Simon Eskildsen
Co-founder & CEO, turbopuffer · 1 appearance on the record.
computed by AI from the episodes · how this works → · full disclaimer →
founderexecutiveengineer
30statements → 16claims → 8claims resolved → 88%fully supported → 3.93/5average certainty → 1.93/5average debate potential → ≈4.0/5argument clarity, estimated →
7 supported 1 partly supported 0 contradicted 1 not yet assessed 7 not checkable as stated how the 16 claims stand · each chip opens the sources
1 prediction · 15 assertions · 1 opinion · 8 insights · 5 disclosures · every statement was checked. The prediction and assertions are the 16 claims: statements the public record can support or contradict. 8 are resolved, 1 is not yet assessed, and 7 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 Simon 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
Eskildsen: Neon retrofitted Postgres for S3, while Turbopuffer built pure object-storage consensus
“I think neon neon was first to, and they're trying to retrofit it onto Postgres. And then they built this whole architecture where you have it in memory, and then you sort of like, you know, mmap back to S-III, and I think that was very novel at the time to do…”
Expressed certainty vs assessment result
weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet
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 Simon Eskildsen on measured tape to publish a rate. This says nothing about how they speak.
Everything Simon Eskildsen said on Latent Space that made the record, most notable first. Filter by type, assessment or year in the ledger →
Eskildsen: Model weights compress reasoning, not all world knowledge
“We can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? We can compress into a few terabytes of we…”
Eskildsen: Object-storage-first databases trade 200ms write latency for pure upside
“The only real downside to that is that if you go all in on object storage, every write will take a couple hundred milliseconds of latency, but from there, it's really all upside, right? You do the first query, it takes half a second”
Eskildsen: Neon retrofitted Postgres for S3, while Turbopuffer built pure object-storage consensus
“I think neon neon was first to, and they're trying to retrofit it onto Postgres. And then they built this whole architecture where you have it in memory, and then you sort of like, you know, mmap back to S-III, and I think that was very novel at the time to do…”
Eskildsen: Offered to return capital if Turbopuffer lacked PMF by year-end
“I don't think I've said this publicly before, but I just called Locky and was like, well, Locky, like, if this doesn't have PMF by the end of the year, like, we'll just like return all the money to you. But it's just like, I don't really, Justine and I don't w…”
Eskildsen: Turbopuffer outperforms Lucene on long LLM search queries
“Turbo Puffer today has a fairly start of the state of the art full text search engine.
We beat Lucene on some queries, in particular, very long queries that we've optimized for, because those are the text search queries we see today.”
Eskildsen: Elasticsearch Was the Most Difficult and Aggravating Database at Shopify
“The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was Elasticsearch. It was very, very difficult to deal with, and I saw a lot of projects that were just being held back in their amb…”
Eskildsen: Vector Search for Readwise Would Have Cost Six Times Its Total Infra Bill
“But this was a company that was spending maybe five grand a month in total on all of their infrastructure. And when I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's going to be like…”
Turbopuffer Bought Oregon Dark Fiber to Serve Notion Across Clouds
“It started getting really painful in like mid-twenty-twenty-four, because we were closing deals with Notion actually, that was running in AWS, and we're like, trust us, you really want us to run this in GCP?
And they were like, no, I don't know about that, lik…”
Eskildsen: Macro AI predictions are a waste of time because nobody can forecast AI
“I don't like just doing like thought pieces on this is where it's going and like trying to be all macroeconomic about AI. That's as turned out to be a giant waste of time because no one can really predict any of this. So I just, Collect case studies.”
Eskildsen: Turbopuffer is profitable
“It now means that we're profitable because we had so much pricing pressure in the beginning.”
Eskildsen: Turbopuffer benefited from having an investor without database expertise
“The other people were talking at the time where database experts, like they, you know, knew a lot about databases and Lockheed didn't. This turned out to be a phenomenal asset, right? I like Justine and I know a lot about databases. The people that we hired kn…”
Eskildsen: Turbopuffer ANN v3 searches 100B vectors with 40ms p50 latency
“ANN v. Three can search a hundred billion vectors with a p-fifty of around 40 milliseconds and a p-ninety-nine of 200 milliseconds. Maybe other people have done this. I'm sure Google and others have done this, but we haven't seen anyone at least not in like a …”
Eskildsen: Scaling a database company requires supporting almost every query plan
“If you want to build a big database company, The database over time has to implement more or less every query plan, because when you have your data in a database, you expect it to over time, not just search, but also, Hey, I want to aggregate this column. I wa…”
Eskildsen: Cursor moved 20 terabytes from Postgres to Turbopuffer
“Like at some point, cursor moved like 20 terabytes of Postgres data into Turbo Puffer, because it's like, it's there, it works, and these particular query plans we know work well, and so they just moved it all to defer sharding.”
Eskildsen: Every company will connect all data to AI within years
“I don't think you're going to find a company over the next few years that doesn't directly or indirectly have all their data available for search and connected to AI.”
Eskildsen: Modern hardware requires databases designed around concurrent requests, not sequential roundtrips
“You really have to build a database where you have as few round trips as possible, right? This is how CPUs work today. It's how NVMe SSDs work. It's how S three works that you want to have a very large amount of outstanding requests, right? Like basically go t…”
Eskildsen: The worst outages stem from multi-system unsynchronized state
“And the worst outages are the ones where you have state in multiple places that's not syncing up. So it really came from a, like, just a very pure source of pain of just imagining what we would be okay being woken up at three a.m. About, and having something i…”
Eskildsen: AI shifts build-versus-buy from technical ability to speed
“And I think AI has also changed the buy versus build equation in terms of, it's not really about, can we build it? It's about, do we have time to build it?”
Eskildsen: Turbopuffer reduced Cursor's infrastructure costs by 95%
“They migrated everything over the next like week or two, and we reduced her cost by 95%, which I think like kind of fixed their per user economics.”
Eskildsen: Cursor obfuscates file paths and encrypts code stored in Turbopuffer
“Cursors like security posture into Turbo Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths. They are like, You, it's very difficult to learn anything about a code…”
Eskildsen: Turbopuffer cuts query pricing 5x for agentic workloads
“We've reduced query pricing. This is probably the first time actually I'm saying that, but the query pricing is being reduced. Like, Five X. And we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts o…”
Eskildsen: Anthropic, Notion, and Cursor use Turbopuffer across three deployment models
“You can run Turbo Puffer either in SAS, right? That's what cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC. That's what, fo…”
Eskildsen: Turbopuffer interview debriefs default to rejecting candidates unless championed
“And so I have a document called Traits of the P-Nine engineer, and it's a bullet point list, and I look at that list after every single interview that I do, and in every single recap that we do, and every recap we end with, I end with some version of, I'm gonn…”
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Eskildsen of Turbo | Mar 12, 2026 | 45m |