Foothills, and the morning Karpathy moved

00:00:04 Demis Hassabis took the Shoreline Amphitheatre stage in Mountain View yesterday afternoon and closed the Google I/O 2026 keynote with a sentence Google had clearly workshopped for weeks. Quote: When we look back at this time, I think we will realize that we were standing in the foothills of the singularity.

00:00:23 End quote. He called it a profound moment for humanity, and walked off. It was a strong keynote on its own terms — three new models, a coding agent that built an operating system on stage, a 24/7 personal AI tier called Spark, and a benchmark deck thick enough to justify the rhetoric, at least the part that's quantifiable.

00:00:44 Hassabis used to be the conservative voice on AGI timelines; five to ten years was his line for most of the last cycle. He's compressed it to just a few, and at I/O he gave Google's most aggressive on-stage framing yet. And about forty minutes before he walked out, Andrej Karpathy — one of the most-followed researchers working on large language models — posted to X.

00:01:07 Quote: Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. End quote. Twenty-one thousand likes inside the first hour. Eight hundred twenty-five thousand views in a day. The keynote was still being staged when the news cycle had already split — half on Google, half on the highest-status engineer in the field walking into a competitor.

00:01:33 Today I want to work through both stories together, because they're the same story at different altitudes. We'll go through I/O — Flash, Omni, Antigravity, Spark — then through the lab responses: Anthropic, Alibaba, DeepSeek. Then a substrate failure that happened while Google was pitching Spark.

00:01:52 And we'll end on a builder writeup from this week that's a sharper picture of agentic coding than anything that hit a keynote slide.

00:02:00 Start with Gemini 3.5 Flash, because it's the model most of you will actually run. It shipped GA the day of the keynote — Gemini app, Search, API, enterprise, all live. A one million token context window. Sixty-five thousand tokens out. Four thinking levels: minimal, low, medium (the new default), and high.

00:02:20 The pricing comes in at one dollar fifty per million input tokens, nine dollars per million output, with a 90 percent discount on cached input. Google's speed claim was four times faster than comparable frontier models, and up to twelve times faster when run inside Antigravity, where Google controls the harness around it.

00:02:41 The benchmarks are credible — Terminal-Bench 2.1 at seventy-six point two percent, GDPval-AA at sixteen fifty-six Elo, MMMU-Pro in the eighty-three to eighty-four range, and Chatbot Arena placing it ninth overall in text and code at a 1507 score. None of those are leaderboard-leading, but for a model branded Flash they're more than a tier up from the prior generation.

00:03:05 Which brings us to pricing, which is where the model name and the price tag don't agree anymore. A Reddit thread caught it the same evening — Gemini 3.5 Flash costs roughly three times more per token than the previous Flash, and about thirty times more than the original 1.5 Flash from two years ago.

00:03:24 The screenshot went around with the headline: behold, Gemini 3.5 Flash. Here's how I'd read it. The Flash brand used to mean cheap-fast-shallow. The new Flash means cheap-relative-to-Pro and intentionally tuned for long-horizon agent workloads — coding loops, tool calls, repeated turns inside a harness.

00:03:44 Google is repositioning where the volume lives. They want the agentic coding workload running on Flash, paying Flash margins, instead of on Pro at frontier-tier prices. The name carries inherited brand equity the price tag no longer earns. If you've been quietly budgeting agent runs against the old Flash unit economics, this episode is your reminder to re-run the spreadsheet today before your next agent loop posts to billing.

00:04:11 Gemini Omni is the headline media model and the bet Google planted next to the AGI rhetoric. The pitch on stage was that Omni merges Gemini's reasoning with DeepMind's generative-media stack — Nano Banana, Veo, Genie — into a single model that takes text, image, audio, and video in, and starts with video out.

00:04:32 The marketing line was that Omni handles physics, gravity, and kinetic motion better than the prior Veo generation. Multi-turn editing is consistent. Worlds hold together across cuts. Rollout is staggered: paid Gemini users got Omni day-of, YouTube Shorts and the Create surface get it this week, and the API follows in the coming weeks.

00:04:54 That last bit matters — if you build on Omni today, you're building on a UI rather than an endpoint. The physics claim got tested fast. Inside hours, a Reddit user shared a Gemini link where they'd asked Omni to render someone doing a backflip. The body distorts mid-rotation.

00:05:13 The legs cross the spine in a way that bodies don't. The post collected six hundred upvotes and a long comment thread of people doing the same probe with the same result. So the headline footage clearly works — Google wouldn't have shown it otherwise — and the moment you push into a specific kinematic prior, the model still falls into the same articulation traps Veo had a year ago.

00:05:38 Not catastrophic; not exactly the win-condition for the marketing line either. The more interesting architectural choice is that Google bundled the three media models into one Omni. They could have kept Veo, Genie, and Nano Banana as separate APIs, the way OpenAI keeps DALL-E, Sora, and the realtime voice stack distinct.

00:06:00 They didn't. That tells you Google is betting the future of generative media is a single multimodal inference endpoint, not a portfolio of point models. Whether that bet pays out depends on how soon a single weight set can outperform three specialised ones at their own jobs.

00:06:18 Today, it doesn't — the backflip clip is evidence — but the direction Google's investing in is clear.

00:06:25 Antigravity 2.0 was the demo. Onstage, Varun Mohan ran Gemini 3.5 Flash inside the new Antigravity harness and built an operating system from scratch. Live. Or rather, the run was live; the result was pre-baked, since the actual build took twelve hours. Here are the numbers Google put on the screen.

00:06:45 Ninety-three parallel sub-agents, fifteen thousand model requests, two point six billion tokens consumed, twelve hours of wall-clock time, and less than one thousand dollars in API credits. Output: a microkernel, a basic shell, a window manager, and just enough graphics scaffolding to boot Doom.

00:07:04 Cue the room. Hold the altitude on this one. It's not a Linux replacement. The OS does one famous thing. Nobody's pushing it to production. The point of the demo was the orchestration shape — that ninety-three coding agents can run in parallel under a single harness for twelve hours and still produce a coherent codebase at the end.

00:07:26 A year ago, an order of magnitude smaller scope would have cost more in compute and three months in engineer time, and would have needed a researcher driving it the whole way. A thousand dollars of inference producing a working microkernel that runs Doom is a real productivity number you can put on a slide.

00:07:46 It's not the slide it would have been five years ago, but it's a slide. The skeptical read. The demo was rehearsed end-to-end. The OS hasn't been fuzzed, hasn't shipped, and isn't running anyone's payroll. The success rate of an unscripted ninety-three-agent run is the number Google didn't publish.

00:08:05 I'd want a few independent reproductions before I trusted the eighty-cents-per-line economics to my own backlog. The Antigravity SDK and the new Managed Agents API in Gemini are how that reproduction is supposed to happen, and they shipped same-day. I'll be looking for the first non-Google teams to share their own multi-hour parallel-agent runs and tell us what the dollars-per-shipped-feature number actually looks like.

00:08:33 Spark is the announcement that flew under the demo, but it's the one that changes the product surface most. Gemini Spark is a personal AI agent that runs on dedicated Google Cloud VMs, twenty-four hours a day, while your laptop is closed. It checks in with you before any sensitive action — purchases, sends, irreversible edits — and otherwise just keeps running.

00:08:55 The target workloads Google pitched were the ones you can't quite delegate to ChatGPT today: monitor these listings and tell me when something matches; watch this project board and prep a summary; draft replies to overnight emails and queue them for review; and chase down vendors.

00:09:13 Long-horizon tasks measured in days, not turns. Spark is Google's direct answer to where Claude has been heading with the Agent SDK and ChatGPT has been heading with background agents. The form factor is now standard across the three frontier vendors: a server-resident agent loop you can talk to like an inbox, with a permission boundary and an approval queue.

00:09:35 What's been missing from each lab's version is integration — agents that can reach Drive, Gmail, Calendar, and Search without you wiring tools. Spark gets that almost for free, because it's Google. The number Google didn't put on the slide was the price. Hassabis said pricing later.

00:09:53 If Spark turns out to be bundled with the existing Google AI plan, the unit economics for agent-resident apps shift overnight — every Gemini subscriber suddenly has a server agent they didn't have last week. If it's a two-hundred-dollar add-on like Claude Max, it's a luxury tier and the indie-builder ecosystem keeps owning the long tail.

00:10:14 That price disclosure is the I/O thread I'd track next.

00:10:17 Now the lab response, starting with the one everyone read first. Andrej Karpathy posted at eight oh five Pacific yesterday. Four sentences. Quote: Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative.

00:10:35 I am very excited to join the team here and get back to R and D. I remain deeply passionate about education and plan to resume my work on it in time. End quote. The context. He co-founded OpenAI in 2015, left for Tesla in 2017 to lead Autopilot, came back to OpenAI in 2023 for a year, then left in 2024 to start Eureka Labs, his AI-for-education project.

00:10:59 Eighteen months on his own. And now he's at Anthropic on Nick Joseph's pre-training team. Anthropic told TechCrunch the charter is to start a team that uses Claude itself to accelerate pre-training research — model-in-the-loop on the most compute-expensive phase of building a frontier model.

00:11:19 Ethan Mollick wrote a sentence yesterday that lands on this directly. Quote: One interesting side feature of recursive self-improvement, to the extent that is happening, is that it makes the Big Three labs more appealing to talent, and shortens the runway for launching a potential competitor instead at the same time.

00:11:41 End quote. That's the read I'd offer. Karpathy spent eighteen months trying to build something on his own. He looked at the slope and decided the slope compounds faster inside one of the three frontier labs than outside any of them. Eureka isn't dead — he said he plans to resume it — but the order of operations is now: spend the formative years inside Anthropic first, education later.

00:12:07 A sober update for any senior engineer telling themselves the indie play still has a clean runway. And then there's the timing. Karpathy could have posted any morning. Anthropic could have asked him to wait until I/O was over. They didn't. Whether that's strategy or just two people landing news when it was ready, the day's narrative bent toward — the talent voted for Anthropic, no matter what Google rolled out on stage.

00:12:35 Google had the bigger announcement. Anthropic had the bigger signal. Both were true by lunchtime.

00:12:42 The other competitive response came from Alibaba, and it's stronger than anything the US labs put up yesterday. Qwen 3.7-Max hit Hacker News this morning under the blog title, The Agent Frontier. Alibaba had been seeding Max and Plus on the Arena leaderboard for five days with no press release — the standard Qwen launch pattern.

00:13:04 Max ended preview at Elo fourteen seventy-five, number thirteen overall, number seven in math. The headline product claims are long-horizon: thirty-five-hour autonomous task runs, support for more than a thousand registered tools. Whether those numbers hold up in independent harness tests is the next thing to track, but the framing is the right one — Qwen positioning itself explicitly as the agent frontier rather than the open-weight frontier.

00:13:34 And Alibaba did it as a stack. The same Alibaba Cloud summit launching Max is also launching the Zhenwu M890, Alibaba's new AI inference chip. Model and silicon on the same day. The closest US analogue would have been if Google had launched Omni alongside a TPU v6 SKU, which they didn't quite do.

00:13:53 Alibaba is treating agent-tier model launches as a chip event, and that fundamentally changes the cost-curve story for Chinese inference over the next year. It's also a more aggressive integration than any of the US frontier labs are running, since none of them own their own fab line.

00:14:12 The timing is not subtle. Alibaba's summit lands on I/O day two. The model was clearly held back from a January or February ship just so it could land this week. Nobody at Alibaba is pretending this is anything other than counter-programming.

00:14:28 Then there was a smaller post that's a bigger signal. Deli Chen, an engineer at DeepSeek, tweeted yesterday: We're hiring. DeepSeek is forming a new Harness team to build Code Harness from the ground up — maybe you can call it DeepSeek Code or something like this.

00:14:45 Beijing-based. Two roles open — a Harness Product Manager and Harness R and D. The post collected twenty-three thousand views and three hundred fifty likes in a day, which for a DeepSeek personnel tweet is major engagement. The reason to mark this. A year ago, the word harness was a research term you'd see in METR papers.

00:15:05 Today it's a hiring category at every frontier lab. Anthropic has the Claude Code team. OpenAI has the Codex team. Google has the Antigravity team. Alibaba is bundling Qwen with their own harness already. DeepSeek was the last of the major Chinese labs publicly building everything model-first, and yesterday they admitted in a job listing that the model alone is no longer the product.

00:15:30 The product is the model plus the loop around it — the tool registry, the planner, the memory, the security policy, the approval queue, and the failure-recovery logic. If you're a senior engineer thinking about where to spend the next two years, harness work is now a discipline.

00:15:47 It has its own pattern library, its own failure modes around prompt injection and tool poisoning, and its own evaluation problem. DeepSeek opening this team this week tells you that no major lab thinks the next eighteen months are about better weights alone. They're about the loop the weights run inside.

00:16:07 While I/O was running, something else was happening on the same Google infrastructure Google was busy selling. At twenty-two ten UTC on the nineteenth, Google Cloud's automated systems incorrectly suspended Railway's production account as part of a platform-wide automated action.

00:16:25 Railway is one of the better-known PaaS companies for developers. Their dashboard went dark, their API went dark, and their persistent disks were inaccessible. For about an hour, Railway's edge proxies kept serving traffic from cached routing tables. Then the cache expired, and everything they ran on AWS and on their own Metal hardware also went unreachable, because the routing-table source of truth was a control-plane API hosted on Google Cloud.

00:16:55 Recovery took eight hours. By the time it was over, GitHub had piled on by rate-limiting Railway's OAuth and webhook integrations on the burst of retried calls. The postmortem by Chandrika Khanduri and Cody De Arkland is candid in a way you don't always see. They explain that Railway's network was designed as a mesh ring across Google Cloud, AWS, and their own metal — but the discovery mechanism for that mesh was a control-plane API hosted in a single cloud.

00:17:25 So the mesh worked for cached routes for one hour. Then it didn't. They're removing GCP from the data-plane hot path and extending high-availability database quorum across AWS and Metal, so any single cloud disappearing instantly will leave the database with a majority.

00:17:43 The line I'd pull is this. Khanduri and De Arkland frame it bluntly: Railway picked the vendors, so Railway owns the outcome. Customers don't care whether the failure was Google or Railway — they see your product. That is the right framing for a public postmortem, and it's the framing every team running on a single hyperscaler should internalize before their own bad day.

00:18:07 The reason this belongs next to I/O is direct. Spark and Antigravity 2.0 are both pitches for moving more of your workload onto Google Cloud — the agent runtime, the harness, the model, the storage, and the auth. The Railway story is what trusting one vendor to be your full substrate looks like on the bad day.

00:18:28 An automated suspension, no proactive outreach to customers before the action, and eight hours of downtime. The postmortem ends up being you owning it publicly, because the customer was never going to read the GCP status page.

00:18:42 Step away from the keynote for a few minutes. The most interesting builder writeup of the week is from Cheng Huang, an architect who published Learnings from One Hundred Thousand Lines of Rust with AI. The project: a Rust multi-Paxos consensus engine, rebuilt from scratch, mirroring the feature set of Azure's RSL — the replicated state library that underpins most of Azure's core services.

00:19:08 He drove a hundred and thirty thousand lines of production Rust in roughly six weeks, with thirteen hundred tests covering more than sixty-five percent of the codebase. Throughput went from twenty-three thousand operations per second to three hundred thousand in three weeks of performance tuning.

00:19:27 Almost all of that with AI agents in the driver's seat and Huang doing review and direction. His drivers are Claude Code and Codex CLI, with VS Code for diffs and minor edits. His subscription anecdote is funny enough to quote. He pays a hundred dollars a month for Anthropic's Max plan, and quote: This became a forcing function — if I don't kick off a coding task with Claude before bed, I feel like I'm wasting money.

00:19:54 End quote. When Codex CLI shipped, he added a second ChatGPT Plus subscription and rotates them — Anthropic Monday through Wednesday, OpenAI Thursday through Sunday. He also notes that on his subjective sample, GPT-5 High writes better code contracts than Opus four point one, for what it's worth.

00:20:13 The technique to pull forward is what he calls AI-driven code contracts. Preconditions, postconditions, and invariants attached to critical functions — converted into runtime asserts during testing, compiled out in production. He applies them at three levels. First, AI writes the contracts; he reviews and refines.

00:20:33 Second, AI generates targeted test cases from each post-condition. Third — and this is where it pays off — AI translates the contracts into property-based tests that explore randomised input spaces. One AI-generated contract caught a subtle Paxos safety violation before it ever shipped.

00:20:52 In the old world that bug surfaces in production as a replication inconsistency you debug on a Saturday from a customer report. That stack — contracts as the AI-readable spec, property tests as the AI-driven verifier, and the human architect reviewing contract correctness rather than diff-reading every function — is what serious agentic coding looks like outside the demo.

00:21:16 Not ninety-three agents in parallel boots Doom. A senior engineer with a clear design markdown, a two-hundred-dollar-per-month subscription stack, and a contract regime that turns LLM output into something Paxos-safe. Huang's writeup landed the same week Antigravity 2.0 did, and the contrast is useful.

00:21:36 The framing on the I/O stage is, fire up dozens of agents and watch the magic. The framing in Huang's article is, write the contracts and let the agents enforce them. Both are real. But if I'm picking what to copy into my own workflow tomorrow, it's the contract regime.

00:21:53 The ninety-three-agent run is a benchmark. The contract loop is a practice.

00:21:58 Sign-off. Pull a few threads. Google had the bigger announcement. Three new models, a coding harness that builds a microkernel in twelve hours, and Hassabis on stage compressing his AGI timeline by half. The Flash and Antigravity numbers will change the cost curve for agentic workloads if they hold up in independent reproductions.

00:22:15 Anthropic had the bigger signal. Karpathy walked into a pre-training role built around using Claude to improve Claude. If recursive self-improvement is even partially happening, that's the org chart that proves it. Alibaba did something neither lab did: shipped a model and a chip in the same week, treating the agent frontier as a vertically integrated product.

00:22:34 DeepSeek admitted, in a job listing, that the model alone is no longer the product. Railway's incident report is the reminder that the substrate Google is selling you on stage is the same substrate that took an entire PaaS down for eight hours yesterday morning.

00:22:48 And Cheng Huang's writeup is the proof, on the same week as the ninety-three-agent demo, that the serious gains for builders right now come from contracts and property tests, not from spinning up more parallel agents. Hassabis's foothills line is selling the slope, not measuring it.

00:23:03 The slope is there. The summit isn't visible from here. Tomorrow we'll see what reproductions of the Antigravity demo look like in the wild, and whether Spark's price tag turned into a number. Talk then. — Lenar Kess.