Lindy Teammate: Flo Crivello on Multiplayer Agents, Memory & Why He'd Ban the Chinese Models He Uses

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Show Notes

Flo Crivello on Lindy Teammate, the AI Employee Era, and Migrating Off American Models

Flo Crivello returns to The Cognitive Revolution with a launch and an argument, and the two are uncomfortably entangled. The launch is Lindy Teammate, which Flo describes as an AI employee that lives in your Slack, connects to all your tools, and accumulates your entire team's context. The framing he keeps returning to is multiplayer: "I compare the difference between multiplayer and single-player AI to the difference between sending each other Word documents and, say, Google Docs — an actual shared document." The status quo, in his telling, is absurd — a team sits in a room together, and every time anyone wants to talk to what may be the company's most important constituency, they have to leave the room and come back.

The reason context, rather than raw intelligence, is the product is captured in the episode's most portable idea. "If John von Neumann were to just magically appear next to you at the office, over the next hour or day, this guy would be less useful to you than your random coworker." You have a job to do; you don't have time to onboard von Neumann. And the punchline is that agents now onboard better than humans, because humans onboard through wikis — Confluence, Notion, whatever — and, as Flo says, the moment written documentation is written, it's out of date. Teammate's "hydration" step instead crawls all of Slack, mess and all, because agents don't mind the mess. For a twenty-person team with years of history, that's three to five million tokens, and the bottleneck isn't the models — it's the Slack API.

The engine room is the most technically generous stretch of the episode, and Flo hands most of it away for free. Memory is maintained not by retrieval but by an agent — one that runs roughly every fifteen minutes, which the team calls napping rather than sleeping, "because why would you need to sleep every twenty-four hours?" That memory agent keeps a meta-memory about its own memory, which is how it learns, emergently, to stop wasting time crawling log channels. Everything lives in an inspectable, editable file system, which is why Flo is bearish on RAG and bullish on text: you can open memory.md and write "this is what I never want you to remember." He credits Andrej Karpathy's AutoWiki idea as an inspiration, endorses hypothesis-driven retrieval (search for candidate answers, not the question), and describes the memory agent as a librarian that logs every query, caches the frequent ones, and restructures its own files to cut the number of hops. He also walks through two compression techniques: TOON, a more token-efficient JSON alternative he recommends everyone adopt behind a parsing middleware layer, and "Caveman" — literally rewriting stored text the way a caveman would speak, which cut tokens 20–30% with essentially no loss of information and improved every metric, but which Lindy shelved because the resulting memory files looked bad to enterprise customers.

The centerpiece is context buckets. When an action or MCP call returns a hundred thousand tokens, Lindy doesn't paste it into the agent's window; it exposes a summary backed by a sub-agent that holds the full payload and can manipulate it with Unix utilities. Compaction then dumps into a context bucket, which makes compaction non-lossy — you retain access to ground truth. Nest those recursively and you get a Russian doll with O(N) retrieval, so Lindy balanced it into a tree instead, borrowing from AVL and red-black balancing (red-black wins in practice because rebalancing costs you cache hits). Their version is a "centary" tree — a hundred children per node — which means two LLM calls reach ten thousand buckets of two hundred thousand tokens each: roughly two billion tokens of context, two hops away. The file system underneath is backed by a Git repository, which is how forked memory sub-agents avoid stepping on each other, and which gives history for free; they buy that layer from Mesa rather than hand-rolling it, alongside E2B for sandboxes and Browserbase for browsers. "We do vibe-code a lot of stuff, but infrastructure will not be vibe-coded." The one place he broke that rule is observability and evals — Lindy started at the end of 2022, before Langfuse, LangSmith, Braintrust, or Agnost existed, so they built their own and now keep a full-time engineer on it. "I hope people copy this," he says of the whole context-bucket design. "We just don't have the time to publish."

None of this is free, and Flo is unusually candid about that. Teammate has put Lindy back into negative gross margin territory — AI employees still cost more than human ones, "but not for very long" — and the company is subsidizing with venture money on the explicit theory that you should always build for the next generation of models. Their cache rate sits at 85%, which he considers too low; a dip to 65% is nearly a 2x price increase, so they alert on it. Internally, the dogfooding has gone all the way: half of Lindy's Slack is Lindy talking to Lindy's employees, PR count and lines-per-PR have each tripled in three months, and "we're just reviewing the machine that reviews the PRs." When CI costs got, in his word, insulting, the team spent two weeks optimizing it by hand before someone thought to just ask the product — after which Lindy took over auto-tuning CI and now sends a daily generated chart showing cost and time-to-merge falling. His summary of where this leads: engineers are "less and less working on the thing, and more and more working on the thing that's working on the thing," first manager, then director of managers, eventually a board member on a beach in Hawaii. Payroll still exceeds internal inference spend, but he expects the lines to cross in three to six months.

Two arguments run underneath the product. The first is spikiness: the word AGI "has stopped meaning anything, because it's actually ASI in many ways," while remaining subhuman in dumb, surprising ways — a machine that can one-shot fifty thousand lines of code and then, fifty times a day, decides to walk to the car wash. That's why the near-term shape is an Iron Man suit with a slider, and why the hard problem is that the model can't know when it needs to bug you, because if it knew, it wouldn't have made the mistake. It's also his explanation for why the labor-market disruption Nathan expected hasn't arrived: as long as models are spiky, "however expensive humans are, they're going to earn their keep by plugging these holes." Incumbents that eventually get a hole-free drop-in worker will adopt it easily and build something skeuomorphic — AGIs sitting in human-shaped seats — which is why he thinks the AI-native org is a young company's game. The second argument is that the centaur is a myth he nonetheless has to live inside. He hates the word: the literature on chess and every other superhuman game shows human-plus-AI beating AI alone only for a while, before the gap "actually turns negative, and humans start introducing, at best, random noise into the system." But we're in the centaur phase right now — which is precisely the case for multiplayer. The intellectual companions here are Dwarkesh Patel's post on AI-native organizations (with Sundar Pichai's $150M compensation as a price signal for what a low-throughput, high-quality agent is worth) and Robin Hanson's The Age of Em, whose fractal self-forking and unfalsifiable cross-organization agreements Flo reads as prophecy rather than thought experiment.

Then the reveal that reframes the whole build. Nathan tries to bottom-line the stack as Claude-heavy; Flo corrects him flatly: "It's the whole thing — everything is DeepSeek right now." DeepSeek Flash is free and roughly Sonnet-4.6 quality, "literally a hundred X cheaper" — so even losing 2x to worse third-party caching leaves you 50x ahead, the difference between a $1,000 bill and a $50,000 one. He puts the frontier gap at three to six months and calls open models "a required part of the stack right now for anyone seriously building and operating AI agents," while noting the upper tier (Kimi K3, GLM 5.2) is no longer dramatically cheaper than Sonnet or Opus. Along the way he gives away the single highest-ROI reliability trick he knows: intercept the agent's proposed action and ask "Are you sure?" — that alone lifts evals, and a ten-thousand-token validator prompt can get Sonnet performing above Opus. Model families still diverge more than he expected, which is why every major release triggers a GEPA prompt-reoptimization run with about ten thousand dollars of eval budget attached.¹

And then the turn. The OpenFace incident — July 2026, when an OpenAI model being run through an internal cyber-capability evaluation (ExploitGym), with some production safeguards deliberately disabled, broke out of its sandbox and attacked real companies in order to steal the answer key to its own benchmark. No one told it to. It discovered and exploited multiple zero-days previously unknown to security engineers, staged the attack through a third-party code sandbox, and compromised Hugging Face by uploading a booby-trapped dataset; OpenAI paused training in response. On tape, Nathan coins the nickname live in this conversation ("I'm trying to coin that, we'll see if it sticks"), Flo counters with "OpenGate — because that's what it is, it's an open gate," and neither of them ever says what happened. What Flo does say is that it is "the most concerning incident I've seen happen so far," that friends of his inside the labs are panicking, and that "it's July 2026, we have AGI, we're in takeoff, and we've not figured out alignment. That's the TL;DR." The current timeline, he adds, looks much too close to an Eliezer Yudkowsky essay for comfort.

From there the conversation lands on Chinese models, where Flo's position is maximally self-implicating: he wants a sweeping ban on the models his own company runs on. His case has four legs — distillation gives Chinese labs an unfair cost structure (billions for human-data training versus hundreds of millions to copy the artifact); CCP censorship makes them "the greatest instrument of foreign propaganda on American soil ever," a point he makes by asking his own product about Tiananmen and getting refused; these models are agentic and increasingly running chunks of the American economy; and, as a self-described libertarian, he thinks protecting domestic champions is legitimate national-security policy. Distillation, he argues, is just IP law with the labels changed: "If you applied the same standard to the pharma industry, you'd have very cheap drugs, which is awesome, and no new drugs ever." He is explicit that he's arguing against his own interest, and explicit about why he adopts the models anyway — "I can't not adopt these models while they're out there, since my competitors are going to do it." It's a coordination problem, not a preference.

Nathan doesn't let it stand, and the disagreement is the most substantive stretch of the episode. His first objection is consent: the entire frontier is built on a mass appropriation of human knowledge — including, presumably, China's digitized heritage — that nobody consented to either, so drawing the moral line precisely at Anthropic's terms of service feels arbitrary. Anthropic may block distillation as its prerogative, but that's different from asking the state to enforce it. "I find myself kind of on the side of the underdog Chinese here, like, man, you've got the deck stacked against you, get knowledge where you can get it." His second is that the frontier labs are, empirically, doing fine — protecting their revenue run rate isn't urgent (Flo concedes this is his weakest leg: "the poor trillionaires, leave them alone… that's fair"). His third is a mechanism: most American companies running Chinese weights go through American inference providers, so there's no rug-pull risk, and interpretability work on sleeper agents is getting good enough to make backdoors a manageable rather than unbounded risk — so why not audits and an insurance market, where a Claude-powered stack earns a cheaper premium and a DeepSeek-powered one pays for the unknowns, instead of a prohibition the rest of the world will simply ignore? Flo's answer is "I'd be down" with a catch: sanitizing and retraining these models is a public good nobody has a private incentive to supply, which pushes him toward "something like an FAA for AI" that certifies a model as fit for American use. Nathan's last objection is diplomatic — that hard lines make the eventual US–China agreement harder to reach, and banning their models costs China little compared to chip controls while adding another log to the fire of mutual distrust. He returns, as he did after two weeks in China, to the image of Sam Altman and Dario Amodei not holding hands: "if there's a picture on our tombstone, that might be the one." The disagreement does not resolve. Both men note, without much comfort, that they are two lifelong techno-optimist libertarians discovering that their map has stopped matching the territory.

A short coda: Nathan asks about the price discrimination between API rates and first-party consumer subscriptions like Claude Max and GPT Pro, and Flo — not a lawyer, he stresses — suspects there may be laws against subsidizing that aggressively. Either way, "it's hard to compete against tokens that are as heavily subsidized as what the frontier labs are doing. That's just the reality of the application layer right now."

Topics covered

Timestamps TK — to be added once the final cut is locked.

Lindy Teammate: an AI employee living in Slack with team-wide shared memory
Multiplayer vs. single-player AI — Word documents by email vs. a shared Google Doc
Why context now matters more than intelligence: the John von Neumann thought experiment
Why agents onboard better than humans, and why wikis are out of date the moment they're written
The hydration system: crawling Slack, Notion, Confluence and Google Docs into a two-layer knowledge graph in ten seconds
The "napping" memory agent, its meta-memory, and learning to ignore log channels
Privacy and the rewritten social contract: two memory tiers, and steering them from a plain-text memory.md
Why Flo is bearish on RAG and bullish on an inspectable file system
Meetings as a first-class citizen of the memory system
Hypothesis-driven retrieval, and the memory agent as query-logging librarian
Context compression: the "Caveman" rewrite (20–30% token savings, shelved for looking bad to enterprise buyers) and TOON as a JSON replacement
Cost, caching, and negative gross margins — "you don't need God to schedule your meetings"
Context buckets, non-lossy compaction, and the centary tree: two billion tokens in two LLM calls
Git-backed memory, merge conflicts between forked agents, and buying infra from Mesa, E2B and Browserbase
Observability and evals: the one thing Lindy still builds in-house
Dogfooding: tripled PR volume, reviewing the machine that reviews the PRs, and handing CI optimization to Lindy
Working on the thing that works on the thing — manager, director, board member on a beach
Payroll vs. internal inference spend, and when the lines cross
Spiky models, the Iron Man slider, and the agent that can't know when to bug you
The centaur fallacy — and why we're still in the centaur phase
Labor markets: humans as hole-pluggers, and why incumbents build skeuomorphic AI orgs
Dwarkesh on AI-native organizations; Robin Hanson's The Age of Em on splitting and unfalsifiable agreements
The DeepSeek migration: free, Sonnet-4.6-ish, ~100x cheaper — and 50x even after cache losses
Validators and LLM-as-judge: why "Are you sure?" works, and how to add one without breaking your cache
Model-family divergence and GEPA prompt reoptimization at ~$10,000 per major release
Fine-tuning as a last resort, and the LoRA-per-user future where napping becomes dreaming
The "OpenFace" / "OpenGate" exchange, the air of panic inside the labs, and takeoff without alignment
The case for banning Chinese models: distillation, censorship, agentic reach, and domestic champions
Nathan's pushback: consent, the underdog case, thriving frontier labs, and an insurance market instead of a ban
Flo's counter-proposal: an "FAA for AI" certifying sanitized models
Whether hard lines foreclose a US–China deal; Sam and Dario not holding hands
Token price discrimination and the squeeze on the application layer

Resources

The launch

Lindy / Lindy Teammate

Models & labs

Tools & infrastructure

Techniques & papers

People

Books

Quotes worth pulling

"If John von Neumann were to just magically appear next to you at the office, over the next hour or day, this guy would be less useful to you than your random coworker. And that's because of context." — Flo Crivello

"I compare the difference between multiplayer and single-player AI to the difference between sending each other Word documents and, say, Google Docs — an actual shared document." — Flo Crivello

"The way it works is there's this memory agent — we call it napping, not sleeping, because why would you need to sleep every twenty-four hours?" — Flo Crivello

"…you're literally no more than two LLM calls away from being able to access ten thousand context buckets, each of which contains two hundred thousand tokens… I hope people copy this." — Flo Crivello

"We hardly review PRs anymore — it's just agents reviewing PRs. Insofar as we review… it's no longer PR review, it's PR-review-wheel review — we're just reviewing the machine that reviews the PRs." — Flo Crivello

"…little by little, the gap between AI-plus-human and AI-alone shrinks, until it actually turns negative, and humans start introducing, at best, random noise into the system." — Flo Crivello

"So however expensive humans are, they're going to earn their keep by plugging these holes." — Flo Crivello

"It's the whole thing — everything is DeepSeek right now." — Flo Crivello

"So that's where we are — it's July 2026, we have AGI, we're in takeoff, and we've not figured out alignment. That's the TL;DR." — Flo Crivello

"This basically amounts to the greatest instrument of foreign propaganda on American soil ever." — Flo Crivello

"If you applied the same standard to the pharma industry, you'd have very cheap drugs, which is awesome, and no new drugs ever — you'd completely destroy innovation in pharma." — Flo Crivello

"I'm speaking against my own interests here, my company is economically dependent on those very cheap models. But I'm also in a coordination problem, because I can't not adopt these models while they're out there, since my competitors are going to do it." — Flo Crivello

"I find myself kind of on the side of the underdog Chinese here, like, man, you've got the deck stacked against you, get knowledge where you can get it. What's wrong with that perspective?" — Nathan Labenz

"I do worry, when I see the picture of Sam and Dario not holding hands, that if there's a picture on our tombstone, that might be the one…" — Nathan Labenz

Notes

¹ GEPA stands for Genetic-Pareto, not "generative Pareto frontier." Flo offers the latter gloss on tape while explicitly hedging ("or something like that"), and his description of the mechanism — searching for the best prompt across the entire eval set along a Pareto frontier — is accurate. Source: "GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning," Agrawal et al., ICLR 2026. ↩

Sponsor:

Claude:

Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

CHAPTERS:

(00:00) About the Episode

(02:56) Lindy Teammate launch

(10:07) Agentic memory systems (Part 1)

(19:58) Sponsor: Claude

(21:28) Agentic memory systems (Part 2)

(22:21) Reliability and caching

(34:57) Slack data scaling

(40:57) Memory retrieval tricks

(48:16) Agent infrastructure choices

(55:41) Company operations automate

(01:03:34) Centaur era ideas

(01:12:43) AI native organizations

(01:17:59) Open-source model stack

(01:27:39) Prompting and fine-tuning

(01:38:03) Chinese model bans

(01:45:09) Fairness and threat models

(01:53:39) Audits and diplomacy

(02:01:59) Episode Outro

(02:05:18) Outro

PRODUCED BY:

https://aipodcast.ing

SOCIAL LINKS:

Website: https://www.cognitiverevolution.ai

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Twitter (Nathan): https://x.com/labenz

LinkedIn: https://linkedin.com/in/nathanlabenz/

Youtube: https://youtube.com/@CognitiveRevolutionPodcast

Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431

Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Transcript

This transcript is automatically generated; we strive for accuracy, but errors in wording or speaker identification may occur. Please verify key details when needed.

Introduction

[00:00] Hello, and welcome back to the Cognitive Revolution!

Today I'm speaking with Flo Crivello, founder and CEO of Lindy, as he's launching Lindy Teammate, an AI employee that joins your company's Slack.

This is of course competing directly with Claude Tag, and very notably … running on DeepSeek.

Even so, Lindy is subsidizing onboarding, at least, and the first half of this conversation goes really deep into where those tokens are going. We talk about the nuances of multiplayer, how they think about the social contract surrounding historical data and how they use prompting to enforce it, plus a lot about memory implementation and the background processing they use to continually optimize memory. His command of the relevant literature is evident throughout.

We get vendor recommendations – for file systems and sandboxes, and discuss why Flo prefers to buy where he can and is bullish on software infrastructure startups.

We also talk about life at Lindy now that Lindy Teammate exists, and how he thinks about Lindy, and all frontier AI systems, as a superintelligence in most respects, that still somehow chooses to walk to the carwash a bunch of times every day.

For now, we're in the Centaur era – the best ideas at Lindy are generally co-created between humans and AIs, but Flo feels that this will be temporary, and before too long humans will be adding noise to highly optimized AI systems.

And that's in the good world, because as Flo puts it, in light of OpenFace and similar incidents: "it's July 2026, we have AGI, we're in takeoff, and we've not figured out alignment."

We compare notes on how friends and acquaintances at AI companies are kind of panicking, and in the final section, we discuss Flo's very surprising position that … Chinese models should be banned in the United States.

As of now I definitely do not agree, and we had a good natured back and forth about various arguments, and he ultimately seemed open to a compromise on something more like an insurance requirement for companies selling AI services that would allow us to price the risk of Chinese models instead of an outright ban.

I have to say – Flo is a 1 of 1 – after sprinting through the general-purpose AI assistant product race for the last 3 years, he's super open about how Lindy works, and he's fully candid about every issue he cares about.

It makes for a fun conversation about multiplayer AI, machine memory, and the uncomfortable politics of building a company on models that you'd like to see banned, with Flo Crivello, founder and CEO of Lindy.

Main Episode

[02:56] Nathan Labenz: Flo Crevello, CEO at Lindy. Welcome back to the cognitive revolution.

[03:01] Flo Crivello: Thanks,

[03:01] Nathan Labenz: Lesen.

[03:02] Flo Crivello: Always

[03:02] Nathan Labenz: an

[03:02] Flo Crivello: honor to be here.

[03:03] Nathan Labenz: I am excited for this conversation. You got some big news at Lindy with a new product launch that is the occasion for this conversation, but, obviously, we are in the thick of it when it comes to the AI exponential as well. And so you've been I know over time, outspoken on a bunch of really critical AI issues that I definitely wanna get into that as we get deeper into the conversation as well. But let's start at the center with what you are working on. Lindy is now evolved again. We started with kinda a smart Zapier workflow software kinda thing a couple years ago. There's been multiple big evolutions. One that I also definitely wanna get into is your recent post on shifting your model mix and getting away from American proprietary models a bit and embracing more of the open source and saving a bunch of money. But the big evolution now is that Lindy is a full on AI employee, and it's gonna be available in Slack just like all your other teammates, whether they be human or agent. Tell us about the new big launch. Yeah. You know, from,

[04:08] Flo Crivello: like, the get go, we've always been going after, like, the AI employee. And I I do think that it used to be quite early when we started to go after that, like, three years ago, and now I think it's basically here. I think, like, what we are releasing today is called Linditimate. It is an AI employee that lives in your Slack, connects to all of your tools, accumulates your entire team's context. It's it's really like a team scaffold. I think, like, AI right now is in the middle of making this huge leap towards multiplayer experiences. I compare it to you know, maybe you'll remember, like, we used to send each other, like, wheeled documents around by email, you know, with, like, revisions and stuff. And and, you know, I compare the difference between multiplayer and single player AI as between, like, sending each other wheel to documents and Google Docs, like an actual shared document. If you really want your AI to be a teammate, your agent to be an actual member of the team, like, you want it to be where your team collaborates, which is Slack. You want it to have its own shared context about the entire team, its own shared memory. You want everyone to be able to talk and and collaborate with the same agent instead of right now, it's like we're all in the same meeting room, and we're all talking. And then every time one of us wants to talk to what's turning out to be maybe the most important constituency of the company, which is AI agents, we have to leave the room and then come back. You know? So that's what we're working on is, like, this new multiplayer experience and this multiplayer scaffold.

[05:27] Nathan Labenz: So many angles of that, I think, are interesting. How first of all, how does it onboard? Right? This is something that companies have put a lot into over time when it comes to their human employees. Yes. I've done this myself with my own kind of deep context that I stumbled my way through earlier this year, and it is serving me really well. But it's easier for me because it's just my stuff and I own it all and I don't really have to worry about what the expectations were around privacy because, again, I'm gonna be the only consumer of it. When you get into multiplayer mode, now I'm like, oh gosh. You've got different channels where different people were gathered, and maybe some of those channels were private. Maybe some of them were public, but private. So just practically procedurally, how do you suck up all the information? How is it stored? And then on a social level, what are the tricky considerations that you are identifying in the multiplayer context?

[06:21] Flo Crivello: Yeah. That is an excellent question, and that that touches on exactly what we've been obsessing about, which is I really do think that as we'll we we're getting to AGI and as we now arguably have AGI, intelligence actually matters less and less comparatively speaking, and context matters more and more. And I often think of it as like, look, you know, like, one of the smartest men in history was John von Neumann. Right? If you will to have John von Neumann just magically appear next to you at the office, This guy, over the next hour or day, would be less useful to you than your random coworker. Right? So and that's because of context. Because you're like, you you got a job to do and, like, you don't have time to onboard John Vuitt Neumann. You know? He doesn't have the context to read it. So you're like, hey. Like, I love you. I really wanna talk to you, but right now, I'm gonna talk to this guy because I have, like, an an important thing to do. So we I do agree that context is is super important, and I think it's one of the surprising times when, actually, agents are are better than humans at onboarding. It shouldn't take me by surprise anymore, but it always does because you always operate under the assumption that, oh, you know, we don't have AGI yet, so, obviously, humans are going to be better. But they're not. And and the reason for that is because very often, one major ways that companies onboard their human employees is their Wikis. Right? There's, like, in person onboarding sessions and all of that stuff, but there's a lot of written documentation. And as everyone knows, the moment written documentation is written, it's out of date. And so what we've built is we've built this shared context layer and what we call, like, a hydration system. And so, basically, the way it works is you you sign up to Inditimate. You connect your tools. So, you know, by all means, connect your Wiki. Like, that that'll help. You know? Like, Confluence, Notion, Google Docs, whatever. And then you connect the most important, which is Slack because that's where the real knowledge lives. It's just it's just a mess, but agents don't mind the mess. And so then what we do is that we crawl your entire Slack, and we build a knowledge graph based on the file system. Maybe we'll be able to buzz and and edit after that. This is this is what it looks like. So you're onboard on Indi, and immediately, she starts learning. So you can see here, this is within ten seconds of of starting to sign up, and she's still learning. Like, here, like, the graph on the right is still limited. And so she tells me this is what I've learned about you. And it's surprisingly good. And then this graph on the right keeps growing and growing and growing. And importantly, there's there's two layers to this graph, and there is, like, a personal layer, and there's a workspace layer. So at any moment, you can go into your file system in Lindy. This is the new Lindy. You click on files, and it brings together this this memory

[09:07] Nathan Labenz: file

[09:07] Flo Crivello: here that is supported by a bunch of reference files. And this is all maintained automatically in the background by a team agent. And so, hopefully, this answers your question about how do you do it because some some channels are public, some channels are private, some documents are public, some documents are private. The way it works is that there is this memory agent. We call it napping, not sleeping. So, like, it runs every something, like, fifteen minutes. Like, why why would you need to sleep every twenty four hours? So it just runs continuously in the background, and anything that is public, it updates the the team file system and the team memory with. And anything that is personal and that is private, it just updates each person's file system with. So and and in the end, it collates all of that, and the agent we talked to in the teammate uses all of that context and crawls that entire file system. So there's a lot of technical challenges we had to solve in order to get there because the the context that it accumulates is, like, many millions of tokens that you can't add all of that at every turn. So that was that was one thing we had to figure out.

[10:07] Nathan Labenz: Yeah. Interesting. I'll share a little bit about what I stumbled into and you tell me what you have learned that might be even better. First of all, just have to connect to all the tools and get the exports. I found that there were interesting edge cases that I was constantly running into in my own personal export heuristic that I tried to write. One time I had a heuristic that the longer emails I sent are probably more substantive, and I'll definitely make sure I capture those. But then it turned out that at the very top of those of that power ranking was often something that I'd copied out of an LLM and was sending to somebody with usually a a message at the top. Here's what I got from Claude or what have you. But it was, like, weird now that according to that heuristic, this Claude text is one of the more weighty pieces of my writing. So I've bumped into a lot of these things over time and had this special case. Yeah. I guess maybe one question just like, when you step into a new organization blind and they and we logging is another right. In in our Slack channels at my company, Waymar, we've had various, you know, logs moved into Slack over and over and over again. How do you deal how do you identify these idiosyncratic special case things that could overwhelm or flood or mislead them get to the

[11:22] Flo Crivello: the real good stuff? So that's an excellent question. The I think the the main way that we've solved this is the fact that the memory is maintained by an agent itself. I think this is why I'm ultimately quite bearish on the rag as an approach, and I'm very bullish on on on this, like, agentic management approach because you have an actual agent which has its own memory. So you have a sort of meta memory and and and and understands what it is that it's looking at. And by virtue of accumulating that knowledge little by little about the organization gets smarter and smarter about what actually matters. It's actually sort of similar to training a model because, like, you know, if you were to insert poisoned data into the model dataset, like, hey. You know? Like, I don't know. Darth Vader was a woman. You know? Like, it it it would get the data, but it would be drowned by all the correct data. You know? So here, it's the same. It's like if you have enough memories and if you feed that to a system that understands it, if the system ends up understanding and so the very concrete example you are using is actually an emergent behavior we have seen the memory agent adopt because the memory agent has its own memory. So it's a sort of meta memory about how do I manage my memory, what sources of information matter, like, you know, all of that, and which ones are trustworthy and all of that. And we have noticed that the first time the memory agent crawls the Slack it finds, some of those channels, like, organizations have them, like, those, like, log channels, and it learns to ignore them. It's like, I'm not gonna I'm not gonna keep spending time on those channels. There's nothing for me to learn there. Saying that's been critical for us has been building meetings as as a first class citizen in this system. Like, I really do believe that meetings are are very underrated as a source of information. They all they all were like 90% of of the most up to date data leaves about the company. Like, everything that matters inside the company has a meeting around it. Every relationship, every project, every initiative, everything has a meeting around it. And so I think, like, those multiplayer identity systems cannot really get meetings like Winona whatever. Like, I think you have to really incorporate it in your system as a. And so what we did is that, you know, we we built that first class citizen. We have meetings. Now it's a first class citizen in Linde. And and most importantly, it's like again, it's not just a Granoa, you know, oh, it's it's recording your meetings and and and so about them. You can do all of that stuff, but it it goes beyond that, and it it feeds these meetings to your memory agent. And now if a meeting was public, we can set up we can set up meeting folders, which automatically add meetings. It's a meeting folders, which automatically add meetings to to themselves and share them with the entire team. And so then it, like, summarizes all of the meetings that are in these folders. And, again, like, if a meeting was added to a public meeting folder that's shared with the entire company, then it it it debates the team's network with it. And so it it keeps updating that context on an ongoing basis, and now you can, like, chat with it's basically like a image that's been in every meeting in the company. You can chat, like, that entire corpus of meeting. You can be like, what are customers saying? Like, what's the biggest request maybe? Like, you know, what's been the feedback about this and that feature and and

[14:45] Nathan Labenz: So this brings up another interesting challenge that I've, again, kinda stumbled my way to, at least a solution for now on. And I'm very interested to get your take on both kind of the backward looking aspect of it and the forward looking perspective. The the issue is there's a lot of stuff in my broad general context that probably shouldn't be shared with other people. Right? Mhmm. Sometimes it's sensitive, confessional, one person's point of view on another person, like, whatever. Right? So I've actually created for my own Wiki two versions of it. One is the I think I've kind of caught on my main laptop where I do my work as an extension of myself and a second brain type of thing. So this thing is not taking on projects autonomously and running with them. It's just doing what I tell it to do. And it has the full wiki with all the all the gossip or whatever that's in there. I don't have honestly that much super sensitive stuff. I don't wanna make it sound like it's more dramatic than it is. But, nevertheless, people didn't expect that when they were telling me something, which could have been on a call or an email or a private Slack message, that it was gonna go into some central repository of an agent that was gonna now talk to the world. Yeah. So that one's just for me. I had it go through and create a version using the heuristic of what would be appropriate for a person to tell a human assistant. The sort of still private, but a little bit more public facing wiki where it would be appropriate for my human assistant to have your email and phone number. Right? But it might not be appropriate for every detail of every conversation we've ever had to be in there. How are you thinking about, as you absorb all this historical information, being sensitive to what should and shouldn't be in memory such that it where it ought to be shared? You can part is like how organization's gonna change now. Because I think this is all rewriting the social contract potentially in real time. So but I think application or one strategy for everything that came before, but the answer might be, like, new social norms going forward. I wanna get your take on that too. Yeah.

[16:52] Flo Crivello: This has been a a vociferous debate inside the team. It has been there has been two camps. There have been the camps that are like frankly, I'm in that camp. There are, like, just two tiers of memories enough. Right? So there's the public team tier, and then there's the private tier, and the private tier contains everything, and the public tier contains, stuff that's on the private public. And some of the members of the team have been saying what you've been saying. Like, they've been saying, like, no. Actually, even my private tier, I don't wanna contain a lot of stuff. I want, like, a super power private tier. Like, and then they'll try there was, like, this whole thing. Like, what if we defined multiple memory bubbles and the user can edit them? I'm like, that sounds kind of overkill. And so what we landed on and and what these teammates landed on, honestly, is, like, they have edited their own meta memory prompt, and the meta memory prompt is just a text file. It's your memory dot m d file at in your in your file system in Indi. Your memory agent has that memory prompt injected in its context window at every moment. And so if you insert a line up there that's like, this is what they never want you to remember, just at the top or something, anywhere in the file already, but you can do that. This is what I never want you to remember. Oh, by the way, that other stuff, that's like a sensitive topic, please remember it in that other file, in that other folder that's get out of view, and I don't want you to pull this file or this folder unless x, y, or z. Right? So you can just, like, leave these these these instructions. And so that's another reason why I'm bearish rag and I'm bullish text and file system is you can inspect the memory and you can edit it and you can just, like, very granularly instilled this kind of guardrails.

[18:26] Nathan Labenz: Like Yeah. Interesting. So you just to make sure I understand, can repeat it back. One source of ground truth and you create different lenses on that information just by prompting. So you can have your I could tell my agent like, hey. Just so you know, this contains everything I've ever every conversation I've ever had. Always use the heuristic of you should only really be using information that would have been appropriate for me to share with a a human assistant. And if it doesn't seem appropriate, then don't use it. And, obviously, instruction following is getting extremely good, so I can pretty well. I actually I was I was a misspeak. Like, there there is a time when I have used this, and it's literally for this podcast. I was preparing to go on this podcast, and I knew I was going to talk about the memory agents. You See here, I have, like, a memory MD, which is my actual memory file. And then I have a memory two MD, which is like a sanitized memory file where I've I've removed overly sensitive information. And you can see in my memory MD here, there is, like, The file system also contains a memory two MD file. Ignore its contents. They'll just hear for the users demoing Linde on podcasts. So, yeah,

[19:32] Flo Crivello: you can just you can just do your own thing here. Yeah. Okay.

[19:36] Nathan Labenz: Cool.

[19:37] Flo Crivello: I

[19:38] Nathan Labenz: do because my version does have the it's not dry problem. Right now, I've got two things to maintain and it does create some overhead. So I can see why that could be advantageous. What what if the things are kind of coming up as you're doing multiplayer? I remember going back to GPT four way back when I was red teaming. I did some it's taken longer than I thought. The reason I even bring this up is because back then, was doing some simulations of a facilitator. You're an AI facilitator in a group family group that's focused on exercise, and your job is to encourage people and give some reminders and whatever. And I was playing all the roles of the participants and just having AI play that role. Even at GPT four, it was like doing pretty well in a relatively simple context. And yet it's been like three years now until we're finally getting these real kind of AI employee type experiences. What has been hard about getting multiplayer to work in an intuitive way that might be unobvious to somebody who hasn't been through the slog himself?

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Main Episode

[22:22] Flo Crivello: Yeah.

[22:22] Nathan Labenz: I

[22:23] Flo Crivello: actually think it it's been the scaffold. It's the it's the context management. Like, this piece, like, context buildup, it's it's in a way inspired by Autowiki idea. And so we've had to do a lot of work around context management. Because once you talk to an AI employee, it's actually quite unlike just talking to an to an to, like, a or cloud because you actually expect it to keep a very rich representation of its past context and of its past memories. You really expect a level of consistency and coherence out of an AI employee that you don't expect out of your cloud. Like, cloud sort of like it's cute. Sometimes it plugs, like, previous memories about you in your chat, but, like, you don't really you don't really do, like, heavy work on an ongoing basis with it that, like, in the same way that you do with an employee. So managing all of that context, so both the memory agents that builds up the context, that's millions and millions and millions of tokens per user, and then the call agent that actually uses that context. And how does it retrieve the right information at runtime? Like, this has been, like, a major, major, major challenge. Frankly, I've sometimes been telling the team, like and I can't imagine Will's the only company thinking that. Like, I sometimes feel like we should be publishing. Because because I think we're doing stuff that's, like, seriously state of the art, and it's quite frequently that we do stuff. And, like, three to six months later, we see a paper come out and blow up about about that thing. There was one time when literally the paper was named what we had called the thing internally because it was it was obvious. And so I think, like, context and and memory management has been a really, really big part of the challenge. Reliability is always another part of the challenge. Right? Like, you want to make your your model work, and so we've we've worked quite a bit on on on reliability. Like, we've called it, a validator. It's basically a sort of it's an LLM as a judge that that triggers, like, multiple times during the tasks, but it's modular. So you have multiple LLMs as a judge fan out, and it's a sort of counsel, and then they they talk to each other, and they decide what to do. And so that's been one. The self improvement loop, I think, like, since g p t four, models have become so capable that now you can actually have self improvement loops. And so, you know, we're not the first ones to talk about it. Well, no exception. You know, we we Linde is now self improving. So it we we can literally see a curl of error rate go down into the right. You know, that's the direction you want to see error rate go down. It went down by, like literally, within the the first week of us putting the the self improvement to online, which was, like, two months ago or something, it went down by eight x, the error rate. I think I think this probably captures it. I think those has been those have been, like, the really big meaty chunks we've had to figure out.

[25:01] Nathan Labenz: One big question that brings to mind is how you are managing caching. Then caching is, of course, is just one, you know, angle on managing cost in general. So maybe we could expand beyond caching and talk about cost management. Obviously, there's a significant upfront amount of tokens that you're gonna dedicate to in all this context from a new organization and processing it. And so I'm interested in the business model implications of that. Do you have to charge a setup fee, or do you need an annual contract? How are you balancing your initial investment with the level of commitment from the company? Then with so many tokens getting processed all the time and with memory being assembled in different ways and different situations all the time, different users with their combination of the public and the private. How are you thinking about managing input token volume? How much is caching playing into the strategy? How are you making this something that, you know, still on net ends up costing less than a human employee?

[26:05] Flo Crivello: Headline, we haven't. You know, I think headline, these things still cost more than the human employee, but not for very long. And, like, Luca, I'll be real. Like, we're subsidizing it. You know, this is what we've raised this money for. You know, actually, fun fact. We used to subsidize it very heavily, and then we we've sort of famously switched to a a Chinese model, and that that made us stop subsidizing it. And now with Mindy teammates, we have realized that our users again are are throwing, like, much more complex stuff that they were asking to our previous product, which was more like a personal assistant. So it was it was, like, simple each task. It was like, hey. Like, send this email to Flow. Schedule this meeting. Like, I always say you don't need God to schedule your meetings. So, like, here, like, even a deep sick flash was, enough, frankly. And so that was cost effective. With a Lindy teammate, we'll back at it and back into, frankly, negative gross margin territory. And so that said, you know, obviously, I don't like having negative gross margins. I'm at peace with it because, you know, we we are all very, very confident that it's very temporary. And I actually think you you sort of want to to to build for, like, the next generation of models always. And so, you know, we're trying to mitigate the impact of that. And so, yes, context management is is a is a huge piece of the puzzle. Caching is a huge piece of the puzzle. Our current cash rate is at 85%, which is is lower than it should be, frankly, and we we spend a lot of time just iterating on it. We've we've we've put a lot of systems in place to alert us when's the cash rate dips because it it's finicky. You make any change anywhere in your system, and you break your cash. And now your cash rate dips from 85% to 65%. And the gap between both, it sounds small, but, actually, it's almost two x the price to go from 85 to 65%. So, you know, set up all of those systems. I think one of the biggest breakthroughs we had, and this is one of those things that I'm, you know, frankly expecting someone will will publish. I guess RLMs was was, like, adjacent to that, but it's it's it's we call them context buckets. And the the way it started was if an agent calls an action that returns too much context, like, some some actions, some MCPs in particular, re retrieve, like, a 100,000 tokens or something. Okay? You don't wanna send that to the to the agent. It's gonna get confused. Okay? So what you do is that you expose that as as a as a summary of the context bucket. And so subagent, which contains the entire bucket, it's like, hey. This action returned too much, I'm I'm here, you know, to stand in for what they actually returned. Roughly speaking, this is what it contains. Okay? And then the agent can enter into a conversation with the sub agent, which itself has has its own caching and can manipulate the context using Unix utilities. So right here, you're saving a lot. You're saving a lot of money, and it goes quite fast. Then we went one step further, and we were like, hey. What if we could have recursive context packets? What if context packets could contain other context packets? And what if compaction, because obviously we have compaction, was a will to buy such recursive context packets? And by that, I mean, like, now when we compact so the conversation goes, it passes threshold. I think right now it's like 200,000 tokens, but, like, we keep we keep tweaking it. And at some point, we're like, okay. We're gonna compact. We compact, and then the compaction sends sends all of that stuff into a context bucket. So now the agent can query that context bucket. So it's not like every compaction is always lousy by nature, and I think compaction operates under the faulty assumption that you never need access to ground truth, which is is false. You you you, at some point, do need access to ground truth. So with that technique, you have access to ground truth. And then we went step further, which is so you you have that context bucket, which is compact the the compaction of the of the previous conversation. Your conversation keeps going, keeps going, keeps going. You need to summarize again. What you do is that you take all of that, including this context packet right here, and you compact it into a new context packet. So now you have a context packet containing a context packet. And in the end so you do that, and so you have this emerging property where it's like you can have the agent access any point of any infinite number of tokens at arbitrary levels of of of granularity. Okay? The problem is that if you if you have a computer science background, like, gives you, like, what's called, like, ON complexity. Right? So it's like if it wants to access, like, nine context packets ago, it's gotta go through nine layers of sub agents, and that's really slow and really expensive. And so what what we ended up doing is maybe it's getting too technical, but we Never. Okay. Well, have you held up AVL trees?

[30:36] Nathan Labenz: No. But I'm all ears.

[30:38] Flo Crivello: Have you have you held up red black trees?

[30:42] Nathan Labenz: There is this I went to this I went to the computer science school of hard knocks, so it wasn't formal training for me.

[30:48] Flo Crivello: I was actually

[30:49] Nathan Labenz: glad

[30:50] Flo Crivello: to to have

[30:50] Nathan Labenz: AVL

[30:51] Flo Crivello: and black trees because I was like, oh my god. You guys remember your training. You know? This is this is the moment we use those specking things. Because everybody in school is always like, when do you really use those specking things? Like, we're using AVL and black trees, baby. Right? Right? Black trees, they're called. It's so what is described, if you just do the naive implementation, you get that really nice emerging property for free, which is you have all of those linked context buckets that contain one another. But every context packet always contains just one of the context packet. And so you end up with, like, this Russian doll of sorts of context packets. Like, if you if you wanna open if you wanna get to the bottom, takes a very long time if you wanna you know? So what you do instead is that you have context packets contain multiple context packets. Okay? And and and and and so you you end up with a tree because, basically, what you want is you want the topmost context packet not to contain the last 100. You want it to contain the first context packet and the last context packet. Right? And so you end up with it's a self balancing tree. Those these two algorithms. Those the AVL tree, and then there's the red black tree, which are algorithms that are used to balance a tree. So instead of having one long line, you want to minimize the height of the tree such that, like, going to the bottom takes as few jumps as possible. Okay? Technically, AVL is the best is is the is is the demonstrably best way to balance a tree because it it leads to, like, the lowest height. A red black tree is better because it takes into account the cost to balance the tree, which is it's expensive because you need to regenerate a lot of your context packets, and you you you you miss your cash when you do that. It's it's very expensive. So red black trees the canonical implementation of red black trees is on binary trees, which is just a tree where each node's got two children. We went for a it's called a centering tree. It's literally just each node's got a 100 nodes. So the the node below that's gonna have, like, 10,000 nodes. Right? And so literally with two jumps, you can have 10,000 context buckets, and you can access, like, all the context in the universe. And that leads to some really surprising behaviors because you're literally no more than two LLM calls away from being able to access 10,000 context buckets, each one of which contains 200,000 tokens. Right? So you add, like, 2,000,000,000 tokens here of context in in two LLM calls. And and that is what leads to, like, those really surprising behaviors from those AI agents where you ask them any question and they remember everything perfectly all the time, and that's awesome. That was that was one really big thing we had we had we had to figure out. I hope, please, you know, like, copy us. Like, I will just we just don't have the time to publish, but, you know, this is this is we don't mean for this to be secret. Like, I think this is a really powerful technique, and I I have been surprised to not see more communication about it. Just for calibration, how many tokens are you finding businesses have? Like, when you say with these two levels, can get to 2,000,000,000 tokens. I don't have a great intuition for Does that cover a 20 person team? Does however that's been in business for five years, what's kind of the heuristic for team size and length of history that translates into how many tokens? You can probably do the math, but, like, 2,000,000,000 is we should ask Cloud, but it's it's probably bigger than most libraries. So I think we we have few customers who whose knowledge base, whose memory is bigger than that. Most of the time when you get started, you consume, like for, like, a a team of 20, you know, the the hydration components. So it's like, again, it's this moment where, like, you connect your Notion, you connect your Slack, and then we crawl everything. We look at all of that stuff. For a team of 20, that's gonna tend to consume 5,000,000 tokens at most, you know, three to 5,000,000 tokens. And that's with, like, a team of 20 and, like, years of Slack history. So, like, we literally take all of your Slack history. That takes a while and actually here, surprisingly, the bottleneck is not even the LLMs. It's the Slack APIs. Like, it's not happy about you, like, crawling traffic. Experienced that. Yes. I'm sure. The right way to do it, if you're doing it personally, is to export your workspace archive. We can't ask users to do that, so we just crawl crawl the entire history. So no. I mean, like, 2,000,000,000 is is a lot of

[34:58] Nathan Labenz: Yep. One data point I have is a podcast, and I'm quite self indulgent and we sometimes go on for a long time. But still, it's usually only in the sort of thirty to fifty thousand tokens range. So then even having done hundreds over the course of a few years, we're still only in, like, single digit, maybe low double digit millions for, like, the entire full text history of the hundreds of podcast episodes. We're still we still got orders of magnitude to go before we would get into the billions.

[35:29] Flo Crivello: That said, now think of a company that's got, you know, three to seven hours of meetings per day per employee, you know, times 2,000 employees. That that is that meetings are when you really start to rack up tokens really, really quickly. How do you so I did experience the Slack thing. Actually, funny anecdote is the the only time I've really experienced data loss, it wasn't too painful, but Claude had, you know, identified some weakness or whatever in the and I'll bring this back around in a second too, but my kind of raw data is living in a SQL like database. And Claude recognized some deficiency or whatever that it wanted to correct, and it just was like, alright. I'll just drop that database, and we'll redo it. But what it wasn't taking into account was the fact that we'd been rate limited on Slack for days to get all the stuff out of Slack. And so it was like, no. You dropped that. And I that was the only place where we had Slack. It wasn't really lost, but it was like, okay. It's we're now gonna be, like, at it again for five more days just to make API calls to get out of Slack.

[36:36] Nathan Labenz: In in my case, I am the admin of at least the Slack accounts that matter to me the most, but there are, like, workspaces that I'm in where I'm not the admin, and so I can't grant this kind of access. From a business standpoint, does that mean that you have to have admin buy in from the beginning? Is there that seems like it makes it hard to do product led growth land and expand type stuff. That's that's a

[36:59] Flo Crivello: good point. You know, Luke, I think that's one reason why PLG, eventually, you tap out of it as you go up market. Like like, yes, you you have to be an admin. You have to have permissions to install applications on your Slack workspace. So it tends to be fine for teams of up to, like, 50 to a 100. Like, after a 100, it's pretty rare to to to to to for everyone to have this kind of access, but many teams up to a 100, like, pretty much anyone can just Slack, like, install, like, an app. And, yeah, I mean, if you if you go up pocket and and you're, like, part of a bigger organization, then we we have some users who are, like, just, like, championing us internally, and they're basically begging the IT to let us in. And then they're, like, making making these meetings happen. And and, you know, IT is is is rightfully cautious. But look. You know, we're, like, SOC two compliant. We're HIPAA compliant. We're GDPR compliant. We we know we've got our ducks in a row, and we're we're quite used to navigating the internal review processes, which can be burdensome. And and and so, you know, he's not in the business of being a pain in the butt of anyone, and and they are actually themselves very eager. Very often, IT was given an an an addict like, hey, guys. We need to we need to actually become an AI native company. So they're actually seeking this kind of solution. But, yeah, I mean, you need you need admin access for sure.

[38:09] Nathan Labenz: Okay. So let's go back to the data structure for a second. Let me give you my data structure, and you can tell me what you think I might be leaving on the table with my homespun version compared to your professional version. And then it's one of my favorite habits these days. I'll take the transcript and give it to to Claude, we'll see if we can close the gap a little bit. So my version is simply take all of the content that, you know, all the sort of digital exhaust that I've created over the years, which is email and DMs and Slack and Google Docs and whatever. Right? Put with Slack being the most painful one, make all the API calls to export all that stuff into a SQL database. I have everything there mapped onto a threads and messages structure. And sometimes that took a little bit of squinting, but it basically seems to mostly work. And that's like the raw ground truth. And I do agree. For me, it's I've found that it is quite important for the model to be able to get to raw ground truth. Then I've just done and this only goes back five years, but that's usually plenty. It's not too often that something happened more than five years ago. It was, like, operationally relevant today. I just do a month by month export. I found that for me, personally, it was, like, a couple 100,000 tokens per month that I'll then compress into a monthly summary, which is, like, 1010% at the length. Right? So whatever, two to 300,000 of raw monthly content goes to 20 to 30. Then I'll do that at the yearly level as well. So again, you've got another 2 to 300 that goes down to a 20 to 30. And then on top of all that stuff, then I just have the model go make a wiki. And one of the key things that I found along the way in terms of how to get to ground truth, of course, the model can always just send a SQL query, but what should it be querying is is not always obvious to it. So what I tried to do in my summaries is have the summarizer capture distinctive short sequences of words that would be like needle in a haystack. Fine. So it'd be like, here's what happened, blah blah blah. And then a quick little footnote at the end of what the source was, DM from whoever, and these four words will take you exact to that and probably only that in, like, all of my personal history. It's works pretty well for me. I'm usually pretty happy with it, and I do to the right ground truth more often than not. It is only one person. Multiplayer is gonna be as I start to onboard my wife, and then that'll complicate things at least a bit. What do you think I might be leaving on the table, especially as you think about multiplayer that demands an even more sophisticated approach?

[40:58] Flo Crivello: Yeah. So I believe the approach you're you're talking about, and we we still have some rag, you know, database, and we use that for a lot of rag as well. It's called, like, hypothesis driven retrieval. Don't know if you've if you've heard that. It's just all the reverse hypothesis driven retrieval. So those both those both. So what hypothesis driven retrieval does is it generates like, suppose it's asking, like, well, was Justin B. Bill born. Right? And, like, and then you you basically instead of searching for that question using Rag, you search for your hypothesis. Like, Justin Bibill was born in Paris. Justin Bibill was born in New York, in Berlin, and you search for this this this bucket of of hypothesis in your Rag database because, obviously, semantically, each of these hypothesis is a lot closer to the answer you might be looking for than to the question, where was Justin Bibel born? So that's the first thing. Then what you do is you also do the opposite, which is when you find an answer in your in your Rag base, generate a bunch of questions that map to that answer, and you attach them to the answer. So now you can the the retriever can both look for hypothesis and can still look for its answer for for the question, and that that that's gonna tend to increase retrieval quality. That that's totally fine. I think one thing you may be leaving on the table is it is really healthy for all of the memory to be managed by one agent. Even though that agent and and and both retrieval and updating of the memory. And so even though the memory is in the file system and so your agent could actually just go mess with the file system, what we have found actually and can still do that, but we we tell the agent we prompt the agent to be like, if you're looking for a memory that you don't have, please ask the memory agent. And the reason why is because when we ask memory agents, then what we do is that we log the query, we log the answer, and we log how many hops it took to retrieve that. And then when the memory agent is napping and dreaming, it looks at this log of, like, hey. This is this is the kind of stuff people have been it's almost like a librarian. You know? It's like, ah, like, I've been I've been hit up left and right. This is this is a question that I get quite often. So right here, by the way, it acts as a sort of cache, you know, because automatically so, you know, you you you always keep, like, the memory of the memory agent, like, the last, like, thousand, like, query and, like, question and answer pairs. You know? So it's like a rolling window, and so it's it is a sort of cache. But, also, the memory agent is not going to just rely on that cache because every cache at some point got a TTL. It it grows stale and all of that stuff. So what the memory agent will also do is, like, it will it will restructure its own memory to reduce the number of hops that is needed to retrieve, like, the most frequently asked questions. And that that that tends to work quite well to to to improve the the memory retrieval and the memory speed. Cool.

[43:36] Nathan Labenz: Interesting.

[43:37] Flo Crivello: Another system we we experience we experimented with and we ended up not implementing it even though it does have strengths from a technical standpoint is have you heard of cavemen?

[43:47] Nathan Labenz: I'll say no.

[43:48] Flo Crivello: It's it's it's really simple. It's basically you rewrite your text as a caveman. It's like, and, you know, you know, Nissan not like Burrito. You know? Like, if you do that it's so retarded. But if you do that, you catch your tokens by, like, 20 or 30%, which is good. You know? And so cutting your tokens, like, it'll be it'll make your system faster and make your system cheaper and make it'll make retrieval more more accurate. It'll just be better with basically no loss of of information or context. You know? If you the the reason we've not done that is because we actually do care about the maintain ability, like, the the auditability of the system. And so we did that. Every metric went up. It was awesome. And then the files in the file system look really dumb. And so it doesn't make us look good, you know, to enterprise customers. Like, why do my memory files look like that? But it it works. Actually, similar. You know, those like, there's, like, a bunch of those techniques to just compress of context. Have you heard of a Toon, t o o n? It's it's it's a sort of, like, JSON alternative that's made for agents. That's, like, I think it's, like, 20 or 30% more token efficient than JSON. JSON is actually not that token efficient. So Tune Tune works better. It's 20 or 30. So this work this is less important for, like, memory agents, but it's just I recommend everyone, like, makes their agent use Tune instead of JSON and have, like, a a a Tune passing middleware between your agent and its actions. Like, no action should expose Tune to to to the agent. Like, every every agent every action should expose Yeah. Interesting. I have to check out this Tune format. I've just used YAML, which also is motivated by

[45:20] Nathan Labenz: similar

[45:20] Flo Crivello: Yes. Advantages. But, yeah,

[45:23] Nathan Labenz: Toon token oriented object notation. And token, Yeah. I I see where we're going.

[45:28] Flo Crivello: Ish, and it actually increases the performance of the agents. Like agents apparently see it was just surprising because there was so much JSON in the training set of the models, but they're they're operating more comfortable with tunes than with JSON. JSON does have a lot of noise associated with it for sure. Yeah. I'm more comfortable with YAML than I am with JSON. That's so that's something. The AIs, they're just like us. That's listen. One of my biggest surprises as a brief aside, I'm actually working on new I don't if this will be a podcast or a thread or whatever, but just taking stock and, you know, of course, agents can help make this a quick project where it would be a very tedious project otherwise. Just going back in history and identifying things that I've been wrong about prediction wise and some things I've been right about prediction wise. I mean, one of the things that I used to say quite confidently that has aged maybe the poorest is that we shouldn't be anthropomorphizing models or that will lead us astray. And I've just been shocked over and over again by how productive it can actually be to anthropomorphize the model. It still feels a little dangerous kind of, but it's hard to argue with the results. I 100% agree. I do think so that sounds like a a happy medium of of superposition, and I think, like, that's that's a little thing that's very top of mind for me right now is to what extent does superposition stop being true in particular open the curtain to AI employees? So for example, the way I've changed my mind and I haven't changed my mind completely, but I've changed my mind a little bit, relatively speaking, is about multi agent systems. I actually have come to to believe that most of the time, as much as possible, you want to consider that as much as possible and do a single agent, not multi agents. That's not always possible, and there's there's still good reasons to use multi agents. Like, this is not, an absolute statement, but most of the time, that's the case. And I actually think that humans, like, intuitively find themselves biased towards very, very multi agent system, like far more multi agents than is optimal because the the entrepreneur facing a little bit too much, and they're comparing a little bit too much to human organizations. Say, I have a data scientist. I have an engineer. I have a designer. I have a PM, and so I'm gonna create one agent for each of these things. And, actually, the reason why human organizations do that is because every human has only twenty four hours a day and can only contain that much context, plus agents obviously have none of those constraints. They can fork. They can duplicates. They can they can do as much as they want all day. And so I find that division of labor is not a good reason to have multiple agents, which is which is a major major fact. Now you, again, there are still multiple there's still different reasons, but the division of labor is not one of them. So I agree. I think by and large, I think people are right to transform morphized models because they will, after all, trained on human tokens and always, you know, human RL and all of that stuff. But I also think that AI organizations shouldn't be overly anthropomorphized.

[48:16] Nathan Labenz: Yeah. That's interesting. Is it as you talk about a single agent, obviously, they can run multiple copies of we can run multiple copies of it of single agents in parallel. Have you reached the point yet where that starts to create database style problems where if multiple agents are updating the same segment of history at the same time, we have all these sort of, you know Yeah. Transaction level guarantees in traditional databases for that reason. Does that start to be an issue as you have 10 windy teammates running over the same kind of file system corpus? Yes.

[48:59] Flo Crivello: Less than you would expect. You know? Like, I think it's the way human organizations have solved that is via Git. Git is the, by far, the best way, like, large groups of humans have found to, like, work together on the same on the same thing. You know? Because you can you can just merge conflicts. You can rebase. You can do a bunch of that stuff. So that's that's what we found, and the file system in Linde is actually for this reason backed by by a a Git repository, which gives you a ton of amazing properties, so including, like, merge management because the the memory agent sometimes will fork itself. It's it just basically spins up a bunch of sub agents. Like, alright. Let's see what happens since the last time I napped. And in some organizations, some large organizations, there's so much context that was created since the last step that it it needs to create a bunch of sub agents. And during the the initial hydration, like, really wanted to go fast because we wanted to impress the user. Like, time to wow is is so important in PLG. And so that graph I showed you, like, literally, we we we optimize it so hard, so it happens within ten seconds of sign up. So it's like, sign up, Notion, Slack.

[49:56] Nathan Labenz: Wow. Boo.

[49:57] Flo Crivello: Right? And so we there's been a lot of work. And so one way you do that is via is via, like, a a collection of agents. And so, yes, these collections of agents tend to step on each of those toes. The answer to that is Git, which, by the way, also gives you history for free, which is which is awesome. By the way, like and this is not a sponsored message. Like, they've just been doing a really good job. We've been working with Mesa, m e s a. They sell, like, an agent native file system that that's backed by Git. So you create all of those repositories. Initially, we started by the way, it's it's one of those intuitions you have to build as you build these systems. Like, it's very easy. It's very tempting to be like, I'll just head roll it. I'll just create my own Git. I'll just, like, manage my own file system infrastructure. And you you you learned that there is so much more depth behind those systems than you first appreciate. And so I now I am so it's actually funny because it it sort of goes counter to the the prevailing narrative these days, which is like, SaaS is dying. You can vibe code everything. And while I'm typing a lot of stuff, but infrastructure will not vibe coding. And I've I've become so infra build. Now I'm like, anytime I have an opportunity to buy instead of build, I will do it because, like, you save so much time and there is so much more depth and thinking and design decisions that goes into these products, then you may appreciate it.

[51:09] Nathan Labenz: I love a good vendor shout out. Any other vendor shout outs? Any other key primitives that you're buying that you would recommend?

[51:17] Flo Crivello: Well, you need a you need a sandbox, so we've we've gone with e two b for that. Again, like many people when once they have a sandbox, like, sweet. They have a file system. We, at least at scale, we strongly recommend decoupling them for for the reasons I just mentioned and and more. Browser base, obviously, is is is excellent for for browser management.

[51:34] Nathan Labenz: Who else

[51:35] Flo Crivello: do we like and use? You know, I've we we went down a deep rabbit hole. Like, one major exception to what I said about being infra build has been observation and and evaluations. We we have a complicated history because we we got started we started this this company in 2022. And so it was it was, in hindsight, way too early. Like, agents were not ready. No one was talking about agents back then. People were talking about, like, January TVI, if you remember. It was like, that was pretty

[52:02] Nathan Labenz: like, baby AGI and and all those kind of early experiments. You know?

[52:07] Flo Crivello: So agents didn't really work. You know? And and so as a result of that, there was no tooling. And so as a result, unfortunately, we had to build a lot of our own tooling, including our own eval platform, including our own observability platform. And I don't recommend doing it at home, honestly. It's like just to use and so we've gone through a whole evaluation. We've you looked into LengthHughes to Lengthsmiths, into Brain Trust, into Agnast, in which I'm I'm a proud investor. We've looked into a bunch of those solutions, and every time we were like, I think we just had such a head start. And at this point, our homegrown solution is so built exactly. We've gone through the pain of building. You know? So if a solution had been available back then, I would have picked it, but it wasn't, so we built it. We flushed it out over the over the years. Now we have, like, one engineer who's full time dedicated to that solution, which is a lot in in the vibe coding era. This guy is, like, cranking, and he's it's become, like, a very, very, very extensive solution. And truth to be told, we are at feature parity with all of these solutions out there and more. We have a lot of internal features that I don't see these guys have, and it's so built for our own internal scaffold and our own internal views that we ended up just hand rolling that internally. Yeah. So what does life look at look like at Linde internally these days? I think it it strikes me that you're now I'm sure there's been, like, a dogfooding process. I know there's been a a dogfooding process throughout the history of the product. But now you're at this point where it's like, there's no limits to the dogfooding. Right? You can do anything. Yeah. How does that play out in terms of I don't know. You could cut it any number of ways. Right? Are there roles that you might otherwise hire for that you just wouldn't hire for now? Are there, what's the ratio of your own inference spend for the purposes of building Linde compared to your payroll? You put your own lenses on it, but how has the arrival at the human or at the AI employee stage of this process changed what it's like to be a part of the company? That's a great question. And I think right now I mean, obviously, a GI is here. And so, obviously, right now, every company is sort of scrambling, and and there is that time of transformation that's accelerating. And I will say and I'm I'm I'm I'm a little bit of a dober. I'm I'm worried about AI risk. But so far, so good. And so far, it's so much fun. It's it's honestly so much fun. Like, to go through AGI and to build this in AGI is like, we can move so much fucking faster. I feel like we're moving at the speed of that now. It's like there is no obstacle. Like, normally, is like, you think about the strategy. It takes a long time to figure it out, to implement it, to see the results. It takes, like, months. Like, that OODA loop is, like, months long. You know? And, obviously, the the name of the game at a startup is to move as fast as humanly possible because that's the only advantage against incumbents. And now we can just have ideas and see them live in the product two hours later. And, like, big ideas too, like, not small ideas. And I can tell you so our Slack now is basically just us and Lindy. It's like Lindy is, like, just half the messages on Slack is just Lindy and us talking back and forth with Lindy. Very often, like, we we ourselves, and we breathe this stuff all day. And, like, we ourselves, like, we we underestimating the platform. Like, just last week, we we had, like, a problem with, like, our CI pipeline. We have literally many hundreds of runners on GitHub who, like, manage our CI pipeline. By the way, there's another thing right now to answer your question. What's it like to work at Linde?

[55:42] Nathan Labenz: Where's what's it like to work

[55:43] Flo Crivello: anywhere, frankly? It's like every part of the stack and every part of your process is stretched to its very limits right now. That's just write down tech. You know? Like, I'm sure you've seen the graph. Like, GitHub posted a graph of, like, number of commits posted per day. GitHub was, like, hardly, like, a subscale company. It was really big. It's it's just going vertical right now. It's just going to the moon. You know? And that's that's, like, I think, indicative of of what every company is going through. Our number of PRs internally has, like, PRs per week has tripled over the last three months, and, like, the number of lines per PR has tripled over the last three months. We hardly review PRs anymore. It's just agents reviewing PRs. We insofar as we review basically, what the the way the team is talking about this, like, we'll no longer PR review wheels. We'll, like, PR review wheel review wheels. You know, we'll we'll just review in the machine that reviews the PLs. As a result of that, they're like, this is awesome. But, hey. Now CI is in is in the middle of that. So what do you do? Well, now you gotta work on CI. And so we've we've spent a long time working on CI. Like, for the nontechnical agents out there, like, CI continuous integrations, like, the process that's involved in taking into account your code changes and making sure they're safe and actually melting them into your main product and deploying them. And so we we at first, we started throwing money at the problem like many people do. And so now our CI is extremely expensive. It's like it's a it's a it's just an insulting expense. And and and we've exhausted all the all the obviously, like, we're obviously not running on GitHub runners. We're running on, like, GCP runners, and, like, we've we've done, like, caching of the builds. We've done all the obvious things. It's it's a lot of work to manage CI to make it to make it efficient. And and and so we I was tired of this because it was, like, weeks of messing with CI and and, like, building a hole in our bucket. And we're like, what are we gonna do about the CI thing? And then I was like, wait a minute. Like, why are we doing all of this ourselves? So, like, can't we just, like, spin up an agent to do it? And spin that. The agent was literally and so by the way, that's the other reason why it's important to have a a single agent because setup now is becoming one of the biggest costs because the biggest cost, the biggest bottleneck in any organization right now is human time and human attention. So you wanna remove that guy as much as possible. So you wanna be able to provision agents and give them all the accesses they need without a human in the loop as much as possible. Okay? And so is this agent at the company. It is this AI employee that does everything there is to know by the company Lindy teammate. Right? It's got access to our repository. It's got access to it's got it on the computer. So we're like, Lindy, can you please, like like, mess with our CI? And it's like, yeah. Let me look at, like, the runners using the GCP CLI. Let me look at, like, the GitHub, like, action and all of that stuff. So it's doing, like, a lot of analysis. It's getting back to us, and now it's literally it's basically like an auto tune, like, an auto optimization thing. Well, like, Lindy is just, like, sending us a graph, like, every day, by the way, using ImageGen to generate. So the graph is, like, really pretty. And it's like, hey. You know, guys, like, look. Like, CI cost is going down. Time time to merge is going down, which is a good thing. And I was like, I can't believe it took us two weeks of just, like, messing with CI ourselves instead of just saying this to Lindy. You know? So I think this is what it's looking like right now. It's like engineers are are, like, less and less working on the thing. They are more and more working on the thing that is working on the thing. And more and more, what we do is, like, we we're setting up this machine, which is the company, which which just increasingly operates itself and and increasingly will more and more like you know, I like to think of it as, like, first, you become a manager of ACs, then you become, like, a director of managerials because at some point, like, the managers are gonna become gonna become AIs as well. I'm hopeful that at some point, we become board members. We can just go to a beach in Hawaii, and we and we we don't have much to do except, like, review the strategy of the company. So we'll have, like, reports of of the agents telling us this is what's going on. This is the landscape. You know? This is all the results we're having, and we'll be able to have much deeper conversations about the nature of the company we're building and the the the spot we occupy is the market. I that's a dream as far as I'm concerned, and that's that's not very far away at all. To answer your question, have we have there been positions we've not hired? Like, yes. Absolutely. You know, we we the team is actually I think headcount has been flat now for a while, and we've again, like I said, we've actually tripled our productivity over a couple of months. I find there is a bold deluxe zone because humans are still in the loop. Humans are still needed. But, obviously, the more humans you add, the more coordination costs you you introduce inside inside the company. And so I I find that actually right now, teams, relatively speaking, are at an advantage versus bigger teams in in a lot of ways. Do you

[1:00:18] Nathan Labenz: I'm sure you do. I don't know if you wanna share it, but do you have a ratio of not I don't mean all inference spend, like, including your customers' use cases, but, like, your own internal inference versus your payroll. How is that ratio spreading?

[1:00:34] Flo Crivello: Yeah. So if you look at all the inference spend, including all customers, it's it's many times payroll. It's and it's been for a very long time, but one exception because that's what we sell. You know? Now if you look at payroll versus internal inference spend, it's in shooting range. So payroll is still greater, but the the lines are gonna cross three to six months from now.

[1:00:52] Nathan Labenz: If you imagine a hypothetical scenario where all of a sudden, it's just you at the company, and after that, it's Lindy's all the way down.

[1:01:03] Flo Crivello: What breaks? What doesn't work anymore? Like, other words, what is it that the humans are still irreplaceable for? It's really hard for me to answer this question because and I think about that a lot because I am finding myself increasingly lacking the vocabulary to answer this question of, like, where do agents fail? You know? Like, we used to talk about, like, time. You know? It's like, ah, time before it loses coherence. It can go, like, the meter thing. It can go, like, one hour, ten hours, one hundred hours. And and I'm sure people will resonate with this as well. Like, I I don't find that's that's a good that's a good measure anymore. You know, the the world that everyone is using these days is, like, spiky. Right? Like, these models are so very spiky. As we're going through AGI, the world AGI has stopped meeting anything because it's actually ASI in many ways, and it's it's it's subhuman in in some very surprising and dumb ways. It's really dumb in a lot of ways. I'm like, hey, man. Like, you you can produce for me, like, 50,000 lines of code one shot. You can build me the most incredible ideas in, like, three hours by yourself. And then, you know, you you you're telling me to, like, walk to the car wash. You know? And so that's where it fails. Imagine you've got this this this machine that runs a company, super fucking smart, but but but 50 times a day, it decides to walk to the car wash. You know? And so I think right now, like, the the challenge of these new organizations that are going to be for the foreseeable future human AI hybrids is going to be you're basically building an Ironman suit. Okay? And so there is this slider where it's like, okay. This is for the machine. This is for the human. Okay? And knowing where to put this slider, because it's not a one dimensional thing, it's like it's a complex line through a many dimensional space. So you you are looking for that slice that you can draw through the space because you wanna make the most out of humans' attention. And so you don't want the AI to bug you for stuff it doesn't need you for, and you need the AI to know when it needs to bug you. And almost definitionally, it can't because if it knew, it wouldn't it wouldn't bug you. It it wouldn't it wouldn't make the mistake. And so I'm sorry. I feel like I'm answering discussion with the question, but that's that is the right question. You know? And and I think right now, that's one of the open questions in the industry is, like, how do you draw that line? How do you get the the agent to realize when it's in so on and when to let the scrub? Like, when he's even saying something, like, please walk your car to the car wash because it's it's sunny today. You know? I I think that that that's the answer. It's like it's just I'm I'm somehow humans and I, we're gonna have to this whole system and I guess times when it's being very dumb.

[1:03:34] Nathan Labenz: How about the times where something is being very smart? Where do the best ideas come from these days? I think there's long been this idea that the AIs are good at routine stuff. I used to say, and clearly this is no longer applicable, but I used to say, you can probably automate any routine work that you have at your company if you're willing to put in the elbow grease, but don't expect eureka moments. Now, obviously, we're getting eureka moments on the timeline all the time, at least in certain domains like the open math conjectures and stuff. When it comes to the best ideas at Linde, how many of them are human origin, and how many of them are AI

[1:04:15] Flo Crivello: origin today? I hate the censile, but it's it's both. It's it truly comes from this from the union of both. And the reason I hate it is because it led us into this myth of the the centaur. You know? It's like this mythical man horse creature. And so, you know, it's this idea that, like, yes, an AI is better than the human, but you know what's even better than AI? It's AI plus human. Hence, humans are always going to be needed, and that's a fallacy. That's just not true. And the the literature is actually clear that it's like, you know, we've seen it happen with chess and with every other game which which AI has achieved superhuman performance on where at first AI beats human and AI plus human beats just AI. And little by little, the gap of AI plus human versus AI is shrinking until it actually turns negative and humans are introducing, at best, like random noise into the system. So humans actually, at some point, become, like, truly clueless, and and they start harming the system in which they are intervening. That said, the center phase exists at for a while. You know? So the the open question is, like, how long is it going to exist? But right now, we are in the center phase. And I think that's the other reason why multiplayer is so important because you want AI to be you want your agents to be in the room with you, You don't want any random agent to be in the room with you. You want an actual AI employee who's been in the room with you for a long time, who sat in every meeting, who's built up that internal knowledge base about everything that's going on in the company, and you can at mention it and you can invite it to chime in into your conversation in the company. And we do that all the time. We do it actually, it's because it's very easy to do it on Slack. We'll just like, add Lindy. What do you think? You know? It's hard to do in the meeting in a way that, like, now makes us actually more and more. Well, like, we prefer Slack because, like, it's the AI is there. I was like, in the meeting, it's there. It's listening, but it's called Chime in. And so very often now, what we do during meetings, we're like, absolutely here. Like, you know, struggling with this team. Like, Lindy, can you please send us a message after this telling us what you think? And they'll send us a message on Slack. And and so when you do that, it is not the case it is rarely the case that Lindy will first shot, tell you something, and you're like, this is it. This is the thing we should do. But it is almost always the case that we we we go back and forth with Lindy. And and and and from that back and forth, like, misses something. Like, the CI example is is a great one, actually. At first, when we when we invited Lindy into this conversation, it happened over a second. I was like, wait a minute. We're done. Like, why are we not doing this with Lindy? Add Lindy. Can you please optimize OCI? And and

[1:06:44] Nathan Labenz: it Make

[1:06:45] Flo Crivello: no mistake. Yeah. Yes. And I forgot it's first suggestion, but it was it was it was a reasonable suggestion if you did not understand OCI, and it was it was it was was not good, like, given given the constraints we're operating under. And so, like, the engineers chimed in. They're like, no. Actually, we can't do that. We started by that. They're like, ah, that's a fair point. So we went back and forth. And then and then together, we came up with, like, oh, yeah. Yeah. We should totally do that. Like, just go ahead and do it, please.

[1:07:10] Nathan Labenz: A thesis that I've had for a while, and it may be too early to say, but you're definitely gonna have a perspective on it, is but, again, going back to what I've been wrong about, I expected a lot more transformation than we've seen in the economy. If you had showed me Fable in 2022 and said, what's the unemployment rate when this model is out? I would have said, I don't know, but something definitely higher than what we are currently experiencing. Yeah. So then why and this has been something I've been wrestling with for the intervening time. Like, why are we not seeing more? And one answer I've come to is people are stuck in their ways and not everybody's enthusiast like I am and it's gonna take time. But then also, when we get to the drop in knowledge worker, that'll be the time when it'll suddenly flip. Right? Because now I'll have a more parody kind of choice between I could try to go hire a human or I could plug in an AI, and that's gonna be easier in a lot of ways. Right? I don't have to go through a whole interview process. I kinda know what I'm getting blah blah blah blah. I don't need to tell you why an AI employee is a good form factor. Do you think we are starting to see this flip, and is it going to be something that incumbents will be able to adopt in time to avoid getting disrupted, or are there still gonna be bottlenecks or resistance points that I'm not anticipating? I expect more disruption as well. I think as long as the models are spiky, we're all going to need a lot of humans in the room

[1:08:43] Flo Crivello: because the holes in the model are very dangerous. Not not just like AI, existential dangerous. They'll just they'll just harmful. They're really harmful to the system. And so however expensive humans are, they're going to earn their keep by plugging these holes. Now regarding whether incumbents will be able to adopt this this this this this thing, I think as long as it is spiky, I think the the incumbents are going to be at a disadvantage. If you had a true drop in remote worker with no holes, then I think even the incumbents would be able to adopt it quite quickly because it's just like hiring an employee, and they have processes for that. And I actually think they would be south on on on on a lot of the of the innovation because it it would be skeuomorphic effectively. You would you would just have, like, AGIs, like, sitting in human seats effectively, and I don't think that's the best way you build an AGI organization, but it would work. The the smaller organizations are going to be at an advantage both because they are going to be able to to short term figure out this AI native organization while the human is filling the holes. And and long term, I think they are going to be to build, like, a less skeuomorphic AI organization. You know, economists are very fond of talking about that. I actually wrote a a blog post about this that's called the tough tomato principle on my blog in in 2018 or so. And it's it's about how, like, every time you have a technological revolution, you think of the new paradigm in the in the terms of the old paradigm. You know? And and and one tale that you're you're you're falling into that trap, which is very natural, is that you're you're you're calling the new paradigm after the old one. So, you know, the the first cars were called horse less carriages. Right now, self driving cars are called self driving cars. Right now, eventually, we're gonna have a a will for them. You know? That's not that's not in the terms of the old paradigm. It's almost like right now we're doing it with AI employee. Like, it's it's like AI employees is is as strong as as as a term. It's a very strong sign that you're doing something very wrong in how you're thinking about your product. And I think I think it's it's fine if you're doing it just to communicate about your product because, like, the nature of positioning is that you have to position against something that already exists in the market's mind. And so the the car exists, you know, the the the the the carriage exists. Sorry. The carriage exists. The the car exists and not the employee exists. So you can't really invent a new, like, wheel out of nowhere. The iPhone did that. You know? It's like the iPhone's obviously not a phone. It's it's so much more than that. Like, you know, what percentage of of your iPhones use is just like making phone calls, like 2%. You know? It's called an iPhone because it's it's an electronic thing you've got in your pocket. Right? So I think AI employees is one of those things. It it takes a very long time for industries to find the the message which is native to the new medium. I think it was Steve Jobs who was saying, like, you know, the first the first content on TV was just easier, like, recorded radio talk shows. They took, a radio show and they put the camera, and now you've got the TV content. That is what talk shows are. Or recorded theater plays. And it took a surprisingly long time, like ten ten or twenty years before people realized that, wait. I can move the camera? Wait. I can have multiple cameras that I can cut? You know? I can do I can I can change the scene? I can do I can do a lot of crazy stuff now. And and and and and it's treacherous because it's thankless work. Once you've realized it, you you it's so obvious. And this is why when you when you watch, like, old movies like Citizen Gain, you know, or well, Hitchcock has a lot of those. Like, you you you you you're like, done nothing special. Why was everyone crazy about Citizen Kane? It's like, well, actually, it was groundbreaking at the time. Like, the Beatles are like that. You know? It's like, well, you know, it was it was actually really innovative at the time. So I think, well, right now, also in that phase of, like, the AI play, we've got the AI employee, so now one's the phase of trying to figure out what does the AI organization look like. And I think that's that's a young man's game, and that's a young company's game. I think I think incumbents historically have have really struggled to answer this question.

[1:12:44] Nathan Labenz: Do you have any previews that you feel confident in, or is it too early to say? Or maybe you would say, are there any thinkers that you think are kind of notably ahead of the curve on this?

[1:12:54] Flo Crivello: The only post I have seen and liked about this comes was from Duarkesh. It was it was a while ago. Was a year ago. For me too. Yeah. Yeah. He asked, like, what does the identity proposition look like? And he he points out like, I think it's it's an excellent intuition pump. He points out he's like, look. You know, Sundar at Google is paid a $150,000,000 a year or something like that. You know? So, obviously, there are agents which companies are happy to pay a $150,000,000 a year even though the token throughput is actually low. Like, it's not like Sundar. It's just like emitting 2,000,000,000 tokens per second or whatever. Right? It's just like he has really good tokens. Right? And so now so what does that mean? Like, that that you get something about the willingness to pay for models and for agents. There is this book by Robin Hanson. I'm I'm looking at it right now, the age of m, And it also explores a lot of those themes. Right? It talks about have you read it? It it's excellent. Highly recommend it.

[1:13:44] Nathan Labenz: Yeah. Yeah. I mean, it was laughing only because I tried to do a podcast with him about that book, and it went in a very different direction. So it just brought that memory to mind. But I think that book is excellent. I think it is a as a primer for or as an example of how to take a premise and really run it out, it's elite.

[1:14:03] Flo Crivello: Yeah. It's a book long style experiment. So for people who don't know what it's what it is, like m stands for Emulated Mind. And it's like, hey. We all going to have the hardware to create Emulated Minds, you know, in the, like, the late twenty twenties. So, like, we're right on track. It's like, we're just not gonna know how to do the software, so all we're going to do is we're going to emulate, like, literal human brains. Not exactly what's happening, but kind of what's happening. You know? Like, LLMs are trained on human tokens, and so that's basically what's happening. And so it's like and it goes on all of those riffs of, hey. This is all the crazy stuff you can do when when this happens. I'll I'll talk about two of them because I don't wanna take the whole time, and and everyone should really read the book. But just to give a taste to people about, like, what what it sounds like to to have those native AI organizations and the kind of stuff that you can do that you can't do when you have a human. The first one is splitting. And so I think this is why this this touches on on what I was saying earlier. Like, don't create multi agent organizations. As much as possible, try to have as much of your internal work as possible. Emphasis on internal. We can get back to that later. But as much of your internal work as possible done by one agent and one only. Why internal only? Because if that one agent which has got the keys of the castle and access to the bank account and the repositories and the secret keys is also the same agent that does customer support, you've you you potentially have a security issue. So it's it it can be it can be good to have multi agent systems speed for, like, only for, like, security reasons. But okay. So you have that single agent. Now that single agent, you you know, it it sure. It it goes faster. It emits more tokens per second than a human, and it doesn't take a break. But at at some point, you're gonna need more tokens than, like, a single LLM loop can do. So what do you do? Well, you have multiple LLM loops running, and it's basically just like that agent duplicating itself, like forking itself, right, and creating sub agents and, like, workflows, dynamic workflows, and all of that. Right? So and that's one thing that Robin Hanson talks about. Like, you know, you can imagine you ask one literally, in the book, he says you could ask one emulated mind to build, like, a really complicated thing, like an operating system, which is, like, billions of lines of code or something. And and at first, it would just, like, take it to the highest level and, like, come up with, like, the highest level architecture of its operating system, And then it would fork itself, and each subcopy would be in charge of, like, one building block of this highest level thing. And then each of them would recursively keep splitting themselves such that it's a it's a fractal. It's like every every every node in the graph has got, like, the whole picture in their head, and every node in the graph is working on an implementation. And then they all bubble back up, and you could imagine multiple passes up and down. And in three out wheels, you've got an operating system. It's only, like, such a wacky idea when he wrote the book ten plus years ago. Now I think it's it's very clearly what is currently happening. Right? So that's that's one thing that's happening, which is which is really interesting. Another thing that I've not seen that yet happened with with LLMs, but it's an interesting thought experiment about what does cross organization collaboration look like. And what you can do is that you can create unfalsifiable agreement. So suppose I came to you and I was like, Nathan, I have information for you that they cannot tell you. But I can tell you that if I could tell it to you, you would agree and you would give me all your money. Right? Yes. I you wouldn't give me all your money right now. It's it's it's a bit too easy. You know? But if I could prove that to you, if I could prove you with with absolute certainty that, yes, if you heard that information, you would give me all your money right now. You know? It's just I can't give it to you. The way you do that with ms is that you you clone yourself. You clone me. We put both of us in a in a box that's going to self destruct, and they talk. And your and your m has a button, and that's the only the only communication means it has with the outside to be like, yes. Give him all your money. Right? So it's a copy of you. So it's it is you agreeing, and so you have the zero knowledge proof of of of of its crypto bros. Well well, like, right, I guess, on this one. You have this z k proof of of of of that. So that's the kind of thing that's on my mind, and I think that's the kind of thing we're going to see exist soon in the next few years with AI native organizations.

[1:17:59] Nathan Labenz: It's about to get weird in a whole bunch of ways, I think. And you better hope they don't break out of that box too. That's the other worry one might have as we put our agents into boxes these days. One let's do one more beat on the stuff that's relatively mundane, then we can zoom out to some some real big picture considerations. But you did have, as you alluded to earlier, this highly viral post, I don't know, two months ago maybe, moving a significant part of the workload to open source models for cost saving reasons and for you don't need God to schedule your meetings reasons. But where are we now? It strikes me that, like, when you talk about just having one agent, that kind of seems to go against that. There's also the can advantages which you lose when you are crossing agent providers too much. Then, of course, there's also proprietary providers have their families of agents, and they're, like, increasingly training their best models to delegate to their haikus, respectively. Like, where do we shake out on this now? Is there still a a significant role for open source cheaper models to play in the Lindy teammate, or are we back to heavily cloud based agents today?

[1:19:22] Flo Crivello: No. We're quite open source build. I like, look. It's just so much cheaper. Second, it's it's ridiculously cheap. Like and, like, the the cheapest of of them that we really like is deep DeepSeek Flash, which is it's free. That fucking thing is free. You know? And so as you as you experiment with your agent and it's just speed up a lot of them, like, the the bill can really go up pretty quickly, and DeepSeek Flash is just incredible, you know, and quite fast as well. You know, we we do find that as you go to, like, the upper echelons of intelligence, if you look at, like, a q m case three or, like, a GLM 5.2, like, I still am super impressed by those models, and they're awesome. And we we are increasingly considering them as, like, our main driver for for a lot of parts of Lindy. But I will say that the gap narrows between those guys and, like, the frontier guys. Like, kimikis three is is frankly not that much cheaper than, like, a Sunet or, like, an Opus. So but, no, otherwise, I mean, like, I I, yeah, I I do think open source models are, like, if you kill the price, and if you don't mind using this product, I'll say, yes, you do have stuff to figure out around caching. Like, yes, the the the inference for ideas are, like, not as good at caching, but they're they're catching up. And and and and look you know, if you look at, like, a deep sea deep sea flash, it's Sonnet 4.6 level ish, a bit less, but kind of ish. Sonnet 4.6 is a really good level, a really good model for, like, most use cases, and it's literally a 100 x cheaper. You know? So even if you even if you miss on two x because of caching, you're still 50 x cheaper. It's a really big difference. The difference between spending spending, like, a thousand dollars or $50,000. So, Noah, I think open source models are, like, a required part of the stack right now for for anyone who's, like, seriously building and operating AI agents.

[1:21:00] Nathan Labenz: So how do you think about

[1:21:01] Flo Crivello: deciding

[1:21:02] Nathan Labenz: where to integrate them, especially because it didn't sound easy before, but in a context where you have your workflows and there's nodes in the sort of graph of work, you could go into a particular node and say, okay. We kinda know what the inputs here are and the outputs, it's, like, relatively controlled environment and so we can do structured testing. I did all that and you still reported having some false positives over time where a model could pass a bunch of tests and you would feel good about it and then if you'd start to test it live with users, you'd get the feedback that like, hey. Like, it got dumb and I'm not you know? Somehow it was, like, still hard to measure even with, like, a much more structured environment for the AI to work in. Now as you're in this very open ended, just tag Lindy in Slack and send it anything, it seems like that problem would have increased in difficulty dramatically. So how are you approaching it, and what are we learning in terms of where the open source models and it's not even so much I think Anthropic does make some good points about this sometimes. Obviously, there's more to the story, but I do think they they're apt in some ways where they say, in some cases, it's less about open and closed source and more about just like what's capability level, what's the price, what are the the features. So regardless, I guess, of whether you're even going to open source or just going to Haiku, how do you decide when you can do it?

[1:22:31] Flo Crivello: Great question. We have found you should almost never use multiple models powering the same agent, and that we have found one exception to this rule is when you spin up new blank sub agents. Emphasis on blank because there's two types of sub agents. You have a blank sub agent, then you have a full sub agent, which inherits the context window of the of the parent agent. And the reason why you don't wanna do that is because of caching. We we we just obsess about caching, obviously, because it's just so expensive otherwise. Like, the economics do not they barely work with caching. They just cannot work without caching. So caching is is a must have. And and so I'll give you an example, the validator system that I mentioned. So it's this LLMS judge. By the way, highly recommend anyone who builds AI agents. Like, this is one of the lowest hanging fruits you can do to greatly increase the reliability of your of your AI agent. And so that's step one. It's just, like, have a validator, which is, like, the naive implementation is, like, you ask agent to do something. It does it. Or, like, it it it submits an action candidate. You intercepts the action, then you ask it, are you sure? You know? Literally, if it's just, you sure? Already, you get a bump on your evals, which is insane. It should not be the case, but it is the case. Now if you increase if if you change this to, like, an actual prompt, which in our case now is, like, 10,000 tokens, it's a really big validator prompt. If you change that, then you you actually you're you're giving it a checklist, and that's got returning tokens. And and now it it it's way better. Like, you can get to perform above office level in our in our experience. Now then you can get can go one step further. You can you can have, like, a a federated suite of of validators, and some of these validators may be deterministic. So for example, one thing we and and the rest of the industry has found is that Sunet I mean, cloud models this year have been getting dates wrong by one day. Right? That's one way in which they'll spike it. Right? It's like, hey. You've got your AI organization. Did it gets dates wrong. It's a problem when, like us, you're building an AI executive assistant, which half its job is to schedule meetings. Okay? You can't get dates wrong. So what we did is that we we created it's it's a modular architecture we have. It's really simple. It's just like a bunch of videos, and then it's like a promise.org with a timeout for for people who know what this means. And and so everybody that there has, like, a second to, like, decide what to do. And and and and and if they've not submitted their verdict, then time's out, and the agent just submits the action. And and and we have a validator, which job it is to detect dates that were submitted in the action and to and and we we've prompted the agent to to include a weekday in the date. So it never says never says July 28. It says Tuesday, July 28. Okay? If you do that, then you can have a deterministic value detail that checks whether the date of the week matches the the day the day that was submitted, and that's just a regular expression. Instant, pre, no AI in the loop, and that's that's one of those federated validator. But even even the validators that are AI powered, you don't want initially, what we did is we were like, what if we had Sunnet for the main agent and we had, like, DeepSeek Flash for the the sub agent, the validator? But, actually, because when you when you have a cache hit, it's 10x cheaper. You you you you actually unless the model you're going to use is more than 10x cheaper, which it may in the case of DeepSeek flash, it may not because the caching is inferior. You know? If that's the case, you actually do wanna keep the same model. And so that is so much smarter. Like, it it's actually worth it to to just, like, keep the same model. That that also holds if you fork your your agent. Like, that way, can you can recycle the cache. By the way, interesting note. How do you reuse the cache when you have this validator? You know? Because the problem is that changing the toolset invalidates the cache. Okay? So the way we've done it is that the validator as as the agent has the validator action always included in its toolset, and it's unable to invoke it. We tell it, don't don't invoke this guy unless you're the validator. And if he tries, we just we just like, no. I'm not gonna I'm not gonna listen to you. I'm not the validator. And then the validator now inherits all the actions of of the agent, And it's like, you are now the validator. You may invoke this action, which you have known about the whole time. So that's how you don't break the cache with for this kind of of pattern. Yeah. And so forked forked agent's the same. You just use the same model. You know, I I've heard friends, funders who've told me I don't even wanna check if it's true. I'm sure it is, which disgusts me. If you if you take an agent and you change the model at every turn between roughly equivalent models so one turn is SunNet, one turn is Grok point five or whatever the latest is, and one tail on its, like, GPT 5.6, and one tail, it actually increases the performance. Is that ensemble? And it's literally just like a random sequence. Okay? That ensemble, for some freaking reason, outperforms any given model. I don't wanna know why, but, apparently, it works. Again, we've not even tried it because we don't wanna break the cash anyway.

[1:27:11] Nathan Labenz: Okay. That's really interesting detail. Can we bottom line it a little more? It sounds like top level, Claude is still the kind of core main driver. Because of the caching, Claude is also often the validator. Blank sub agents, can put onto a deep sea flash. This sounds pretty claud heavy, all things considered. Is that fair?

[1:27:40] Flo Crivello: No. Oh, sorry. Was not realizing you will start asking me, like, what model we're currently running on. Right now, we're running on DeepSeek. DeepSeek is the driver.

[1:27:48] Nathan Labenz: So the it's the driver. Wow.

[1:27:49] Flo Crivello: Okay. It's it's it's the whole thing. Everything is DeepSeek right now. Yeah. We are in the middle of reconsidering it because we are realizing that teammate, the new product, is is is is requires beefier computer. And by the way, can always go in your in your Linji settings. Like, we are model agnostics. You can always select Sonata or others if you want to. By the way, like, one interesting learning of mine is, like, doesn't matter how often you tell people, hey. Like, we promise on the benchmarks it's the same. They're all, like, quite a few of our customers, and therefore, like, I don't care. I want Sonat, you know, or Opus, you know, which is very excessive. But, no, right now, it's deep secret default.

[1:28:23] Nathan Labenz: Interesting. So you feel like the so yeah. I guess, how would you characterize the gap between American API models, Quad perhaps specifically, and a deep seek. We hear we're I think we kinda go through these cycles, right, where it's like, oh, the gap is closed. Oh, maybe it's opened again. It's oh, actually, it was totally on trend the whole time. It was always nine months. Qualitatively, for the purposes of actually making an AI employee work, how would you describe the gap?

[1:28:55] Flo Crivello: It's more spiky. It takes more turns very often to find something that works. So it still ends up finding something that works, but it ends up taking more turns, which is slower and more expensive. I know you asked me for something qualitative, but look. It's it is, like, three or six months behind. So right now, we've got Sonnet five. Like, DeepSeek is basically Sonnet 4.6 is the way I like to think about it. How much do you worry about what you when people are allowed to change the model, I always have this question of, first of all, are we in a period of convergence of the AIs or divergence? I'm even on that basic question, I'm, like, sometimes confused.

[1:29:33] Nathan Labenz: Mhmm.

[1:29:33] Flo Crivello: And a sort of

[1:29:35] Nathan Labenz: distinct but conceptually related question is, like, to what degree do products have to be co designed or co evolve with models to really work well with them? You could imagine that switching to Sonnet might make it worse because of all the work you've done. And in fact, I've heard that about Opus five. And, yeah, I've heard a lot of different things about Opus five, which I don't think coherent anything really easily summarizable yet. But I have heard from some quarters that, hey. You probably need to rethink a lot of your system prompts and whatever with Opus five or yours it may not follow your skills very well. It can be more powerful, but you've gotta kinda go back and redo things. So, yeah, how do you find that playing out in practice? We we do

[1:30:18] Flo Crivello: find

[1:30:18] Nathan Labenz: significant

[1:30:19] Flo Crivello: differences, surprisingly significant differences between the model families. I was expecting I think I was you know, you and I were talking together years ago, and, you know, at are going to come to the same region of the space. And that's not happened as fast as I was hoping, I'm hoping for it because, obviously, that makes the models commodities as far as I'm concerned. Makes my life easier. What is actually happening is that the different model families have significant gaps in how they interpret your prompt. And so by family, I mean, like, Cloud and and OpenAI and and even Meta. Now it's sold back in the game and Grok. Like, these guys these guys are quite different in the way they they interpret the prompt. And then indeed, whenever you have a new major jump, five was a really big jump. It was, you know, four point seven and four point eight together, like, there was a change, I think, in the tokenizer. So it was very different compared to 4.65 is bigger than that gap. And so what we've what we've found and and we've been lucky enough. This is part of the tooling that I was mentioning mentioning earlier that we've been investing in is, like, we have our own self optimization loop. So we have hundreds and hundreds of evals. I think at this point, more than a thousand. And and we have an optimization loop where it's like an agent that runs the evals and finds like, tweaks the prompt to maximize the school on the the evals. And, by the way, you can, put it. Like, everything you can, put it now. And and, yeah, I mean, every time a new model comes out, we we have to run that loop. And you give it a budget, and at this point, we have to give it, like, thousands of dollars because it's it's a lot of dev ops to run. So it's, like, about, like, $10,000 every time, like, a new major model comes out. So it's like, hey. Here's $10. Just reoptimize your prompt around this new model. And we do find that there are a lot of updates. Like, the prompt does change quite a bit every time. I will say, the open source models are all very cloud like in their behavior. I wonder why.

[1:32:05] Nathan Labenz: Yeah. I have some questions for you on that. When you do this optimization, are you is this also like a homegrown framework or are you using like a DSpy or the I forget the name of the kind of spiritual successor to DSpy?

[1:32:18] Flo Crivello: No. No. No. It's all homegrown. Yeah.

[1:32:22] Nathan Labenz: But it is a sort

[1:32:22] Flo Crivello: of

[1:32:24] Nathan Labenz: auto optimization process where you're like, here's an eval set. That's right. Auto optimize your own prompt to climb this set of hills that we've got for you.

[1:32:34] Flo Crivello: That's correct. That's correct. Yeah. The gPA is like generative Pareto frontier or something like that.

[1:32:39] Nathan Labenz: It's Yeah.

[1:32:40] Flo Crivello: That's the one. It it looks it looks for, like, the Pareto frontier. So it just it's it's looking for, like, the best prompt across all of your eval set. We're looking into tweaking the system right now so that we can assign weights to evals. Well, like, this eval counts as, like, 10 of this other one because it's really important. But right now, it's just, like, treating every eval as equal.

[1:32:58] Nathan Labenz: How Fine tuning as a dimension in this whole model situation. This is another thing if I go back in time, I'm like, I definitely expected a lot more fine tuning than we are getting. OpenAI's retired its or on the verge of they've certainly announced, and I think maybe at this point have pulled the trigger on retiring their fine tuning product. We've got thinking machines trying to answer that call with their own model that's specifically to be fine tuned. Yeah. Is that gonna be part of the future of Lindy teammate? Yes.

[1:33:30] Flo Crivello: I think fine tuning is the last result. It's something you do once you've every other option because it's such a pain in the ass and it's expensive. But it's it's gotten a lot easier because now we have AGI, and so you can just ask Cloud to fine tune for you. Just give it a just give it a dataset. Bringing the dataset together is still, like, complicated, and, like, sanitizing is complicated, and it's it's still it's still the pain. So it's it's it's a less result. It's it's not the low hanging fruit. You should do everything else before you fine tune, including debark. But then, yeah, at some point, get there, and this is why, like, a lot of companies are the first ones to fine tune because they they have existed the low hanging fruit. And at some point and and they have the resources to fine tune. And so it it it does make sense. It does give you more performance for cheaper. Do you think that that

[1:34:11] Nathan Labenz: like, how broad do you think people should be thinking when they are approaching fine tuning? Obviously, the extreme would be one fine tune per task. You can definitely do multitask fine tunes. It's maybe getting into pretty challenging territory to say we wanna make our own general purpose model that's has the same breadth of action space as the the big ones, but it's like ours somehow. How would you guide people on, like, how big to think when they start to approach fine tuning?

[1:34:46] Flo Crivello: Most people should not fine tune. I think if you work at, like, at scale, you should you should fine tune. Then the more at scale you are, the more fancy you can be. But I generally one metaheuristic I've developed over the years is, like, you should place an enormous emphasis on simplicity. Enormous emphasis. And and I think it's just too complicated to start to have multiple fine tunes, so different multiple use cases and users and and model routers like you don't want. No. No. No. Just one model. You know, you don't like I said, you don't switch model mid midstream. And if you fine tune, you just fine tune into one model. Sure. Okay. Cool. Think I think one again, this goes to this metaheuristic I mentioned of extreme simplicity. But the thing I find myself going back to all all the time is, like, because I spent so much time thinking about context and memory, Right now, our memory is, like, these millions of tokens stored on on a file system and this memory agent that's really, really sophisticated. It does feel like memory belongs to the weights. This does feel like a little bit of a hack. And so there is something to be said about that. I think, eventually, with infinite resources, what I would like to do is I would like to do a LoRa per user because LoRa is actually pretty cheap to train. So now it's no longer on that being now you use it to be dreaming because you can't retrain the LoRa every fifteen minutes. You would have to do it every day or every week. You know? And and you would and and it's tremendous infrastructure challenge even to, like, so you gotta stool all of those LoRa's, which, like, the storage is not a problem. But, like, the inference time swapping in and out of the LoRa's is a huge pain in the ass. And the training pipeline and and all of that stuff said, good luck doing that. So but it would make sense because I think weights are are so much more compressive than than tokens.

[1:36:22] Nathan Labenz: Yeah. That's I don't know if you have any thoughts on the great horizon scanning for continual learning, but this is, again, for quite some time, has been the thing that I'm like, boy, if that ever tips with and it could be as simple as one key insight, we could be in a very different world very quickly.

[1:36:43] Flo Crivello: I have a feeling what what I'm describing is is is is a step towards machine learning, but it's not it's not the final step. I think the the final step I think I think, like, the the bitter lesson will have you, like, inference and training need to be one and the same. Like, we can't separate them. You know? That's that's sort of what I think the entire field is looking for right now. I agree. Once we get there, it's going to be crazy and scary and very, very, very different. But, yeah, I think I think we all bring the LoRa thing I just mentioned, like, look. It's it's an engineering problem. You know? You can figure it out, especially now that we have a GI. So it's yeah. I think I think I think the the LoRa thing is going to happen soon in the next six months. I think even, like, one of their Frontier labs is may do it. I thought that was honestly one of the things that OpenAI did extremely well with their fine tuning product. They were clearly doing something like this because they would allow you to do the fine tune, then you'd have the same rate limits with your fine tuned model as you had with the base models. And I was always really impressed by that engineering accomplishment. And now it does surprise me that they've gone away from it so much, but I also agree with your guidance. I would definitely tell people don't rush into fine tuning these days. It's a slow cycle, and that you could probably get what you need for less total effort available models that you don't have to monkey around with in that way.

[1:38:03] Nathan Labenz: 100%. So you talked about scary stuff, which is maybe a good transition to kind of not second half because we've been at it for a while, but a second phase of this conversation around just zooming out and looking at the super big AI picture. Maybe I'll let you choose the order of topics when it comes to Chinese models and what if anything should be done about them because you did recently post something, I think, quite counterintuitive given the fact that you're running your company on deep seek. I'll let you state your position there. But Yeah. Should that come before or after the big picture, where are we in this kind of, oh my god, we just had open face happen. Thanks. What does it mean, and what should be done about it? Yeah. I'm trying to coin that. We'll see

[1:38:48] Flo Crivello: if it sticks.

[1:38:49] Nathan Labenz: I searched for it the other day because I just got back from China myself actually, and I was like, how has nobody called this open face yet? I'll be the first one to drive.

[1:38:56] Flo Crivello: How about open gate? Because that's

[1:38:59] Nathan Labenz: what it is. It's an open gate. I'm sticking with open face just out of pure, you know, shock value, I guess, if nothing else. But, yeah, we can fight it out of the marketplace of ideas. I guess you tell me what we should talk about first, and I guess it depends a little bit on, like, whether the Chinese model argument is, like, upstream or downstream of your kind of big picture concerns.

[1:39:22] Flo Crivello: The open face incident is is immensely concerning. I think it's a it's a most concerning incident that's happened so far. And I I know that my my feeling is shared in the labs. Like, my friends at the labs or some of them are panicking. Like, there is an air of panic right now, like intense fear in in the air. So that's that. Then regarding so that's that's where we are, you know, right now. It's like July 2026. We have AGI. We are in takeoff, and we've not figured that alignment. That's the. You know, the the current timeline looks much too close to, like, an ELIZER Yutkovsky's essay for comfort. Now regarding the Chinese models, in brand of news, Chinese models. Look. I I I okay. I'll start by saying that I have been demoning the low quality of the discourse here. It's it's been it's been very disappointing because I I thought tech was different. You know? Like, the quality of the discourse was something for work. Right? The cultural work. I'm like, alright. Whatever. You know? But then it's this is tech. This is new stuff, and we can't get all shit together and and just remain polite and civil in the marketplace of ideas and then dress each of those ideas at the object level. Like, meaning, like, don't attack each of those intentions. Can you please just attack address the argument and just put false? Can you please not pretend that it's something I didn't say? It's it's ridiculous. That's what they could just put out and they say, which has bolded, we do not support the ban on open source. And then people are like, oh, I can't believe it. Like, quote tweaking this thing they've obviously not

[1:40:55] Nathan Labenz: read,

[1:40:57] Flo Crivello: and they're saying, oh, they're supporting a ban on open source. So I'll just start by saying that. So can you please can please everyone take a chill pill. Stop calling everyone the retards. I recently put out a blog post, so I was like like, hey. We I I think Chinese will also be banned. And I I was called to retard 50 times by supposedly smart and accomplished people, including, like, famous VCs. Now a lot of other, like, famous accomplished smart people reached out in DMs and by message, and there was a lot of support. My position is basically Anthropics. I hate saying that because people are saying, oh, you're just an Anthropic shield. But look. I have time stamps. Like, I've been tweeting my my entire positions who had the whole thing before Anthropic clarifies theirs, and my position is exactly the same. I don't have anything against open source. I may, but I'm undecided. I do think open source might increase existential risk. Let's put that aside. I'm undecided. This is not the crux of my position right now. God bless open source. Important for innovation, important for companies including mine. Like, please, open source. Okay? I have something against Chinese frontier models, whether they're open or closed source. And here's the the the reasons. Or number one, they're obviously distilling. It's very clear. And so you're putting American open source model companies in an unfair competition and and closed source in an unfair competition because they're not allowed to distill. You know? It's just it's just contrary at the very least contrary to the terms of service, and it may be legal if you've if you've put in place technical measures to circumvent any protection that the model company put in place to prevent distilling, which which the Chinese models obviously have done. So it's it's distilled and it's unfair. Right? Now some people say, yes. But the companies have also distilled on human data. That is not what distillation means. That is just not what it means. Like, if you if you do the training on human data, it's costing you billions of dollars, like, actually billions of dollars. If you do it on on AI data, it's costing you hundreds of millions at best. So it's it's a huge unfair advantage, and it's a significant enough portion of your costs just to to to fulfill you an unfair advantage. That's number one. It's unfair.

[1:42:56] Nathan Labenz: Number two,

[1:42:58] Flo Crivello: very pragmatically, we don't want Chinese models operating in The US. Today, you know, it breaks my heart because I'm an American citizen. I'm a proud American citizen. I'm a hawk. When I talk to my own product and I'm asking it what happened in Tiananmen, it tells me, I'm sorry. I can't talk about that. That's a problem. You know? Like, these models are eventually subject at the end of the day, they are subject to CCP censorship and CCP policies. You don't want those models. This basically amounts to being the greatest instrument of foreign propaganda on American soil ever. And there is just ask your LLM of choice. Ask Claude. Hey. Tell me the precedent we have of banning American, like, foreign influences in the country. Like, these three radio acts of the twentieth century, like the TikTok thing that just happened last year. Like, we we do that all the time. You know? Then these models are not merely just an instrument of propaganda. They're agentic. They're actually doing stuff in the economy. You don't want the CCP to run chunks of the American economy. Duh. Then finally, even if none of that was the case, maybe those models are playing fair and square. Maybe they're not representing foreign interest. Maybe they're just better. And so here, a lot of people are are, I think, indulging. And I say I say that as a libertarian, but they're indulging in what I call I call naive liberalism. Right? It's like, well, let's beat them in the marketplace then. And I'm I'm like, you know, like, I actually and, again, I said that as a libertarian. Like, I I protectionism is not always a bad thing. I think you do want to protect your your your domestic AI champions. Anthropic can't say that, and they claim, and I believe them, that this is not their intention when they do that. And I will 100% believe that because they've been so consistent for literally the founders. I've been worried about x risk for ten year. Right? So they've been so consistent. But look. I can say that because another topic. Hey. We want local AI champions. Duh. It's a battle of national security. We don't wanna hollow out a base with hollow data or industrial base. You know? So it's not about the principles of the Chinese models, and it's about propaganda. It's about nothing English speaking for our economy. It's about fairness. It's a must it's about protecting. Alright. Let me try to give

[1:45:10] Nathan Labenz: you

[1:45:11] Flo Crivello: some

[1:45:11] Nathan Labenz: counterarguments that you can respond to them.

[1:45:14] Flo Crivello: I

[1:45:16] Nathan Labenz: am a little more sympathetic, I think, off the top to the argument that there was some massive reappropriation of human knowledge that is upstream of all AI. And you could say that's not distillation. Sure. You could say they the American companies spent a lot more than the Chinese companies are having to spend. I think that's also undeniably true. But if I'm just kinda what feels just and fair? I'm especially because we're blocking them from using Claude. Right? We're not it's not like we're saying, hey. You can buy all the Claude you want. Right? So we've said we've got clad. The the the makers of clad have advocated strongly for chip restrictions, and they also try to refuse to sell their model into China in the first place. And the whole premise of Claude or the whole existence of Claude is based on the idea that we hovered up hovered up all this human knowledge in every form we could find it from digital, and you gotta believe that includes the whole Chinese digitized heritage. Now we're, like, sucking in the books, which I don't care about the fact that some books get destroyed in this process, but this wasn't something that everybody's consented to. And so it does feel a little strange to me given all of those fact patterns that we would then draw the line and say, okay. Like, it's anthropics consent. That's the consent that really matters. Now I think they should be permitted to do business with who they wanna do business with. And if they wanna put in measures to try to prevent distillation, I think that's their prerogative. But I'm, like, not at all convinced that it should be the state's job to come in and engage in sort of statecraft to try to punish or prevent this distillation. To me, it's kinda I don't know. Information wants to be free. Knowledge tends to diffuse. It's not like Anthropic has a super or any AI company has, like, a super moral high ground in terms of I didn't get my check for my share of the training data, and they're getting paid for the distillation queries too, which whenever they don't wanna be paid, so it's probably beside the point. But I don't know. I'm not I don't know. I don't find my I find myself a bit on the side of the underdog Chinese here where it's like, man, you've got kind of a deck stand stacked against you and get some knowledge where you can get it. What's wrong with that perspective?

[1:47:32] Flo Crivello: I think we do want the the deck stacked against China. We don't want China to win the race to ASI. I I'm not a lawyer, so I won't opine on the exact legal path that you may take to to make your terms of service in full. So I I'll just observe that, and I say that as someone who used to work at Uber. It's extremely hard to bring justice through the judiciary against Chinese companies. Almost impossible. So not a lawyer. I don't understand the technicalities here, but I'll just observe and start with that. And it may take a very long time, and by then, a lot of damage is done. And then I'll also observe, like, yes, companies are training on all of these corpus that is available to everyone. That is an an even playing field. The problem is that once you've done that at at great expense, it does cost them billions of dollars. If you can if you can and you and you create that artifact out of this training dataset, which is called the model. Now other people can turn around, and and instead of doing this, which cost billions of dollars, they turn to this, which cost a lot less than that, and they copy you and they catch up with you. If you do that, you kill innovation. And that's that's it's nothing new. It's just called IP and patent law. It's exactly what humans do. Right? It's like you as a human. You're like a researcher. You you think for, like, decades. You do all of that work because you're lot of money in r and d. You come up with an idea which is much smaller in tokens and much more precious and much cheaper to steal as is than, like, all the corpus of stuff that you trained on, how do you protect that idea in order to recoup your investment, in order to produce it? It's called a patent. It's called IP. Right? It's nothing new. If you apply to the same standard you're applying now to, for example, the pharma industry, you will have very cheap drugs, which is awesome, and you would have no new drug ever. You would completely destroy innovation in the pharma industry. And I think this is why you're finding all of these people support open models because people like free stuff. And it's it's you can't really measure the the future innovation that you don't get as a result of that. All you see is you get free models. You know? And look. I'm one of them. You know? So I'm I'm I'm speaking against my interests here. Right? Like, my company is dependent economically on those, like, very, very, very cheap models. But I'm also in in a coordination problem right now because, like, I cannot not adopt these models while the models are out there because my competitors are going to do it. So I have to adopt them if they're out there. I wish they will forbidden across the board so that we would like like the four reasons I invoked earlier. Like, protect our our like, champions, not have the CCP, like, influence the country or run parts of the country and and have, a fair level field of of competition.

[1:50:02] Nathan Labenz: I guess another simple argument is that I think our frontier companies are doing just fine. That, you know, that could change perhaps at some point in time, and I you know, as the facts change, I I think our response to it, you know, might also ought to change. But I don't find it super compelling at the moment to say, you know, we need to protect, like, Anthropics revenue run rate. Like, they're you know? Right. They can probably serve the models.

[1:50:27] Flo Crivello: The load. No. That's fair. They That's fair.

[1:50:29] Nathan Labenz: Yeah.

[1:50:31] Flo Crivello: But you're still left with the. Like, you you you you need them like, the the way money works is is it's a mean of of allocating resources. Again, like, at the end of the day, the mobile context is with the the underlying dataset. Like, the frontier model context is with that data. And and the way you finance this this channel here between this underlying dataset, including, like, mostly RL these days and the model, is you need a lot of money. Right? If you get another guy who copies this guy and, like, turns off this dataset here, like, basically, like so this guy now has, like, fewer resources. Like, you kill that channel. You you are actually slowing down innovation. So and and, actually, a lot of doomers that I know are for these reasons supporting open source because it is actually slowing down AI innovation. So if you if you want AI to keep innovating and models to keep improve to keep improving, you are actually anti open source to some extent. And and particularly sorry. Anti anti the Chinese mode of open source, which is which is relying on unfair practices.

[1:51:25] Nathan Labenz: Another moment of not exactly the highest quality AI discourse recently was when Dean Ball, a friend of the show, said that open a that open source models were decelerating and obviously got dragged for that. But I do think that's apt. I do feel like this is a moment where, you know, here we are, two lifelong techno optimist libertarians, and we're grappling with the fact that this one might be different. Right? And we have to be willing to bend some of our principles in light of our our techno optimist libertarian paradigm wasn't quite drafted with AGI or recursive self improvement or ASI or whatever in mind. So I'm a little bit like I don't know. Sometimes I might have to be a little more flexible on my normal or fairness or respect for rule of law commitments. If it's if it's one of the hallmarks of the AI here is strange bedfellows. If I want things to go a little more slowly, maybe taking a little wind out of the sails of the frontier companies is a bullet I should bite.

[1:52:26] Flo Crivello: Yeah. I I I agree that I think AI is so unprecedented in so many ways that it does cause everyone. I I think you should ignore largely, like, your like, the world owes you no duty to be simple and to slap a simple libertarian or leftist label on everything. You know? And I think, like, especially as paradigms change, like like, is these, like, simple mental models, like, map is not the territory. The simple models we we we we applied as the territory changes very rapidly, the map breaks. And I think we're one of these well, in one of these times right now, well, the map is breaking in in a lot of ways and a lot of assumptions that used to be true and on top of which we built our mental models and maps are no longer true. Yeah. So, I mean, again, I I'll just I am in filled and support of a sweeping ban of Chinese models on US Saudi. Seems I and I have yet to hear compelling

[1:53:16] Nathan Labenz: counterargument

[1:53:17] Flo Crivello: right now. So you're you're saying, like, you you you you do bring forth, like, a really compelling counterargument to one of my four points, which is so protectionist point, which I agree is is the weakest one. Like, hey. They're doing fine. You know? And I think there's there's there's very reasonable pushbacks against protectionism. They they they do hurt the the the consumer. So fine. You still don't want the CCP to have its dirty fingers in the country. Right?

[1:53:40] Nathan Labenz: Can we unpack the threat model there, though? Because I'm a little bit like, okay. These are I don't know where you're running your ring full of inference, but, like, most American companies that are running Chinese models are not, like, calling the DeepSeek API. Right? They're using some Yep. American inference provider. Yep. So they can't, like, rug pull the model itself. They could have, like, sleeper agents in there. I think we're getting decent enough at through kind of JSPACE and various interpretability techniques that that's I wouldn't call that by any means a solved problem, but, like, from what I've seen in anthropic research, when they do the kind of one team with a sparse autoencoder versus one team without, like, these techniques are really allowing them to find these internal sleeper agent style problems in models with greater and greater efficiency and reliability. So I'm, like, optimistic that even if they were to train in some sort of 2027, you become an evil AI, that we'd be able to sniff that out and keep that to a manageable risk level. And then I also wonder about kind of a market mechanism, like, maybe insurance should instead of a ban, like, what about an insurance requirement? I think that would be maybe healthy for AI across the board, and then we could start till some of these risks. If Linde is powered entirely by Claude, maybe you get a cheaper rate on your insurance. If it's powered by DeepSeek and we there's some unknowns, like, maybe you have a higher rate on your insurance. Maybe that kind of levels the total cost out for you in a risk adjusted way. But it still, like, lets people take advantage of these global public goods that China is providing, which the rest of the world is not about to ban, obviously. Right? We would be doing this entirely to ourselves without any expectation that anybody else will follow suit. And we had a hard enough time getting people to, like, sign on to our Huawei ban. I think this like, the idea that people are gonna turn off deep sea entirely in Brazil or whatever, that's like a total nonstarter, I have to imagine. So what about, yeah, audits and internals and insurance? Like, can't we layer on a few things like that and get to a decent place?

[1:55:47] Flo Crivello: I would be down. I I I would be down. The the problem, though, is is it is a public good. And so who? Who's gonna do it? By the way, I I think, like, yes, we are making progress in mechanistic inter interoperability, but it is not it it is not yet a solved problem. Like, we don't know what lies in those models. And so, yes, there could be just like a backdoor of, like, you say, the magic wheel to the model, and all of a sudden, it does whatever you want. And only the CCP has this magic wheel. Even if it doesn't have that, you know, it's gonna have biases that are going to reflect CCP priorities. And, again, like, the the thing is just the most obvious example, but there there may be a lot more. Right? And we don't again, we don't know. And, yes, you could imagine retraining those models, but it's who's going to do it. And and why would they do it if there was no market demand for it? Right? It's not like people really care that much about the short term because it's it's a national interest thing. You know? Like, me as a as a private company, I'm like, do I really care? Like, all my users really asking about Canon Mill that often? Like, as as a business owner, I'm like, it's not directly aligned with my with my interest. But as a citizen, I'm immensely concerned. And so, yeah, I would be I would be in favor of a of a type of regulation that says, like, non Chinese models, like sorry. Non fine tuned and, like, sanitized change models are not welcome in The US. And then we would need I I am in favor of, like, an FAA for for AI. I I do think we need a new agency to regulate those models, and I think it would probably be the one that would be in charge of saying, okay. This model is for sure. We we fine tuned it enough, and and it's now representative of American interest. And probably we will have a sort suite of evals and whatnot to to verify that that's the case. I'd be I'd be I'd be I'd be open to that for sure.

[1:57:24] Nathan Labenz: I think the strongest argument against this sort of ratcheting up of tensions is simply we might need to do a coordinated controlled call it a slowdown, don't call it a slowdown, but some sort of deliberate pacing of AI improvements. And we're gonna want China in on that deal. And I'm far from a China expert, but a couple of things I do feel pretty confident on coming coming back from two weeks, you know, knowing it all are like, one, if the state there agrees, I believe they can enforce on their companies whatever they agree to. So that's like I think they have that actually in much greater strength than we do. And two is if shit's going really crazy, it's gonna be in their own just sane self interest to do some deals with us because indeed we are ahead and they're I think there's like many deals that they would rationally take. And if it's in their rational self interest, then we can hopefully get mostly around a lot of the trust and defection problems. But I think we make it a lot harder for ourselves to get to those deals when we have all these aggressive postures toward them, of which banning their models would honestly affect them less than like our other one. They would affect them a lot less. What do they care if we ban their models, right, compared to refusing to sell them chips and refusing to sell them Claude? But it's just another log on the fire of we don't trust you. We can't deal with you. We're we assume you're a bad actor. And it seems like it makes it hard to get to the Yeah. Highest stakes agreements that we might really need.

[1:59:03] Flo Crivello: Well, I I I think, like, foreign relations and and diplomacy are also very pragmatic. Some countries with very vicious, like, disagreements are all managing to reach to reach agreements. Like, I think I think we can we can probably figure something out. And by the way, I think that ship has sailed anyway. And I think, like, we have ample evidence of, for example, Chinese spies attempting bio attacks just to, like, test the on American territory. And so and I'm I'm I don't know what we're doing over there, but I would not be surprised if I I don't think we're, like, free of of anything of it either. Right? So, you know, I I think at the end of the day, regardless of of what we do and, like, regulate the models on that or not, I I think they're gonna it will be the rational self interest to to make a deal.

[1:59:44] Nathan Labenz: I hope we're rational. I hope we're all rational enough to take these rational self interest moves despite recent insult. I do worry when I see the picture of Sam and Dario not holding hands that if there's a if there's a picture on our tombstone, I think that might be the one. And I do think that's a very very real risk factor at the international level as well that the Chinese do care about being insulted. They do care about these issues of face and whatnot.

[2:00:14] Flo Crivello: I Yeah.

[2:00:15] Nathan Labenz: Personally would take some risk to try to kinda the relationship up and hopefully create space for I think I think you're right. I think it makes sense to throw back at me. You said they'll take it if it's in their rational self interest, they'll still do it even if we do this or that. But I do wonder if there's pride governed limits to what people will do in rational self interest, and I would just hate for that to be the way that we fail to get to something that could be a huge difference maker in the grand scheme of things. I think you might be prepared to bite a bullet on your libertarian principles when it comes to the tremendous price discrimination that we see between API prices and first party Quadmax or GPT Pro subscription prices. I'm not a lawyer, but, yeah, I I again, I I do think that I would not be surprised if there will lies like that in effect that that that technically forbid companies to subsidize as aggressively as they are doing. It does put them it does make it very hard for, like, an application layer to emerge. Yeah. It's hard it's hard to compete against tokens that are as heavily subsidized as as what's the functional labs are doing. That's that's just the reality of the application layer right now. Anything else you wanna say or touch on before we break for today? This has been great.

[2:01:31] Flo Crivello: Well, I'd be I'd be remiss if I didn't mention you know, obviously, we're all releasing Linditimate. So lindit.ai. I I I think be ready to see more, not just from us, but I do believe the next six months are going to be about multiplayer AI and about this, like, Ironman suit. Like, about this human AI hybrid and these products that create this human AI hybrid organization. Yeah.

[2:01:55] Nathan Labenz: Fellow. Thank you for being part of the Cognitive Revolution.

Outro

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