From InstructGPT to Jev: What Comes After ChatGPT — Diogo Almeida, TypeSafe Co-founder & CEO

Summary

• Diogo Almeida’s Jev launch is a bet on a new model class — “System One” or “large programmable” models where “the goal is for code to be the consumer,” not chat. Unlike RLHF (“please humans”) or RLVR (“optimize benchmarks”), TypeSafe’s task, which he calls RLCD, targets reliability for programmatic use and the frontier of intelligence per dollar — hence Jevons paradox. He frames it as the second-to-third major North Star shift in LLMs: “RLVR is a 0.2 in my opinion, and I think that’s generous.”
• Adoption looks real, not touristy: in less than a week Jev passed 1 trillion tokens per day, “even at night… constantly churning, so you know machines are calling it.” Waitlist signups “don’t matter for a developer platform”; the scarce resource is rate limits, because software lets users specify a rote task once and run it in the background. Pre-launch, revenue was nearly zero and more than half of testers “did not get it” — a challenge to orthodox product-market-fit thinking.
• His mode-dropping argument explains why long-string error intuitions do not straightforwardly apply to RLHF-shaped models. Calibrated, mode-covering models would include outliers; GAN-like mode dropping instead removes minority outputs and makes generation conservative because visible errors are heavily punished. That may preserve long strings while making string models poor decision engines. He treats calibration, confidence thresholds, and typed outputs such as choice/score/null as product responses.
• He is strongly against public benchmarks and demos as primary evidence, not against measurement itself. His preferred path is “vibes and trust until you put it into a workflow and evaluate it for that workflow.” TypeSafe keeps internal evals but treats not gaming them as a top-level priority. He held this line even when it hurt fundraising: “no one believed us… We are principled. We’re gonna stand by our guns.”
• Safety alignment in an API is, to him, a type error, though he says he is not opposed to safety as a principle. Refusals inside dependencies can make software fail stochastically. Safety alignment may make sense for first-party products like ChatGPT; at the infrastructure layer he compares intelligence to a database and says end-use restrictions should not be built into the technological layer because they fracture general capability.
• His coding-agent thesis is that the named incumbent agents are built around a single-model world, while open agents can experiment with multi-model designs. His follow-on research agenda is “coding agents free from the tyranny of the KV cache”: labeled subtask trees, shared state across parallel agents, and cheap historical-context lookup instead of treating continuous learning as a separate problem.
• The macro thesis is explicit: “TFP growth rate: 3% in 5 years.” He argues that models are currently tied at roughly zero percent of economically valuable work despite solving hard math problems, predicts an “inverse SaaSpocalypse” in which SaaS is supercharged, and does not expect mass unemployment. He promises deployed models will not be silently changed, while offering no general long-term support commitment; possible model sizes, LTS, and other forms of machine-native intelligence remain under consideration.
• He dismisses both the “pace the frontier” consensus and much of the neo-lab wave. Pacing arguments, in his view, assume everyone must do more RLVR with unrestricted action spaces; for Jev’s shape, “zero is the optimal amount.” He calls most neo-labs weak unless they have a real North Star task, while advising researchers who do have one to pursue it.

Deep dive

1. Launch week: “never been worse” emotionally, never more in sync with reality

• Diogo’s state after Jev took over the timeline: “I’m a ragged corpse of a person right now,” but mentally vindicated — “the entire AI field is like one of those carnival houses of mirrors, and everyone is just insane… for just this week, I’m better in sync with reality.” His headline claim: “an AI-based economic revolution is back on the table.”
• He deliberately prioritized a Discord town hall — the server was then about 100,000 people — over VIP investor meetings: “it feels dirty if, in my gigantic calendar… the community isn’t one of those.” He even considered hosting a town hall while walking to the studio.
• swyx notes that three topics were trending on Twitter; Diogo discovered that he was looking at his own personalized feed and cringed: “I’m a noob.”

2. What Jev is: System One models where code is the consumer

• The definition, in his words: a new class of models — “machine-native, System One, large programmable” — distinct from pretrained LLMs (“autocomplete of the internet”), RLHF chatbots, and RLVR. “The goal is for code to be the consumer… things that directly are consumed by code, hence the name type-safe.”
• Jev is the brand for models “on the frontier of intelligence per dollar,” named for Jevons paradox, with the launch framed around trade-offs among reliability, cost, calibration, and speed. They avoid “decision models” as a name because “System One is beyond that”; Diogo says they have more work in the tank.
• A relief of launching: “now I don’t have to answer that question to my parents anymore, ‘cause ChatGPT can just explain it.”

3. How mode dropping explains the disconnect with LeCun’s doom slide

• Diogo’s view is that Yann LeCun’s takes are among the closest to accurate, but he explains the apparent disconnect with the famous slide about error probability as sequence length increases through mode dropping.
• In a calibrated, mode-covering distribution, outliers are expected. GAN-like mode dropping instead discards minority classes and retains common outputs. For long strings, visible errors are easy to detect, so the model becomes extremely conservative.
• The punchline for his business: that conservatism can preserve long strings while making string models poor decision engines — “that calibration is total poison into the probability distributions of strings.”
• On LeCun’s JEPA fix, he demurs pragmatically: “really cool early research. Is it practical yet? Probably shouldn’t say.” His brand, he insists, “other than unhinged, is practical.” Scaling laws can require exponentially more resources for normally sublinear gains, which looks like a bad investment unless the gains are extremely valuable.

4. Safety alignment is a type error in an API

• Diogo says he is not opposed to safety as a principle, but considers safety alignment generally misaligned with users. Capability alignment — doing what the user wants — “is sick for software engineers”; safety alignment is “the opposite of instruction following,” because it follows a lab’s restrictions. Fine for a first-party product; “in an API, that’s nuts… so anti-user.”
• The concrete failure mode: a refusal in Claude Code — “I’m sorry, I can’t read DNA.py” — is annoying but workable, and users tolerate it through “Stockholm syndrome.” But if a dependency refuses in the background, “you want the software to just stochastically break because a user sent a weird message?”
• swyx’s pushback — “what if we use it to kill people?” — gets a layered answer: Diogo would prefer good uses and would “put my thumb on the scale” for them, but “will I do it at the technological layer? Absolutely not, because that will fracture the intelligence.” His model of intelligence is “more like a database than a coworker”: “we are an API… we shouldn’t be able to know what the downstream users are doing.”

5. Vibes and trust over benchmarks — a stance that cost them a fundraise

• After a preview-period terms-of-service misunderstanding about benchmarking — which swyx says the team would remove, while Diogo says he asked the lawyers to check it — Diogo lays out the position: intelligence has “a je ne sais quoi… the good-model smell,” and public benchmarks “are extremely, extremely gameable.”
• His example: labs collected data that looked like MMLU to improve scores, “which is just benchmarking with extra steps.”
• The alternative is “vibes and trust until you put it into a workflow and evaluate it for that workflow.” TypeSafe’s job is to keep moving “the nines of reliability.” Internal evals exist, but “it takes a lot of discipline not to game those”; not fooling themselves about intelligence is a top-level priority. “We need to be truth-seeking.”
• The cost of principle, worn proudly: “last year when we did fundraise, no one believed us… they wanted just benchmarks and we’re like, we’re not gonna do that. That rewards bad actors.” A tell on quality-versus-time trade-offs: “if we wanted to, we could have released Jev a year and a half ago if we wanted it to be dumb.”

6. A data lab, not a model lab — and why they won’t train on user data

• “We will always care so much about data… model capabilities means data. Data is so unbelievably complicated, and that is what gets nines.” They are “hiring infinite data people. Actually infinite.”
• All training data is synthetic, deliberately so. Real usage follows a power law — “you’ll end up overfitting to it and fracturing to it” — while the target is a sci-fi future where these models are general infrastructure, “kind of like UDP as LLMs and TCP as our models.”
• Even having all present-day data would mean overfitting to the present. His picture of a great data person is closer to an artist than an annotator: they study the cognitive core, find its jaggednesses, and address them surgically across past, present, and future. “The general case rather than the specific case” requires intelligence every time.

7. RLCD: the third North Star shift, and his Bitterest Lesson

• His hierarchy, attributed roughly to Suchin: “algorithms beats compute very roughly. Data matters way more than compute, obviously. And doing the right task, having the North Star, is the hardest, most important thing.” Task shifts have happened “twice so far, maybe 2.2 times” in LLMs: RLHF for instruction following, RLVR as “a tiny little edit,” and now RLCD — “programs in the loop.”
• He unpacks why “RLHF” is really a task, not an algorithm, through several historical examples. He is not fully certain about the exact attribution of the early backflip work, then discusses OpenAI’s Learning to Summarize work as PPO on language models for an ill-specified task. Dario, Alec Radford, and Ryan are mentioned in connection with the cited work.
• “It’s not about the PPO. That part doesn’t matter.” The North Star is instruction following, just as DPO and its descendants can still belong to RLHF despite not using the original algorithm.
• One nuance he adds to swyx’s summary — programmable AI and the human out of the loop — is that they must remain practical. There may be programmatic types that are “sick AF,” but if the technology is not ready, it is not a tragedy if they are not deployed yet.

8. The pre-Jev world was a tragedy — and the AI winter he feared is averted

• The emotional core, from why he left OpenAI: “If an AI winter did happen and I did not do every fucking possible thing I could to avert that, I would see myself as personally responsible” — both for the RLHF direction, which he thinks widened overpromise versus underdeliver, and for not going all-in on this. Post-launch he changed the wording: “the AI winter I’m worrying about is averted.”
• His favorite provocation: how can AI solve Millennium Prize problems in math while still failing to automate basic rote work? It is “a supercharged engine of automation that just does not have the right plugs.”
• The categorical claim: if Tessl disappeared, it might take a year or two for people to catch up, but “it’s done, right? This has changed the path of technological history.” He admits the original arrogance too: “I thought this whole project would take a week… I was unbelievably wrong, so I am so sorry to everyone at OpenAI.”

9. The numbers: a trillion tokens a day, and why waitlists don’t matter

• The milestone he volunteers: “a trillion tokens a day is a lot… this is not fleeting tokens per day. Even at night it’s constantly churning, so you know machines are calling it and not just people trying things out.”
• Signups he actively dismisses: “waitlist signups don’t matter for a developer platform… if every single human being in the world just wrote a couple of queries, that would be a rounding error compared to one power user’s for loop that is just creating value.” The scary variable is rate limits.
• The “marketing genius” narrative gets punctured — “that was just us. We don’t have a marketer, also hiring” — and he credits the platform team: “I think we have more nines of uptime than Anthropic while having the most unprecedented launch ever.”
• swyx cites roughly 36 million launch-video views, with Diogo updating that to 38 million. He compares it with Navio at 74 million and Fable 5 at 57 million, then says Jev is currently number one among Neo Lab launches in 2026. Diogo rejects the category: “I don’t give a shit about being a neo-lab… what I care about is being a reliable dev platform.”

10. Determinism is the wrong North Star; robustness is the one that matters

• swyx asks why there is no seed parameter. Diogo reframes: “reliability is actually a catch-all — whenever AI can’t automate something, it’s due to some form of reliability.” Determinism — same input, same output — is “slightly interesting for unit tests” but “the wrong North Star”; robustness — similar inputs, similar outputs — is where “people get burned with AI making decisions.”
• Their test is to inject UUIDs or nonces into prompts and demand similar answers to semantically identical questions.
• He will build a deterministic model if convinced it is valuable — “we live to please” — but “it just gets you less intelligence per dollar,” and he suspects they will be GPU-constrained for a long time. swyx warns that users will still demand determinism.
• A revealing aside on the Pareto frontier: “we do absolutely disgusting things to be on the Pareto curve of intelligence per dollar.” The team told him not to call the result “a Frankenstein’s monster of models.”

11. Versioning promises: never silently swap a model, but no long-term support either

• On swyx’s quantization concern, Diogo makes a categorical commitment: “We will not change our models when we deploy them. That is insane. We care about developers.” First-party products can change behavior; an API cannot.
• But “we are not promising long-term support for the models” and will launch new models faster than users may expect. He gives a possible example of temporarily LTS-ing Jev 1.13.0 because developers hate breaking dependencies; the alternative — fracturing the fleet — would be a bad outcome.
• Research is underway toward a more developer-friendly LTS approach, but he does not promise that exact models will be maintained indefinitely. Model versions should get smarter, with the largest deltas appearing when a system moves from jagged to broadly capable.
• On porting LTS models to other silicon such as Cerebras or Etched: “No comment.” Intelligence per second is “a different metric”; real-time budgets between 100 milliseconds and 1 millisecond can be valuable, but “I don’t think that that will be Jev’s niche.”

12. The primitives: choice, score, and null (from Bernoulli)

• “Null” is derived from “Bernoulli” and is a continuous, bool-like probability. Rejected names included PBool and Pool — “we were wanting to call it a pool party, but then no one let me.” All three primitives are deliberately new concepts: “these are not types that exist in programming… a score is not an int.”
• The mapping he endorses: choice maps to a switch statement on an enum, null maps to if statements, and score maps to sorting or thresholding. “There will be more types, and they will map into programming primitives.” Choice is closest to a function call, but “a function call is an extremely disgusting thing.”
• Internal color: the name Jev drew substantial internal hate. Everyone has apologized except Diogo’s co-founder, who wanted it called Meow.

13. Structured state in, and system prompts as “disgusting global variables”

• The under-read part of the API: state, instructions, and criteria can all be structured JSON. “If you’re using a template, turning it into a system message or something, you are thinking in the old way… within the computer, you want to be passing nested structure that is semantic all around.”
• His indictment of the status quo: “System messages are disgusting global variables where you just put everything in there… and you hope that every single instruction gets nailed instead of asking the questions in parallel.”
• The design goal is a flow state where people can write a type-safe System One query and get accurate branching without having to repeatedly test whether the model works. “That is the dream. And that is going to be a long, long slog.”

14. How to decompose: many small questions, “ML without the ML”

• His pro tips as the model’s heaviest user: break problems into “the smallest semantic unit,” be “really structured and explicit” — he likes backticks — and be clear about what each reference means because the model should be literal.
• For refusals, do not ask “should I refuse here?” Ask many independent questions about specific refusal conditions, threshold them, and add missed cases as tests. “You fix the bug by adding that question in… and now it is just solved forever.” The decomposition makes the system measurable: “It’s like ML without the ML.”
• swyx reports that his own one-big-prompt versus 100-call benchmark made decomposition slower, more expensive, and worse. Diogo’s counterpoint is that this is inconvenient but produces software that can be trusted and run in the background.
• Parallel questions are cheap: “put IDs on every message and ask a question about each ID… pay for the state once.”
• One hedge he volunteers: automated trading with the models makes him “a little bit afraid.” It is a high-level task the models may not be good enough for yet; confidence estimates and human-escalation thresholds can help determine when not to deploy.

15. Fine-tuning: maybe, warily — plus model sizes, cascades, and killing the regex

• On locally wrong calibration, he concedes freely: “it will get many things wrong, to be clear.” The team wants each model version to be notably better. Fine-tuning “could be in the cards,” but narrowing a general model might improve edge cases in one task while damaging generality.
• swyx says OpenAI and Claude, and possibly Gemini, have rolled out and then withdrawn fine-tuning. Diogo agrees that the versions he knows about were poor and that removing them may have been better.
• The sci-fi he is cooking — explicitly “not a promise, a desire” — is calibration plus a cascade of model sizes: confident answers stop early, uncertain ones escalate, and automatic fine-tuning might become possible across a Pareto frontier. “Is there a world where people don’t write regexes anymore because the intelligence per dollar is cheaper than the complexity of a regex?”
• Diogo is open to different model sizes because he cannot know how much intelligence each user needs, but this is a direction under consideration rather than a guaranteed shipping roadmap.
• He also says he likes the idea that culture is what a company does when the market does not reward it, while acknowledging he may not have the exact wording.

16. System One versus System Two is an empirical boundary, not a slogan

• Asked to define the boundary, since “people are just trying to Jev everything,” he refuses to theorize categorically: “the truth is that this is an empirical problem, just like scaling laws.” His claim is that pretrained condensations of intelligence are fundamentally System One thinkers.
• RLVR has done “incredible things for System Two” — “I am in awe” — but the resulting behavior is fragile and jagged. ChatGPT-era models were described as general; RLVR models are described as narrow and brittle, and “math is not just spiky, it’s fractal.”
• swyx’s field report from day-one access: single-hop is “state of the art,” while multi-hop starts to fall down as the number of hops increases. Diogo says the work is to unearth, smooth, sculpt, and combine as many capabilities as possible. System One is simply the description of what currently works in that paradigm.
• Diogo does not promise never to use string-based or other forms of reasoning. He says less slow, inefficient, and fragile forms of reasoning remain possible, while his firm commitment is to the machine-native ROI North Star.
• On vision, the obvious ask after the Doom demo: “everything is in the cards.” The harder question is what users think they want versus what they actually need. Diogo says, with a hedge, that Jev is “as far as I can tell” by far the best at avoiding long-context degradation, while other providers have simply given users the long context they requested.
• Taking the product philosophy too far toward satisfying stated demands could produce “Anthropic nanny-state-style thinking.” Finding the balance between trust, speed of release, and treating users as adults will be one of his major near-term debates.

17. Pre-launch reception was bad — a case against orthodox product-market fit

• The honest history: “the reception was pretty bad… more than half the people we had play with it just did not get it,” nontechnical team members feared they were “selling a vitamin and not a painkiller,” and there was “almost no revenue before launch.” Those who got it asked how to get it through procurement.
• Then the launch: “It explodes and everyone’s like, we need as many rate limits as we can. Can we literally give you GPUs?” His conclusion: “I do want to call into question the concept of product-market fit.” The mechanism was not simply marketing but “passionate developers whose souls basically resonated at the same frequency.”
• His loyalty response is to explore launches “better for developers than enterprises.” He dyed his hair and ran garbage-bag town halls because “it felt dirty to me during our company’s most important times not to keep talking to them.” He asks the community to hold him to that and bring out the pitchforks if he changes.

18. The use-case map: dark data, real-time, verify everything, smart software — and computer use from left field

• The families they mapped from first principles before launch: “dark data” — companies have hoarded data but avoided LLM analysis because it was too expensive — plus coding agents as the big money-makers; real-time and assistant use; “verify everything,” meaning checking LLM calls in an observability-like way; and intrinsically composable “smart software,” including a programming-language-as-Jev project.
• Computer use “came out of left field.” swyx shows a voice-driven demo involving Notes, Arc, x.com, and Photo Booth, with Jev used as a decision model. Diogo’s position is both enthusiasm and caution: “just like I’m anti-benchmaxing, I’m also anti-demos. I want to make sure that it works reliably.” He still calls it “super fucking sick” and wants it to work for hands-free computer control.
• Community humility: users’ builds are “kinda cooler than ours… if that was our demo, holy shit.” The cookbooks are “not AI-generated trash”; they are inspired by real customer problems and contain practical techniques.

19. Coding agents: the one-model incumbents’ dilemma, and freedom from the KV cache

• Diogo calls the named incumbent agents — Claude Code and Codex in the discussion — the likely number one and two, while explicitly saying he is not fully sure. He says they are built around a single-model world, while open coding agents are experimenting with multi-model designs.
• Since open agents are roughly at parity — “there’s not so much you can do with a while loop” — the first killer multi-model pattern could spread quickly across them. He is unsure what the single-model incumbents will do.
• His research agenda, from the article “KV Cache Rules Everything Around Me”: the cache locks an agent into one model and encourages continuous appending, which conflicts with state management, abstraction, and decomposition. It helps explain why routing is hard, why sub-agents often fail, and why compaction is difficult.
• Freed from that constraint: labeled subtask hierarchies that can be searched for relevant context; parallel sub-agents reading each other’s state with intelligent rather than “basic-ass” locks; and cheap lookup over historical context. “Isn’t it kind of weird that you start from scratch every time and need to solve a problem called continuous learning? That’s actually a memory-management problem.”
• swyx points him to Prime Agent and recursive-language-model work as adjacent efforts.

20. The macro call: 3% TFP growth, zero automation today, inverse SaaSpocalypse

• swyx flags the manifesto line — “TFP growth rate: 3% in 5 years” — as unusual among labs. Diogo says that is what an economic revolution is and connects it to what the OpenAI charter used to stand for.
• His scoring: “all models are roughly tied right now at zero” percent of economically valuable work, although there is some chance the process has begun. When real automation happens, “it will show up in the economic statistics.” He says it will not cause mass unemployment.
• The software-stagnation puzzle: “How is the software basically exactly the same [as 2019] despite AI being so freaking awesome, other than sometimes having a chat box on the side?” These systems cannot yet be trusted with decisions that companies have stakes in.
• Hence “an inverse SaaSpocalypse”: SaaS is going to be supercharged because incumbent software companies know which work is valuable to automate.
• His aesthetic preference is that AI disappear into the background: “I’m really tired of AI always being the foreground character… the world should just be more delightful, and AI should just help with that.” He wants “do what I mean” behavior throughout software.

21. “Pacing the frontier” is a sleight of hand built on an RLVR monoculture

• His rebuttal to the frontier labs’ pacing document is that it assumes everyone needs to do more RLVR with broad action spaces. Sandboxing problems could have been solved, he argues, but labs chose greater model power over those constraints. Given those choices, “oh yeah, we’re heading into a dangerous world, guys” follows — but the premise has alternatives.
• “I obviously don’t think I need to do more RLVR on our models. I think zero is the optimal amount for our shape.”
• Who is at fault: “the only people who are at fault here are the researchers.” He says the public reasonably assumes OpenAI and Anthropic are doing the best they can, without seeing the other directions available.
• swyx’s report that pacing is primarily political positioning, including around the 2028 election, lands hard: “Oh, I wish I didn’t hear that… that makes me lose faith in humanity a bit.” Diogo’s COVID analogy is that overconfident appeals to authority produced a suboptimal response and bad downstream effects. “Misleading people, even for the greater good, is just — I’m not a fan.”

22. Origin story: from InstructGPT and copywriting slop to “fuck it, man, we ball”

• He fought to deploy InstructGPT. Early versions used an unpublished algorithm he wrote because cleaning PPO data was too slow, and “basically immediately it took 50% of the market share of LLMs at the time.”
• Then the letdown: it was used mainly for copywriting — Jasper AI, Copy.ai, and what is now called slop — and “we were worried we had made the internet a worse place.” Everyone, he argues, should be able to explain why superhuman instruction-following was not AGI.
• The reframe that became TypeSafe: working backwards from an AI economic revolution — “what will we be calling the AI if AI is an API? Humans or code? I figured it was many nines of code. But all the optimization was going into the humans part.” Sam’s response to his document was: “This is so fucking good. You should go work on it.”
• He assumed Anthropic must already be working on the same direction; he then characterized ChatGPT as a copy of an internal Claude product that had not shipped. swyx added that reasoning was “first-ish,” while Diogo responded that the product question was debatable despite the research quality.
• The founding sprint: he called Eric first; Sasha, whom he was not even recruiting, said “I’m in” and folded her startup. “Within two weeks we had funding, people moved into my apartment — it was the worst ‘cause I’m a neat freak.”
• swyx links the Thanksgiving GPU run at OpenAI to the coup period; Diogo initially says he is not sure, then remembers the coup as annoying and promises to discuss it later. His self-assessment: “I describe myself as 0% entrepreneurial… I can’t imagine anyone doing this twice. Honestly, doing it once is pretty bad.”

23. Advice to frustrated lab researchers, hiring, and the AWS of intelligence

• Should others do what he did? “Most neo-labs are crap… they tend to want money to play around with their experiments.” His deeper heresy is that he does not value research pedigree by itself; he values people who identify and pursue the right North Star task. He still says researchers are needed.
• If someone has a genuine North Star task: “abso-fucking-lutely, please do. Please break this hive mind.” If someone mainly wants to play with research, he thinks an established lab may be the better place. His capital-allocation line remains: “if you gave me a billion dollars, I wouldn’t pre-train… Frankensteining is not the most elegant, beautiful thing, but it solves problems, baby.”
• Tasks he throws to others include intelligent games — simple state machines for NPCs in something like Stardew Valley — and coding agents free from the KV cache. He also says he will not bake Jev’s identity into the model: “I am not going to put into the models that you are Jev from TypeSafe. That fractures it… represent what the internet thinks. Be correct.”
• Hiring priorities include “infinite” data people, rebranded as model-capabilities people so they receive higher status; platform engineers; a founding marketer; and people to ship new shapes of machine-native intelligence beyond decisions.
• He says speed of light is a bottleneck and is unhappy that European users were only three times as fast rather than 100 times as fast because there are no servers there yet.
• The end state he wants is “an AWS of intelligence.” He says it would be arrogant to claim it will be TypeSafe, but he will do what he can to make it happen. System One is “the TCP of it,” and swyx’s earlier phrase that Diogo liked was “Build product on God.” There are several more layers and model shapes to explore.