Vijay Krishnan
Co-founder & CTO, Turing · 1 appearance on the record.
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founderexecutiveengineerscientist@krishnanvijay ↗LinkedIn ↗turing.com ↗
Vijay Krishnan co-founded Turing, an AI talent and data platform that provides vetted technical talent and domain data to train frontier AI models. Earlier in his career, he conducted NLP research at Stanford University and co-founded Rover.
27statements → 6claims → 0claims resolved → 3.85/5average certainty → 2.48/5average debate potential → ≈4.0/5argument clarity, estimated → 2said about them ↓
6 not checkable as stated how the 6 claims stand · each chip opens the sources
3 predictions · 3 assertions · 4 opinions · 15 insights · 2 disclosures · every statement was checked. The predictions and assertions are the 6 claims: statements the public record can support or contradict. 0 are resolved, and 6 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.
The record, in short
What the tape says about how Vijay argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
How they sound: not measured why? →
We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Vijay Krishnan on measured tape to publish a rate. This says nothing about how they speak.
Everything Vijay Krishnan said on the Neon Show that made the record, most notable first. Filter by type, assessment or year in the ledger →
Krishnan: OpenAI, Anthropic, and Google models are within a hair's breadth of parity
“I do think these you know, models of OpenAI, Anthropic, Google have definitely gotten to the point where they are within a sort of hair's breadth of each other, ultimately. Yes, there are the, in domain A, one model might be better, in domain B, the other mode…”
Krishnan: End-to-End Outcome Startups Benefit from Smarter Frontier Models
“Now, when you're doing this kind of end-to-end thing, you are less likely to get disrupted. In fact, if you know, if anything, if the if the cloud models or codex or even Gemini models or any of these other models get a lot better at coding, these people only …”
Krishnan: Rapid growth and 10x product improvements trump AI defensibility
“The number one thing before you know, you get into defensibilities, make sure that whatever it is you're building is is was impossible to build four years ago. Make sure, you know, it is a 10 X or a hundred X better than the status quo. Make sure that it has a…”
Krishnan: Engineers at many companies have not written code in six months
“There are so many companies now where software engineers have not had to write a single line of code in say more than six months, which is I mean, quite unprecedented, right?”
Krishnan: AI will reach coding-level trust in law and taxes in 1-2 years
“Definitely. This is not the case with the law and a number of other law or taxes and a number of other things, but it's only a matter of time. I think the models will get there in the next probably year or two.”
Krishnan: Prediction of a 10x AI-driven startup surge was wrong
“One prediction on way on which I, I'm just, I turned out to be wrong was I would have expected this kind of productivity gains to come all the way on the stack along the stack. Like I would have expected that, for example, the number of let's say good venture …”
Krishnan: AI Accelerates Execution but Not Fundamental Innovation
“Yes, while this has done a lot to the execution part, it does not really seem to have speeded up the innovation and the invention part, because theoretically, imagine you speed up everything, then who cares? There are 10 times the number of venture-funded star…”
Krishnan: Purely model-generated synthetic data hits a wall without human feedback
“There is also, I think, limits to how much value can be added there because ultimately there is just not too much new information. If you're telling the model itself to kind of generate, yeah, There is a, I mean, there's all sorts of things about how beyond th…”
Krishnan: Unreviewed AI-generated code will create widespread security vulnerabilities
“Where people are suddenly able to produce many tens of thousands of lines of code. Nobody has time to review. Nobody has this one. At best, people can do a sanity check and move on. I guarantee it is going to create a lot of security holes and various other so…”
Krishnan: Meaningful services provide AI startups a moat against model advances
“Having having the need for a meaningful services component does, for example I mean, it serves a useful purpose also today, right? Like in a manner which might not have been valued as much a few years ago, which is that It absolutely gives you a certain moat a…”
Krishnan: Startups acting as band-aids for model flaws won't survive upgrades
“I, even a couple of years ago, I always thought certain categories of startups, which were doing some almost like a bandaid on what is today's a hole in today's model is really not going to survive because the next version doesn't need that bandaid basically. …”
Krishnan: Foundation model companies cannot compete without expert human data
“This became pretty much like a must have. It sort of became like if you're running a model company without this component, it's like, you know trying to enter a race track with three tires instead of four basically.”
Krishnan: AI model training tasks get deprecated within six to nine months
“The one thing I think that makes this business a bit of a moving target is that the models do learn pretty rapidly. Like if you are offering a certain type of this thing a certain kind of a collaboration to labs with a certain, let's say, type of talent and a …”
Krishnan: Coding and math training improves overall AI reasoning capabilities
“Coding in general has nice spillover effects in terms of improving reasoning capabilities of models as a whole. Whereas you know, let us say the model companies prioritized something like the models getting better at medicine. I'm not too sure if that would he…”
Krishnan: Economic value will concentrate entirely on uniquely human tasks
“If the human can do at least one thing better than the model, yes, they are going to add value. That is where the costs will go. If all the other costs will go to zero, this will become, you know at least as far as micro economics goes the this is where the ye…”
Krishnan: Over half of global software engineers have narrow task scopes
“A big chunk of maybe more over half the you know software engineers in the world operate at a relatively narrow task scope.”
Krishnan: LLMs require high-skill experts unlike traditional ML low-skill labelers
“While they were definitely data annotation companies that had relatively more scale low skill labelers. Yes, which is what a lot of the traditional machine learning pipelines needed. The what these LLMs and the modern foundation models needed was just somethin…”
Krishnan: Human-designed prompt and auto-verifier tuples maximize synthetic training data ROI
“The, this method is the one, I think, which has a lot of legs in the, particularly the more you can operate in this particular paradigm of prompt and then these rule or rubric based verifier tuples. The, that is a very nice way for sort of creating synthetic d…”
Krishnan: AI data headroom lies in multi-day, long-horizon tasks
“Generally speaking, there's less of a room for simpler tasks. The more you're doing expert level tasks the more room might be there. The more you are into some you get closer and closer to these long horizon tasks. The kind of thing that would take a human one…”
Krishnan: Application startups do not need to hire ML researchers
“If it's an application startup, I don't think you even need an ML researcher to begin with, right? And even an ML researcher will get frustrated.”
Krishnan: Incremental products paired with heavy deployment models yield terrible outcomes
“If you're incremental of what is already there and you have an FD model and, you know, bad cost structure, you literally have the worst of all worlds basically.”
Krishnan: Agentic products on existing models offer highest enterprise AI ROI
“And very often we do find that the right kind of agentic products built on top of existing models is the right high ROI decision for most enterprises.”
Krishnan: Turing began helping OpenAI train models in early 2022
“This whole thing started when yeah, when we started helping OpenAI you know, A little over about four years ago, like, since from early 20, 20 to long before chat GPT launched and the that is how we got started in this particular area.”
The other half of the tape: Vijay Krishnan's own voice is left out of every number here. Other people bring the name up 2 times in 2 episodes on the Neon Show. every mention, with the transcript →
Who brings them up most Siddhartha Ahluwalia 1Mahesh Ram 1
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2026 2 mentions in 2 episodes 1 per episode
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| The Man Training GPT, Gemini & Claude Reveals What's Coming Next | Vijay Krishnan, Turing | Jul 31, 2026 | 1h 2m |