Atin Sanyal
Co-founder & CPO, Galileo. On 1 show, 1 appearance. The Shows tab opens the full record on each.
founderexecutiveengineer@atinsanyal ↗LinkedIn ↗galileo.ai ↗
Atin Sanyal co-founded Galileo, an enterprise GenAI evaluation and observability platform acquired by Cisco. Previously, he served as a staff engineer and architect on Uber’s Michelangelo ML platform and worked as an early engineer on Siri at Apple.
1shows
1appearances
16statements
1resolved
1supported
0contradicted
100%fully supported
Everything Atin Sanyal said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Sanyal: LLM-as-a-judge approaches choke and fail to scale in production
“LLMs as judges, which was the traditional way that people were using to evaluate, and that has a whole history of it. They don't scale. They don't scale in production. They don't allow you to do full scale observability, which means intercepting every single i…”
Sanyal: LLM evaluation does not require general-purpose reasoning models
“We feel like if you really focus on the fundamental problem that, hey, evaluations for language models is a very task-specific, constrained problem, and it does not require you to use you know, general purpose reasoning for that, and then there's fine-tuning y…”
Sanyal: Software and code are commoditized into tokens by fast AI generation
“In an era where you can build an, you know, a website and a web application with a Postgres database behind in 30 seconds, Software is just commoditized. Software is just tokens. Code is tokens.”
Uber built the world's first machine learning feature store
“We had worked on the feature store at Uber, and we had built the world's first feature store, and we literally coined the term.”
Sanyal: SLMs will always maintain an inference cost advantage over LLMs
“It is so
cheap to run a single inference on an SLM versus an LLM. So that gap will always be there.”
Sanyal: MCP addresses only a drop in the ocean of agent interactions
“Well, MCP is one stab at solving a very specific problem of agent interactions, which is to selecting the right tools and sort of automating that process. There's a lot more to be solved. It's a drop in the ocean of the overall set of interactions.”
Sanyal: AI outputs are mission-critical because AI is now the product interface
“I think it's the first time in human history that AI has become the face of the product. A user is directly talking to an AI and working with an AI. So every output of an AI system is mission critical. And with agents doing actions and making critical decision…”
Sanyal: Most enterprise data scientists are essentially data curators
“The main thing they actually work on, though, is curating data, because at the end of the day, you're not really a scientist sitting building new model architectures. Most data scientists are essentially data curators.”
Sanyal: ChatGPT commoditized modeling skills, turning AI adoption into software engineering
“Ever since ChatGPT, I've seen the adoption of AI in enterprises increase its velocity manifold. And the reason is because the niche skill has been commoditized. Now you have a black box and the whole point is how do you build a software and infrastructure laye…”
Sanyal: Compliance, security, and resilience are the main enterprise AI bottlenecks
“So banks and telecom companies and healthcare companies, while their engineering and innovation has stepped up in its velocity, the main bottleneck now is compliance, security, and just general resilience.”
Sanyal: Enterprises Will Not Deploy Action-Taking Agents Without Observability
“No one's going to put an agentic system, especially if they do actions and tasks, they're not going to do it without any kind of observability.”
Sanyal: AI observability faces a trilemma of cost, latency, and quality
“Observability is a bit of a different ballgame because it has a different set of challenges. It's an infrastructural problem and latency and cost and quality. Those are the three things, and they are a bit of opposing forces with each other. It's like this tri…”
Sanyal: AI developers should build evaluation suites before building applications
“In fact, you build the evals before you build the app. That kind of becomes, that's why people say that, hey, evals is the new weapon for product managers because they are the ones who are defining the app's behavior.”
Sanyal: In the end, humans will just talk to software to work
“In the end, we'll enter an era where we'll talk to software, and software will do work for us, and everything in the middle is a means to an end.”
Sanyal: Software could become completely headless and ambient within 5-10 years
“Ideally maybe 10 years down the line or perhaps five to 10 years down the line if we can build a system which is completely headless, where you just start kind of going back to Iron Man, right? Where you just talk to the walls and they answer you and they get …”
One line per show, most statements first. The link opens Atin's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 NEON SHOWLEDGER | Co-founder & CPO, Galileo | 1 | 16 | 100% 1/1 | full record on the Neon Show → |