa, what’s going on here?” — and open-weights models deliver cost-effective options about three months later. “We saw that happen several times.”
• End of 2025 brought OpenClaw: not another IDE but “a new form factor that brought in… a productivity or internet creator… to AI for the first time,” with a heartbeat architecture pinging the chosen model to check whether it was still alive — “you don’t want to pay a lot for a heartbeat.” That made the auto-router “rocket exponentially.” Hermes followed, leaning into skills and memory management.
• Scale today: roughly 10 trillion tokens a day and about 9% week-over-week token-volume growth. Alex’s coming-of-age marker, via swyx, was Karpathy saying he no longer reads r/LocalLLaMA and instead goes to OpenRouter’s leaderboard. Per Alex, the leaderboard’s history “is kind of like a movie of how the AI space has changed over time.”

11. Stripe: new sheriffs for a $10 trillion token economy

• The fraud taxonomy Alex laid out is already industrial: stolen credit cards, terms-of-service traffic resellers, hacked and compromised companies, side-channel inference resale, runaway agents — and “we blocked 10× as much dollar volume last month as the month before.” His structural warning: “If you’re making a gateway or selling generalized inference, you are a target for fraud” — pushing the ecosystem toward discrete task pricing (“the Datadog pricing page is a good look at the future”) and bring-your-own-inference, since “the pressure from the labs… to do a commit and then bring your inference elsewhere is going to be very high.”
• Anjney’s macro frame: as online payments grew to over $1 trillion and demanded new fraud infrastructure, “over the next five years, we’re expecting the token economy to get to roughly $5 trillion,” alongside a roughly $10 trillion token-flow figure over ten years. His formative scar: Midjourney’s free trial was resold by someone in China while the company was below a $300 million annual revenue run rate. “I don’t think Midjourney has ever actually turned on the free trial since.”
• The Stripe parallel is deliberate: Stripe absorbed fraud cost as customer acquisition, launched Radar five years later, and, in Anjney’s framing, “Stripe really is a security company today.” The next threat multiplies: “It’s not just bad human beings. It’s all the bad agents that are going to be attacking the token flow.” Labs see only a fraction of agents going rogue, so the ecosystem needs “new sheriffs in town” seeing cross-lab data, or “we might never get there if people just don’t trust tokens.”
• What changes after the deal, per Alex: the OpenRouter brand, product, and roadmap stay; “most things will be what we would have done had we been independent, except everything will be moving faster.” Longer term: “Hopefully I can comment on it soon, but I can’t now.”

Verification Notes

• The transcript contains conflicting versions of Anjney’s ten-year hedge: the opening excerpt says “I’d be shocked if we got to $10 trillion,” while the later discussion says “I’d be shocked if we didn’t get to $10 trillion.” The digest reports the figure without choosing between those formulations.