Ankur Goyal
Founder & CEO, Braintrust · 2 appearances on the record.
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founderexecutiveengineer@ankrgyl ↗LinkedIn ↗ankurgoyal.com ↗
Ankur Goyal is the founder and CEO of Braintrust, an AI observability and evaluations platform for enterprise LLM applications. He previously founded Impira, which was acquired by Figma, and served as VP of Engineering at SingleStore.
51statements → 22claims → 3claims resolved → 100%fully supported → 3.69/5average certainty → 2.59/5average debate potential → 11said about them ↓
3 supported 0 partly supported 0 contradicted 19 not checkable as stated how the 22 claims stand · each chip opens the sources
4 predictions · 18 assertions · 7 opinions · 21 insights · 1 disclosure · every statement was checked. The predictions and assertions are the 22 claims: statements the public record can support or contradict. 3 are resolved, and 19 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 Ankur argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
Their most notable supported claim
Goyal: Figma vector formats consume far more LLM tokens than HTML or JSX
“Vectors are very difficult because they're a data inefficient representation, so the vector format in something like Figma Is choose up like many, many, many, many, many more tokens than HTML and JSX. So it's a very difficult medium to just sort of throw into …”
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 Ankur Goyal on measured tape to publish a rate. This says nothing about how they speak.
Everything Ankur Goyal said on Latent Space that made the record, most notable first. Filter by type, assessment or year in the ledger →
Goyal: Running LLM workloads at scale is impractical outside OpenAI
“It's just not practical outside of OpenAI to run use cases at scale in a lot of cases. Like, you can do it, but it requires quite a bit of work. And
Because OpenAI is so good at making their models so available, I think they get a lot of credit for the science…”
Goyal: Agent control flow and graph routing will move into models
“It feels very clear to me that this type of logic is going to be built into the model. Anytime there is control flow complexity or uncertainty complexity, I think the history of AI has been to push more and more into the model.”
Goyal: OpenAI o1 will make agentic frameworks obsolete
“And I think O-one is going to do that to agentic frameworks as well. Hey, I think To me, it seems very unlikely that the, you know, you and me sort of like sipping an espresso and thinking about how, like, different personified roles of people should interact …”
Goyal: Open-source inference providers are far less reliable than OpenAI
“They are nowhere near as reliable as, I mean, every single time I use any of those products and run a benchmark, I find a bug, text the CEO, and they fix something. It's nowhere near where OpenAI is.”
Goyal: Publishing Public Benchmarks Is Marketing, Not Product Improvement
“It's just that the value proposition of publishing an eval is completely orthogonal to the value proposition of building evals in service of building a good product. I think the purpose of publishing benchmarks is marketing, and it's good marketing.”
Goyal: Providing eval criteria and examples is more effective than writing specs
“In many ways coming to the table of product building with representative examples and criteria that articulate what good versus bad is for a use case is just a more precise and usable form of product management than writing a spec.”
Goyal: Software engineers will drive AI engineering, but ML tools are unusable for them
“The real gap is that software engineers who have a particular way of thinking, a particular set of biases, a particular type of workflow that they run, are going to be the ones who are doing AI engineering, and that the tools that were built for ML are fantast…”
Goyal: Simple tool-calling prompts cover 80% to 90% of AI use cases
“For probably 80 or 90% of the use cases that we see with people doing this, like very, very simple, I create a prompt, it calls some tools. I can like very ergonomically write the tools, plug into popular services, et cetera, and then just call them kind of li…”
Goyal: Vector Search Hard Part Is Application Permissions, Not Search
“The problem is that the challenge in deploying vector search has very little to do with vector search itself, and much more to do with the data adjacent to vector search. So, for example, if you are at Figma, the Vector search is not actually the hard problem.…”
Goyal: Fewer Braintrust customers run fine-tuned models in production than six months ago
“I will say in my own experience with customers as of the recording date today, which is September or something, yeah, very few of our customers are currently fine-tuning models. And I think a very, very small fraction of them are running fine-tuned models in p…”
Goyal: OpenAI dominates production while Anthropic Sonnet leads side projects
“We still see an overwhelming majority of customers using OpenAI, but almost everyone is using Anthropic for the, and Sonnet specifically for their side projects, whether it's
You know, via cursor or prototypes or whatever.”
Goyal: Public clouds fail to match direct OpenAI endpoint experience and capacity
“It hasn't been a smooth journey for people to get the capacity on public clouds that they're able to get through, you know, OpenAI directly. I mean, I think a lot of this is changing, catching up, et cetera. But it hasn't been perfectly smooth. And I think the…”
Goyal: Engineering around LLM limitations guarantees technical obsolescence
“If you make assumptions about the capabilities of models, and you engineer around them, you're almost, like, guaranteed to be screwed.”
Goyal: Under 5% of Braintrust production customers use open source
“Among customers running in production, it's less than five percent.”
Goyal: Nearly all Braintrust clients shifted to simple code with LLM calls
“Almost everyone that we work with has gone into this model that, that I, that actually exactly what you said, which is sprinkle intelligence everywhere and make it easy to write dumb code”
Goyal: Creating Golden Datasets for AI Evals Is Wasted Effort
“People don't really want to create golden data sets. It's, I think it's often a wasted effort to the point that you're making. I think the best teams view offline evals as a mechanism of reconciling what they see in production with real users who are using the…”
Goyal: Average AI companies cannot hire expertise to prevent reward hacking
“You need to have like a pretty specific expertise to design the RL environment in a way that's not vulnerable to reward hacking. And I think that either you'll end up with some fixed number of very well engineered RL environments, or you need to somehow employ…”
Goyal: Snowflake's VARIANT is the best semi-structured data implementation
“It is, without any question, at least in my experience, the best implementation of semi-structured data and sort of solves the problem of storing it very, very efficiently and querying it efficiently, almost as efficiently as if you specified the schema exactl…”
Goyal: Observability products would ideally run on Snowflake's VARIANT type
“And I think every observability product in some sort of platonic ideal would be built on top of Snowflake's variant implementation. And have better performance. It would be cheaper. You know, the customer experience would be better. But, you know, alas, it's j…”
Goyal: Unstructured data extraction startups remain low-tier enterprise priorities
“It is very, very hard to motivate a large organization to prioritize the problem. And so you're always going to be a second or third tier priority.”
Goyal: Figma vector formats consume far more LLM tokens than HTML or JSX
“Vectors are very difficult because they're a data inefficient representation, so the vector format in something like Figma Is choose up like many, many, many, many, many more tokens than HTML and JSX. So it's a very difficult medium to just sort of throw into …”
Goyal: Designers reject AI designing for them, but AI code generation bridges UI engineering
“In my limited experience and working with designers myself, I think designers do not want AI to design things for them. But there's a lot of things that aren't in the traditional designer toolkit that AI can solve. And I think the biggest one is generating cod…”
Goyal: Matching runtime and eval abstractions eliminates the AI data ETL problem
“If you structure your code so that the same function abstraction that you define to evaluate on equals equals the abstraction that you actually use to run your application, then when you log your application itself, you actually log it in exactly the right for…”
The other half of the tape: Ankur Goyal's own voice is left out of every number here. Other people bring the name up 11 times in 4 episodes on Latent Space. every mention, with the transcript →
Who brings them up most Shawn Wang 4
Every mention by year
tap a year for its mentions
2026 9 mentions in 2 episodes 5 per episode
2025 2 mentions in 2 episodes 1 per episode
Appearances (2)
| Episode | Date | Speaking time |
|---|---|---|
| The Great Evals Debate — Ankur Goyal & Malte Ubl | Dec 7, 2025 | 15m |
| Production AI Engineering starts with Evals | Oct 11, 2024 | 1h 28m |