AIE Europe 振り返りとエージェントラボの主張：教師なし学習と潜在空間のクロスオーバー特別編（2026年）

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.

Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what’s real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.

Thanks to Jacob and the UL production team for hosting and editing this!

Jacob Effron

LinkedIn: https://www.linkedin.com/in/jacobeffron/

X: https://x.com/jacobeffron

Full Episode on Their YouTube

We discuss:

swyx’s view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI

Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility

The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era

The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings

Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important

Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences

What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world

Why memory and personalization may become the next big wedge: today’s models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems

The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run

Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less

Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far

What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding

Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile

Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop

Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability

Consumer AI vs. coding AI: why ChatGPT’s consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum

The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx’s thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else

Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab

AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems

Why traditional SaaS may be under real pressure: swyx’s own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements

Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic’s Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI

The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer

Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems

What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation

“Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles

Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist

Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization

The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding

Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today’s LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligence

Timestamps

00:00:00 Intro preview: AI coding wars, startup pressure, and market structure

00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover

00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra

00:04:33 Why AI infra is harder than apps, and where startups can still win

00:06:39 Should companies train their own models?

00:09:28 Open models, custom chips, and the new inference race

00:11:25 Designing products for agents, not just humans

00:16:49 The state of the AI coding wars in 2026

00:19:27 Capability exploration, token-maxing, and why coding is going parabolic

00:21:41 What the end state of the coding market could look like

00:23:50 Where app companies still have room against the labs

00:27:02 Why AI valuations and market swings feel unprecedented

00:28:56 Consumer AI vs. coding AI, and why sticky products still matter

00:32:28 What the next breakthrough product experience might be

00:32:53 2026 thesis: coding agents break containment and eat the world

00:35:27 Are foundation models wiping out startup categories?

00:37:33 AI vs. SaaS, vibe coding, and internal team tensions

00:40:01 Biosafety, security, and the politics of restricted model releases

00:42:19 Giant models, compute constraints, and the limits of scale

00:44:30 Memory as the real bottleneck in AI

00:44:57 Why swyx changed his mind on open models

00:47:44 Dark factories and the future of zero-human-review coding

00:49:36 Why post-training and RL may matter more than people think

00:51:50 Memory, world models, and the next frontier of intelligence

00:53:54 The Good Will Hunting analogy for LLMs

00:54:21 Outro

Transcript

[00:00:00] swyx: Isn’t that crazy? That number is just mind boggling.

[00:00:03] Jacob Effron: What is the state of the AI coding wars today?

[00:00:05] swyx: We’re in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.

[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?

[00:00:21] swyx: Mid-size startups. Yes.

[00:00:23] Jacob Effron: What do you think the end state of this market is

[00:00:25] swyx: for the market structure to, to significantly change? There would be

[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what’s really become an annual tradition, a crossover episode with our friends at Latent space.

Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what’s happening in the infra world, the coding wars, and a bunch of other things. It’s a ton of fun to do this with someone I really respect and another great podcaster in the game.

Without further ado, here’s our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.

[00:01:02] swyx: Yeah,

[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.

Awesome talks and, and I think just have a great pulse on the zeitgeist of what’s going on.

[00:01:16] swyx: Yeah.

[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?

[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we’re doing roughly one per quarter now, which Yeah, you’ve

[00:01:27] Jacob Effron: really up

[00:01:27] swyx: the, hopefully

[00:01:28] Jacob Effron: up the, up the pace.

[00:01:29] swyx: It’s trying. We’re trying to match AI speed, you

know?

[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,

[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.

I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there’s a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let’s call it.

Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.

[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.

And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving

[00:02:36] swyx: for Harrison for sure. Right? Like he’s basically had to reinvent the company every year since he started Lang Chain.

Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he’s like one of the most nimble, adept sharp people about this. Yeah. Yeah.

[00:02:49] Jacob Effron: Saying now, now is finally the time stability

[00:02:51] swyx: this. Yeah.

[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?

[00:02:56] swyx: I think that. It, it’s very expensive to say this Time is different sometimes, but when you’re just writing code, like it’s actually okay to just like try to make a call and I think it may not even matter if this call is right or not.

Like I just don’t even care that much because you can be right on a thesis, but if you don’t, you don’t figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.

And it feels like we’ve landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don’t see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let’s call it in, in agent engineering.

Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there’s no point trying to stake your reputation on this thesis that we’re there because if it changes again, just change with it.

It’s fine.

[00:04:33] Jacob Effron: Yeah. It’s always, you know, I’ve always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you’ve seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they’re like, look, we build, you know, what’s ahead of the models and we’re willing to throw everything out every three months, you know, as the models get better and better.

Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That’s like decently sticky. Um, you know, they will mostly stick, you know, they’ll, they’ll give you a shot at least of, of building these things. What I’ve always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it’s like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.

Yeah. And so it’s definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.

[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it’s like. Very complete. Like, they’ll leave to like the, the hot new thing, uh, because there’s like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.

Like it’s, it’s a, it’s a known thing. Uh, so I think like basically what we’re talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.

[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your

[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn’t have you, they would’ve to hire in house and they’re not gonna hire in house so they have you. And like, I think that’s like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.

I do think like there is, um. It like sort of useful horizontal companies being built, but they’re all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it’s another form of compute guys, like, let’s not get too excited about it.

But I mean, like the, the workloads are enormous.

[00:06:38] Jacob Effron: Right.

[00:06:38] swyx: Yeah.

[00:06:39] Jacob Effron: It’s interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there’s a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.

And, you know, uh, I think there’s questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?

[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I’m involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I’ve been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.

But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research

[00:07:38] Jacob Effron: from my seat.

I can’t tell how much of it is like actual, you know, value that’s provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the

[00:07:45] swyx: I think it’s both.

[00:07:46] Jacob Effron: Yeah.

[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it’s not subsidized, people do choose it as like one of the top four or five.

This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it’s not subsidized. Like, so that’s as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.

Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you fi