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 lea
