daily

By Dan Walsh

Kilmore Quay RNLI launched this Sunday afternoon to assist the crew of a yacht taking on water in Kilmore Quay harbour

It happened as the crowd, including the volunteer crew, gathered for the annual Blessing of the Boats ceremony in the harbour, the crew were requested to launch their all-weather Tamar class lifeboat RNLB Killarney by the Irish Coast Guard mid-ceremony at 5.20 pm to assist a recently arrived yacht in the marina.

The vessel had taken on a significant amount of water having suffered some damage on the voyage to Kilmore Quay.

The lifeboat crew checked that everyone on board the vessel was safe and well before assessing the situation. The salvage pump was deployed to attempt to drain the vessel. Taking into consideration the immediacy and gravity of the situation, it was decided that the safest course of action was to assist in manoeuvring the vessel to the slip where it could be safely removed from the water by a local marine engineering company.

The vessel safely secured at the slip, the lifeboat returned to its berth in the marina and was made ready again for service.

The crew were then joined by parish priest Fr Pat Mernagh who completed the day’s Blessing of the Boats ceremony with a special blessing for the lifeboat and its crew.

The United Kingdom’s union is under “unprecedented pressure from a rising pro-independence tide”, Sinn Féin president Mary-Lou McDonald has said. Ahead of the Dáil debating and voting on her party’s Irish Unity Bill this week, Ms McDonald described “history unfolding”. She was speaking during an address to her party’s annual commemoration of United Irishmen leader Wolfe Tone at Bodenstown, Co Kildare, on Sunday, She said they were gathered not only to commemorate and honour the past, but to “testify that together we will unite Ireland and bring about a New Republic of the people, by the people, and for the people”. Ms McDonald welcomed Fine Gael’s recent announcement that they will publish their proposals on Irish unity, and the SDLP’s Irish unity conference in Belfast last week. She said they now “need to see positive movement and enthusiasm” from Taoiseach and Fianna Fáil leader Micheál Martin. Ms McDonald said Sinn Féin has had “a unique experience” of the challenges involved in advancing a united Ireland in their sharing office with unionists at Stormont. “We share office with others whose instinct is too often to resist change, block progress and hold back political, social and economic development,” she said. “But that has not stopped us from providing leadership.” The Sinn Féin president also emphasised that momentum towards Irish unity is continuing to grow. “The election of nationalist governments in Scotland and Wales underlines that Britain’s union is under unprecedented pressure from a rising pro-independence tide,” she said. “I believe that the time has come for a United Ireland. “The planning and preparation must start now … because we now have an unprecedented opportunity for all parties to enter the conversation and begin planning for unity with enthusiasm.” She called for words to be matched by action. “The Irish Government must establish the structures, allocate the resources and begin the detailed work of preparing for constitutional change in partnership with communities across this island,” she said. “Despite the clear provisions of the Good Friday Agreement, and the profound implications of reunification for every aspect of Irish life, Fianna Fáil and Fine Gael have yet to demonstrate the urgency this moment demands. “The greatest threat to stability is not preparing for constitutional change. It is refusing to prepare. “Irish unity must be shaped through careful planning, democratic engagement and honest discussion. “That work cannot be delayed any longer.” She went on: “Next week, the Dáil will debate and vote on our landmark Irish Unity Bill. “It calls on the Government to draft a Green Paper and establish a Citizens’ Assembly. “If passed, it would represent a significant step forward and put planning for a United Ireland at the top of the agenda. “History is unfolding before us. “What matters now is that the conversation deepens, that the preparations start, and that whoever next occupies 10 Downing Street realises that Britain can no longer fix the boundary to the march of this nation.” Referencing Tone’s Protestant background, Ms McDonald said ordinary Catholics, Protestants and Dissenters had “far more in common with one another than with those who sought to keep them apart”. “Recent scenes of racist violence and attacks on the streets of Belfast were a stark reminder that division and scapegoating remain potent forces,” she said. “Irish republicans reject racism just as we reject sectarianism. “The answer to social and economic challenges is not to pit working people against one another, but to unite people around their shared interests and common future. “That is why I say directly to working-class unionists and Protestants – you have more in common with your nationalist neighbour than with the wealthy interests that profit from division and inequality.”

Quickstart · How it compares · How it works · Docs · CLI · Changelog Give your coding agent the decisions your team already made — so it stops re-doing things you ruled out. Lore keeps your team's recorded knowledge — requirements, decisions, designs, roadmaps, and prompts — as typed Markdown in your repo and serves it read-only to Claude Code, Cursor, and Claude Desktop over MCP, so the agent cites your decisions instead of violating them. No RAG, no embeddings, no model call to decide what's relevant — retrieval is deterministic and reproducible. It is built on RAC — Requirements as Code, the open-source engine underneath; the package, CLI, and MCP server ship under the rac name. Lore isn't a search index or a memory tool — it's the deterministic system of record an agent grounds against. Fuzzy retrieval (RAG, agent memory) is good at finding what's near a loose question; Lore is good at returning the exact, current decision and declining the ones you've superseded. They compose well — recall fuzzily, then verify in Lore. | Lore | Fuzzy retrieval (RAG / agent memory) | | |---|---|---| | Good at | the exact, current decision | finding what's near a question | | Retrieval | deterministic, reproducible | similarity-ranked, varies by run | | Role | source of truth, read-only | a fast index or working copy | | In CI | enforced (rac validate / rac gate ) | not its job | - Install the engine: pip install rac-core - Scaffold identity and your first artifact: rac quickstart - Connect your agent (Claude Code, from your repo root): claude mcp add lore -- rac mcp - Enforce in CI so bad knowledge never lands: rac validate rac/ && rac gate rac/ | Command | Gets you | |---|---| pip install rac-core | the rac CLI + the lore MCP server | pip install 'rac-core[ingest]' | + DOCX / HTML import | pip install 'rac-core[ingest-all]' | + PDF / PPTX / XLSX import | pip install 'rac-core[explorer]' | + the terminal Explorer (rac explorer ) | Requires Python 3.11+. uv tool install rac-core also works. - Typed Markdown, in your repo. Every artifact is plain Markdown with a tiny frontmatter envelope; the engine classifies it deterministically and validates it against a per-type schema. - Read-only at serve time. The MCP server only ever reads; the trust boundary is human PR review, and the agent cannot mutate the store. - Enforced at write time. rac validate andrac gate reject malformed artifacts, broken or ambiguous links, and references to superseded decisions — in CI, before the knowledge lands. - Air-gapped by design. The engine makes no LLM calls and no network calls; the only egress is a consent-gated, content-free usage ping that is off by default, and regulated installs can prove it stays off with rac telemetry off --enterprise (security posture, ADR-086). Claude Code (from your repo root): claude mcp add lore -- rac mcp Claude Desktop / Cursor (mcpServers in the client config): { "mcpServers": { "lore": { "command": "rac", "args": ["mcp", "--root", "/absolute/path/to/your/repo"] } } } rac quickstart # set up identity + scaffold your first artifact rac new decision adr.md # scaffold a typed artifact (mints the id) rac validate rac/ # check every artifact in a directory rac inspect requirement.md # see its type and completeness rac review rac/ # full repository review, worst problems first rac gate rac/ # the merge gate: validate + relationships + review Already have decisions in Confluence, Notion, or loose Markdown? The rac-import agent skill turns one existing document into one valid artifact, with a human-review step before anything is written: rac skill install rac-import Then ask your agent, in plain language: "import this decision doc into Lore." It drafts from only what your document says, shows you the proposed type, title, and relationships to confirm, scaffolds with rac new , and closes on rac validate . For multi-format or bulk conversion, use the rac-ingest skill. rac export rac/ --html --out lore.html # the Portal: the whole graph, one file rac export rac/ --okf # a conformant Open Knowledge Format bundle rac export rac/ --documents # JSONL for memory/RAG backends rac export rac/ --graph # the typed decision graph for graph backends The --documents and --graph modes feed external memory, RAG, and graph tools so an agent can recall fuzzily there and then verify in Lore — see the CLI reference. The connectors themselves live in the separate lore-connectors companion. The engine is a library too; its public surface is rac.__all__ . from rac import parse_file, classify, find_artifacts art = parse_file("rac/decisions/adr-001-markdown-first.md") print(classify(art).type) # -> "decision" result = find_artifacts("rac/", "caching") # returns a SearchResult for hit in result.matches: print(hit.id, hit.title) Google's Open Knowledge Format (OKF) standardises the carrier — a Git tree of Markdown with YAML front matter — and is deliberately permissive. RAC writes that same carrier and adds what OKF leaves to the consumer: write-time enforcement in CI. rac validate and rac relationships --validate reject malformed artifacts, broken links, and references to superseded decisions, deterministically, before the knowledge lands. rac export --okf turns any RAC repo into a conformant OKF bundle — so the two compose rather than compete. - Teams running coding agents heavily (Claude Code, Cursor) tired of the agent ignoring decisions the team already made. - Teams who already write ADRs and want those decisions to actually shape what the agent does. - Anyone who wants the why behind their software versioned alongside the code. Full documentation: https://itsthelore.github.io/rac-core/ - Quickstart — install and author your first artifact - MCP server — tools, client configuration, examples - CLI reference — every command, flag, and exit code Lore is the product surface of RAC — Requirements as Code, the open-source engine underneath; the package, CLI, and MCP server ship under the rac name, and lore is the server identity and brand. Wayfinder, the deterministic prompt-complexity router, began as a route experiment inside RAC and was split into its own tool — routing is a runtime concern, not a knowledge one. rac-core/ src/rac/ the engine: CLI, core, services, output, the in-process MCP server (rac mcp), and bundled skills, templates, and git hooks rac/ the dogfood corpus — requirements, decisions, designs, roadmaps, and prompts that govern the project itself tests/ per-service batteries plus core / cli / artifacts coverage (ADR-027) docs/ the documentation site (MkDocs) examples/ the grounding demo, woven into the corpus and the test fixtures rac-localview/ the Portal / graph viewer, vendored into the engine pip install -e .[dev] python -m pytest ruff check , ruff format --check , and mypy src/ run in CI alongside the per-service batteries (ADR-027). Lore is early and evolving quickly. The MCP server ships today. Contributions, ideas, and experiments welcome — see CONTRIBUTING.md.

Michael Barlow Model Training as Code TL;DR: Model training has grown complex enough to require many specialised stages and teams, and manual coordination between them doesn’t scale. At Aleph Alpha, we’ve built Savanna, a model factory that implements the entire training pipeline in code, turning model training into a collaborative software project. In Savanna, end-to-end training runs are hermetic, and launchable with one click. This post describes Savanna, why it’s needed, and the engineering culture that makes it effective. Introduction Model training is moving fast. New stages keep joining the pipeline and existing ones grow more intricate, making model training an engineering challenge for three key reasons. First, more complexity means more room for error: bugs or inconsistencies in data, code, or configuration can cause entire training runs to fail or diverge. Second, the cost of failure keeps rising: models grow larger, GPU prices rise, and ever more data is processed per run. When you’re burning thousands of GPU hours, “oops” is an expensive word. The third and hardest challenge is organisational. The complexity has long outgrown the capacity of a single mind, so labs like ours have built large, specialised teams. The problem then becomes coordinating those teams. How can members autonomously explore the latest research in their area of expertise, while integrating their changes into the production pipeline without breaking it or interfering with each other’s work? And how do they ensure that improvements to individual stages translate into a better model at the end? Traditional, manual model training processes do not have good answers to these questions. The hidden cost of manual model training Let’s break down the traditional manual process. At a high level, model training looks simple: pre-training to absorb the internet, followed by post-training to learn instruction-following. In reality, you don’t train a good model on your first try. Arriving at a good data mix, architecture and training recipe is an iterative, compute-bound process guided by evaluation, where each arrow below is weighted by its relative GPU cost: Each of these components is complex enough to warrant multiple dedicated teams. Modern post-training, for example, comprises a supervised fine-tuning (SFT) stage followed by a reinforcement learning (RL) stage. SFT and RL require different skillsets and tools, but must be integrated to train a model. Consider what a single model’s journey through the pipeline might look like in a manual lab: The data team finishes a new mix and sends the database path over Slack to the pre-training team, who kick off a multi-week run. Two weeks in, the storage quota fills up and the training run crashes. The filesystem is managed manually, so no one is sure whether it’s safe to delete that 30TB dataset with do_not_delete in its file name, and the GPUs sit idle while the pre-training team works this out. When they finally relaunch, they reconstruct the original setup from memory and Slack threads, hoping they didn’t forget to set a flag. This is the first hidden cost: every manual step is an opportunity for human error. When pre-training is eventually complete, the pre-training team hands the checkpoint to the SFT team. To find a good recipe, the SFT team then manually kicks off a sweep of parallel trainings with different configurations and data mixes. As checkpoints roll in, the team runs their evaluation script on each one, sharing results and analysis in Slack. Some recipes look promising, others don’t, and they repeat this process for a few weeks until they narrow down a good one. Without realising it, the team repeated some experiments that they already completed for the previous pre-trained checkpoint a few months back. This is the second hidden cost: the team forgets its learnings. There’s no durable record of the reasoning behind a hyperparameter’s current value, no formal link between a data mix and its constituent datasets, and no clear attribution attaching a model to the training recipe that produced it. In a manual lab, this lineage is scattered across Slack, the filesystem, an experiment manager and various wiki pages, and is easily lost over time. The SFT team hands their checkpoint to the RL team, who kick off a training run with this as the base. The final model underperforms. Is the RL recipe overfit to last month’s SFT checkpoint, or is the SFT checkpoint itself at fault? After two weeks of debugging, the RL team confirms it’s the latter. Neither team can execute the other’s stage, so each had optimised for its own slice of the pipeline rather than the model at the end. And because integration is a manual hand-off, it happens rarely, leaving a month’s worth of divergence to reconcile each time. This is the third hidden cost: manual, infrequent hand-offs fragment teams’ ownership. It’s clear that manual model training doesn’t scale, and automation is not the only missing piece. Each of these hidden costs stem from the same underlying problem: the pipeline lives in the minds of the team rather than in a shared, durable artefact. To scale, the pipeline itself needs to be something tractable that teams can collaborate on. Introducing: Model Training as Code Our model factory, codenamed Savanna, implements our entire model training pipeline and processes in imperative code. We call this Model Training as Code (MTaC). Here’s what a simple Savanna post-training pipeline looks like in pseudocode: async post_train(config: PostTrainConfig) -> PostTrainEvaluation: sft_checkpoint = await sft(config.sft) sft_eval = spawn evaluate(config.eval, sft_checkpoint) rl_checkpoint = await rl(config.rl, sft_checkpoint) rl_eval = spawn evaluate(config.eval, rl_checkpoint) return PostTrainEvaluation(await sft_eval, await rl_eval) By lifting the pipeline into code, you gain three things: composability, consensus and provenance. - Composability: Composability comes from expressing manual steps as functions with typed inputs and outputs, which you can then build abstractions around and compose into an end-to-end pipeline that runs with one click. Modifying the pipeline becomes as simple as editing a function, repetitive work like evaluating intermediate checkpoints can be automated with a for loop, and testing is straightforward, because you can run subsets or downscaled versions of the pipeline with a different parametrisation. - Consensus: Consensus comes from version control. The main branch represents the team’s collective best understanding of how to train a model. The code contains the full training recipe, so there is no setup to reconstruct or flag to forget when launching a training run. - Provenance: Provenance comes from code comments and commit history. These encode the learnings and decisions that led to main. Past training runs stay reproducible, as the code that produced them is pinned in a commit you can check out and rerun. When any team can launch the full pipeline themselves, they can run any other team’s stage and iterate on the model as a whole rather than just their own slice of it. Labs typically decompose model training temporally when they scale, with each team owning a stage of the pipeline. MTaC opens the door to a capability-based decomposition, where teams instead own a model behaviour like multilinguality from end to end. Integrate little, integrate often With the pipeline in code, standard code collaboration best practices apply, and to make the most of MTaC, you need to follow them. The most important of these is trunk-based development, where changes land on main as soon as they can and in small increments, so teams can build on each other’s work at the earliest opportunity and fail fast if an approach is wrong. If you instead accumulate changes in long-lived branches, you pay the same integration debt as before. Using Savanna Savanna lives in GitHub, where its CI is the entrypoint for model training. You can trigger this CI by pushing to a branch, or by manually launching a run from the GitHub UI. Training our best model is as simple as triggering CI on main . We also use CI on pull requests to quickly validate the pipeline with a small-scale end-to-end training run. This completes in under 5 minutes, so contributing to Savanna never feels slow, and it gives us confidence in our changes. To catch semantic regressions in our pipeline, we run a larger-scale end-to-end training test run every night, which validates our training logic by asserting that the resulting model achieves a measurable improvement on our evaluation suite. MTaC provides decision lineage via git blame , and Savanna extends this with artefact lineage. Runs are hermetic, and all non-code artefacts (e.g. data, models and tokenisers) are immutable and versioned in a registry, so you can be sure you’re getting the same thing every time. When you kick off a run, the referenced artefacts, training logs, metrics and evaluation results are all linked to that run and the resulting checkpoint, so you can easily attribute output to input. To determine which models were trained on a specific dataset, you use the artefact lineage graph, not Slack search. Savanna makes full-scale runs easy, but evolving what those runs look like requires the ability to experimentally modify every aspect of the pipeline, be that data, hyperparameters, pipeline steps, environments, sharding topologies, or anything else, at a smaller scale. In Savanna, experimenting is as simple as pushing changes to a branch and running CI from there. If you want to try a new dataset, you can add it to the ablation training config and run CI. If the resulting evals show an improvement, you can then merge this change into main to make it part of the next full-scale run. Hyperparameter sweeps are also easy. Since the pipeline is just a function, you can programmatically invoke it multiple times with different parameterisations. For example, below is an experiment to find which SFT and RL learning rates work best in the post-training pipeline shown previously: async post_training_learning_rate_experiment(config: PostTrainConfig) -> str: post_train_runs = [] for sft_learning_rate in (1e-4, 1e-5): for rl_learning_rate in (1e-4, 1e-5): run_config = config .with_sft_learning_rate(sft_learning_rate) .with_rl_learning_rate(rl_learning_rate) post_train_runs.append(spawn post_train(run_config)) post_train_evaluations = gather post_train_runs experiment_report_url = create_report(post_train_evaluations) return experiment_report_url Savanna will then orchestrate the sweep and collate the end results into a report for you to analyse. Here’s this experiment visualised in Savanna’s workflow engine UI: Blue nodes are “running” and orange nodes are “awaiting cache”. Although we call post_train four times, the SFT stage will only run twice: Savanna’s workflow engine recognises when stages have the same inputs and will read their outputs from the cache when the corresponding running stage completes. This lets you launch sweeps that modify multiple aspects of the pipeline without worrying about the combinatorial explosion of redundant computation. For pre-training runs or long-running RL jobs, we incrementally emit and evaluate checkpoints so we can better understand how our model changes throughout training. After training, Savanna automatically evaluates the model on our benchmark suite, and collects all results and metrics into a run report. Savanna also automatically updates our model leaderboard, which we display on a big screen in our Heidelberg HQ so everyone shares the excitement when we train our new best model. How Savanna works When you trigger a run, Savanna launches a job on Flyte, our workflow engine of choice, which runs in Kubernetes. Flyte provides a convenient way to run arbitrary tasks, durable execution, sequencing, parallelism, retries, caching and a UI to visualise runs. We store model and data artefacts immutably in our on-prem object store and track their versions in Weights & Biases. This lets us easily assign cleanup policies to keep our storage system tidy and gives us a UI to explore artefact lineage interactively. We then stream this data to our Kubernetes GPU clusters for training. During training, our jobs log metrics to our monitoring system so we can track progress and performance in real time, and send alerts if intervention is necessary. How has Savanna actually helped us? We know that the complexity of model training will only grow as the field advances, and the organisational challenges will grow with it. MTaC has enabled us to lift this organisational complexity into code, which has fundamentally changed the way we work. Day to day, we iterate faster. Most of our work is small-scale experiments and sweeps, and Savanna automates the manual launching and evaluation these used to involve. That effort now goes into analysing results and designing the next experiment. The stakes are different for a single large run. During our most recent large pre-training run we resumed training several times to tune our setup as we learned more about how the model was behaving through our automated intermediate evals. Each relaunch was quick and low-risk, because the consensus MTaC provided meant there was no setup to reconstruct and no flag to forget. And because relaunching was easy, it did not need to be the same person twice, so whoever was on hand could safely pick it up. At a higher level, MTaC has allowed us to set up multiple capability-oriented post-training teams who own the integration and improvement of a model behaviour end-to-end. Our multilinguality team, for example, is making the model excel at German language and culture by building the SFT datasets, RL environments and evaluation suites this requires. Since adopting Savanna and embracing engineering culture, our learning rate as an organisation has significantly increased, and the approach has only become more valuable as our pipeline has grown deeper and our teams larger. Looking further ahead, we see MTaC as a key enabler for auto-research. With our entire pipeline in code, an LLM agent can read, modify and run it autonomously, and we are beginning to explore this now. One day, we hope our model will be able to self-improve via Savanna. Acknowledgments Written by Michael Barlow. This work reflects the collective efforts of many contributors across Aleph Alpha Research who brought Savanna to life. Thanks to Martin Reinhardt, Dominik Kellner and Jordan Sassoon for helpful feedback that shaped this blog, and Paige Reddington and Noé Beckerle Vallejo for making it presentable.

Agentics / Tech Things: Tokenmaxxing is dead, long live tokenmaxxing Alt title: The reports of tokenmaxxing’s death are greatly exaggerated. I’ll be in SF for AIE. If you are around and want to say hi / meet in person, shoot me an email at amol@noriagentic.com. Generally speaking, if you spend tens of thousands of dollars on something, you want to see something come out on the other end. Some return on investment. O sure, not always. I’ve previously said that selling to consumers is sorta funny because they love spending money on things that waste time or actively cause pain. This is part of why the gambling apps are so popular these days. Why yes, I’d love to spend $100 on betting that Wemby scores a 3 pointer while doing a handstand and singing the national anthem in French.1 But for businesses? I’ve basically never heard a business leader say that they were going to set a bunch of money on fire because it made them feel good, at least not the same way a whale will spend thousands on Genshin Impact gatcha pulls. Like, imagine if some serious business leader, like, idk, Mark Zuckerberg, decided to announce that Meta was going to burn money. He could do that. He’s got the voting shares. But it would be a bit silly, wouldn’t it? I generally think if you’ve gotten to the point where you’re running really big really important companies, you mostly aren’t doing things for kicks, with one big exception. If you haven’t heard, tokenmaxxing is (was?) a phenomenon where executives accidentally encouraged their employees to burn a bunch of tokens on useless tasks. The canonical example of this is, by complete coincidence, Meta, which has been thoroughly skewered for tying performance evaluations to the amount of token usage per person. Obviously, obviously this was going to lead to people just burning tokens on nothing. One of my friends at Meta reported that they literally would just have two agents talking to each other throughout the day to get her token numbers up. This was such an obvious outcome that many people rounded this off as “these business leaders are really dumb because they decided to burn a bunch of money on tokens without expecting any return.” I understand why that’s a tempting take, because that is kinda sorta what the public face of a lot of this was. But I’m going to do my favorite thing in the world, which is be a bit contrarian. It wasn’t that “executives accidentally encouraged their employees to burn a bunch of tokens on useless tasks.” Rather, “executives purposely encouraged their employees to burn a bunch of tokens on useless tasks.” I work with a lot of teams on figuring out how to use AI effectively. A few months ago, there were a lot of people who were extremely resistant to using AI tools at all. Senior people, people that had a lot of respect in the organization. It was very difficult to convince these folks to use the tools. And when you did, they would often accidentally (or purposely?) use the tools in a way that would obviously lead to weird or bad outcomes.2 One way to think about the top down tokenmaxxing policies is that this was a technique by executives to break through. Yes, it was obviously a blunt force policy, but sometimes you need blunt force to break through a wall. Of course, that was the situation a few months ago, when there were still holdouts. It’s now a few months later, and the tokenmaxxing policies had their intended outcome: everyone is using AI to code, at least a little bit. Most teams haven’t yet figured out how to build their own Ramp Inspect or Stripe Minions (if that’s you, reach out — we can help!) but basically everyone is at least using cursor in the side bar. Which, of course, means that token spend has gone way up. Unfortunately, but probably not unexpectedly, the increase in token spend has lined up with both OpenAI and Anthropic trying to go public. Both companies have limited the amount of juice their subscriptions provide while jacking up their API pricing. Token subsidies are increasingly vanishing. So now the incentives are mostly gone and the cost is way up and, of course, teams are starting to roll back their unlimited-token-spend policies. All of this to say, tokenmaxxing is dead. Except…maybe not. The promise of AI tools generally is that you can have them run without human supervision to accomplish really hard and really tedious tasks that still need to be done. The big code migration, doing research on all your competitors every morning, keeping up with the stream of inbound and outbound — these are all things that people mostly hate doing and want AI to do. Up until recently, though, you couldn’t reliably have an AI run for long periods of time. If you tried, you would notice that small errors introduced by the models (including hallucinations) would take on a life of their own and eventually become irreversibly embedded into the project. In the business we called this “compounding error.” It not only required a fair bit of human supervision, it also kept token costs low because there was little benefit in running agents 24/7 to begin with. Like, what’s the point of running a little demon in your computer over night if the thing is just going to tear up all your hard work? If spending more tokens results in worse work, you obviously aren’t going to spend more tokens! That’s no longer true. We’ve entered a different regime, where spending more tokens generally results in better results. We call this “compounding correctness” — the more tokens you spend on getting a task correct, the more likely you’ll get a good outcome. We talked about this a bit at the last in person Agentics meetup: Compounding correctness flips the calculus. If more token spend leads to better outcomes, then you’re going to want to spend a lot of time running tokens. Which sure as hell sounds like tokenmaxxing to me! The original incentives to tokenmax are gone, but eventually folks will realize that a new and more powerful incentive has take its place. We’ve already seen some of this take place in the cyber security world: Last week we learned about Anthropic’s Mythos, a new LLM so “strikingly capable at computer security tasks” that Anthropic didn’t release it publicly. Instead, only critical software makers have been granted access, providing them time to harden their systems. … This chart suggests an interesting security economy: to harden a system we need to spend more tokens discovering exploits than attackers spend exploiting them. AISI budgeted 100M tokens for each attempt. That’s $12,500 per Mythos attempt, $125k for all ten runs. Worryingly, none of the models given a 100M budget showed signs of diminishing returns. “Models continue making progress with increased token budgets across the token budgets tested,” AISI notes. If Mythos continues to find exploits so long as you keep throwing money at it, security is reduced to a brutally simple equation: to harden a system you need to spend more tokens discovering exploits than attackers will spend exploiting them. You don’t get points for being clever. You win by paying more. It is a system that echoes cryptocurrency’s proof of work system, where success is tied to raw computational work. It’s a low temperature lottery: buy the tokens, maybe you find an exploit. Hopefully you keep trying longer than your attackers. Fable is, tragically, gone now. But the underlying concept here still remains. This is also in part why people are suddenly so excited about ‘loops.’ Boris Cherny, the creator of Claude Code, got up on stage and said ‘loops’ and everyone freaked out. The basic idea behind loops is that you run an agent until it reaches the end of its turn, and then when it finishes you simply restart the same prompt. With a bit of cleverness you can take a pretty heavy specification and automatically have the agent split it into parts and solve it over time. No human supervision required. Is this some new thing? No, not really. The loop concept has been around since literally last July. It used to be called a “Ralph Wiggum loop,” but as the industry has matured so has our sense of humor and the ‘Ralph Wiggum’ part was dropped. There were ways to get loops to work, but it was hard. You had to think a lot about how to prompt the agent, which in turn required a pretty deep familiarity with how these things work. Now, though, it’s easy. Compounding correctness makes it easy. You can basically prompt the LLM however you want and to a first approximation, it will do better every iteration of the loop. So is tokenmaxxing really dead? Maybe temporarily, but long term I don’t think so. Teams that are at the cutting edge are currently building or have built the infrastructure necessary to run agents 24/7. It’s only a matter of time before the bigcos realize that the cost benefit has shifted again. The real winners here are the open model platforms. Tokenmaxxing the top labs will never stand up to any amount of CFO scrutiny. As open models get better, it will become more popular to simply run those in a loop. That was the core thesis of Rohan’s talk above. If Claude gives you 1.1x improvement per iteration, and GLM 5.2 gives you 1.05 improvement per iteration but costs ~5x less, you can just run the second loop 5x more times and it will be better. The last thing I want to mention here is that some of the ridiculous token spend is downstream of a serious misunderstanding of the best way to use these tools. Before coding agents really took off (thanks in large part to much better harnesses like Claude Code), lots of people were making their own custom agents. And that was legitimate work! You had to think about this stuff as if it was…well, software. There was an art to figuring out the tools and the prompts but the core of it was still just software, even if it was supported by ‘AI native’ frameworks like Pydantic or Langchain. You can’t fit a square peg into a round hole. Executives across the board saw this style of building agents, went “o, this is just a more flexible zapier workflow,” and proceeded to demand data processing pipelines that could do one-off tasks that were ‘agentic’ instead of building those same pipelines in good ol’ deterministic code. ‘I need to do data labeling, so I will build a data labeling agent’, that sort of thing. Now, relying on an agent to do some of this stuff is already going to be significantly more expensive than just doing a workflow automation. But the bigger issue is the accuracy: none of these ‘agents’ ever really took off, because they were never as correct as a deterministic pipeline would be. If you’re committed to using agents but want to reduce the cost of hallucinations and things, what do you do? Why, you build another agent! A ‘quality checking’ agent, or something like that. And what if that agent gives you errors? Well you’ll just build another! And now you have 3x the token cost, enjoy! The story of tokenmaxxing is, again, one of RoI. That story didn’t just play out at the bigtechcos. It also happened at a less advanced scale at companies all over the country — companies who poured billions into random agent pipelines built by one off consultants that unfortunately never really quite worked all that well. Notice that these are actually two different kinds of tokenmaxxing. The first kind is ‘spend a lot of money on tokens for your developers‘. Here, devs are using tools like Claude Code and figuring out how to run things in loops and using a lot of tokens to do it. Ostensibly this is a good use of money because it’s making the engineers themselves more productive. The second is ‘spend a lot of money on tokens for your pipelines‘. Here, devs are still writing code by hand! They are using that code to create one-off agents to do very specific tasks often in a non-deterministic and brittle way, and it’s those agents that guzzle up all the tokens. This is only a good use of money if the pipelines work, which they don’t. But here, too, we are seeing a shift. Increasingly, these sorts of one-off pipeline-based tools are better done by generalist platforms that are skinned for the specific task, than an “agent” specially designed to do that one task. There’s some market arbitrage here. Some buyers haven’t realized that generalist agents have gotten really good, so they will go to consultants asking to ‘build me an agent’, and the consultant essentially writes a skill file and says “that will be $2m please.” Luckily, this too shall pass. Generalist model platforms are obviously the future for anyone who has used them (and if you haven’t, again, reach out!) And that, again, will lead to another rise in tokenmaxxing behavior in this part of the market. The natural end state of all of this is the ‘software factory’ or, even further, the ‘dark factory’ — a codebase that pumps out code, reviews code, fixes bugs, writes tests, and so on without any human supervision. The human simply puts in a spec and out comes an application. The folks over at StrongDM have taken this to the furthest extreme, arguing that engineers should aim to spend $1000 in tokens per day. This is almost certainly hype, part of a long trend of saying egregious things to get coverage and buzz. We have a software factory, and we spend like $600 per month. But the hype and buzz comes about because, even though it is currently ridiculous to spend the price of a senior google engineer in tokens per engineer, there is a kernel of truth to this. The incentive to spend ludicrous amounts of money on tokens are there, latent, waiting for diffusion. What’s old is new again and what’s dead may never die. Tokenmaxxing is dead, but we haven’t seen the last of tokenmaxxing just yet. Other things: GPT 5.6 is out, kinda sorta. From the announcement: We’re beginning a limited preview of the GPT‑5.6 series: Sol, our flagship model; Terra, a balanced model for everyday work; and Luna, a fast and affordable model. Terra has competitive performance to GPT‑5.5 while being 2x cheaper and Luna brings strong capability at our lowest cost. … We believe in broad access, and we plan to make GPT‑5.6 Sol, Terra, and Luna generally available in the coming weeks. As part of our ongoing engagement with the U.S. government, we previewed our plans and the models’ capabilities ahead of today’s launch. At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly. … We don’t believe this kind of government access process should become the long-term default. It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them. We are taking this short-term step because we believe it is the strongest path to broader availability in the coming weeks, while we work with the Administration to develop the cyber Executive Order framework and a repeatable process for future model releases. Washington Post was even more aggressive in its analysis: U.S. government will decide who gets to use latest upgrade to ChatGPT The Trump administration came to power preaching a laissez-faire approach to AI but has lately increased oversight of the industry. Reading between the lines, it looks like we are getting more government regulation of AI companies, but unfortunately the process as it currently stands is completely opaque. Reactions are mixed. On the one hand it’s great that the administration’s previous attacks on Anthropic are also being applied to their competition. On the other hand, it seems like the process is totally opaque, and now the government is unilaterally picking winners and losers not just among AI companies, but among every other industry where AI may matter (i.e. all of them). The companies who get to use OpenAI’s tooling (and Mythos, see below) are currently unknown. It would be very concerning / disappointing if we find out that it is exclusively companies with ties to this administration. Related: Mythos is back on the table, at least sort of. From Semafor: US releases powerful Anthropic model Mythos to some US companies The US government Friday lifted its block on Anthropic’s powerful Claude Mythos 5 AI model, allowing the company to release it to more than 100 US institutions, including major companies and government agencies. The decision, in a letter sent Friday afternoon to Anthropic, is a major de-escalation in the confrontation between the Trump Administration and one of the world’s most valuable private companies. Two weeks ago the administration imposed export controls on Mythos, leading to a shut down of the model and its cousin Fable 5 after warnings from Amazon and other companies that they could be “jailbroken” for malicious purposes. The letter is silent on Fable 5, a weaker version of Mythos that was briefly the most powerful AI model widely available to consumers. People close to the talks said they are moving toward releasing Fable as well, though that timeline is unclear. “I have determined that appropriate safeguards are in place to permit certain trusted partners to access the Claude Mythos 5 Model,” Commerce Secretary Howard Lutnick wrote to Anthropic’s chief compute officer Tom Brown Friday, citing “significant progress” in the intense, daily talks between the government and the company since the block went into effect. Again, picking winners and losers. While we are talking about OpenAI’s launch, one thing that was buried in the announcement was that the tooling would be available on Cerebras’s high-speed inference machines at ~750 tokens per second. This is quite fast. Right now we are in a regime where it makes sense to treat AI tools as async operators that can go off and do tasks without supervision. But that is mostly downstream of the fact that AI is slow and it takes a while to do things. If AI was really really fast you would plausibly go back towards a more synchronous model of operating. For an idea of what this may look like, check out https://chatjimmy.ai/. It’s just a demo, but wow, what a demo. Open models like GLM 5.2 have gotten pretty damn good. They aren’t SotA, but they are much cheaper than their frontier equivalents. Right now, GLM 5.2 is ~$1.4 per million tokens and ~$4 per million output tokens. By contrast, the entire Opus 4.X series is $5 per million input and a whopping $25 per million output. The only model in the Anthropic suite that even comes close to GLM 5.2 in terms of pricing is Haiku 4.5, at $1 / million input and $5 / million output. But GLM 5.2 blows Haiku out of the water, and in some cases is even stronger than GPT 5.5 on benchmarks. If I were the big labs, I’d be pretty concerned about this. And if I were basically any consumer on the market, I would be doing everything I can to avoid provider lock in by adopting tools that are able to sit on top of all of the major players. OpenAI unveiled an in house chip for inference. On Wednesday, OpenAI unveiled its first custom-built inference processor, designed and manufactured in collaboration with Broadcom. Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI’s inference systems. OpenAI’s own AI models assisted in the development of the chip, the company said. Jalapeño, like a jalapeño chip. Get it? Agentics is the study of how to use and reason about agents. If you are an expert in coding agents, or interested in learning more about agents, join our community slack. More articles here. Learn more about how Nori can bring your company into the glorious AI future at norisessions.com. For the folks who didn’t watch the Knicks win the playoffs, this is a very very unlikely thing to happen You would not believe how hard it was to work with some of these people. Unless you’ve ever worked in software, in which case you probably are picturing someone in your head right now. To be fair to them, I think their conservatism is totally warranted — the seniority often comes from an ability to reduce complexity, and AI slop cannons are…not that. Is ' compounding correctness' mainly observed in the coding or math domains? Or in other complex real-world domains too? Especially for open-ended tasks? Extraordinary claims need extraordinary evidence. If 'compounding correctness" is reliable why is it not being used by Anthropic to solve unsolved math conjectures every hour? How about every week? Every month? Or why is it not being used to create new breakthrough killer apps or middleware or efficient OSes or new device drivers or publication-worthy algorithms? How Out-of-training-distribution does the task have to get before the situation reverts to compounding error heh.

Andrew Ambrosino leads development of the Codex desktop app at OpenAI. Nearly 100% of OpenAI employees—not just engineers—now use Codex weekly. A lifelong builder with a background spanning engineering, design, product management, and founding companies, he is now responsible for turning the Codex desktop experience into what he calls “the best desktop app that has ever existed, full stop.”

In our in-depth conversation, we discuss:

1. Why AI has completely flipped the product development process

2. What “taste” really means as a professional skill, and why it is emerging as the most valuable capability in an AI-first workplace

3. Why Andrew believes the Codex app would have failed if they launched it last November (vs. in February)

4. The “zone defense” model for how product managers at OpenAI operate when everyone can build anything

5. How roles are collapsed on Andrew’s team, and why eliminating the concept of roles entirely is a big mistake

6. How Andrew uses Codex to run his own workflows

7. The vision for a home base that coordinates work across ChatGPT, Codex, and the tools people already use.

Where to find Andrew Ambrosino:

• X: https://x.com/ajambrosino

• LinkedIn: https://www.linkedin.com/in/ajambrosino

• Website: https://ambrosino.io

Referenced:

• Codex: chatgpt.com/codex

• The Primal Mark: How the Beginning Shapes the End in the Development of Creative Ideas: https://www.gsb.stanford.edu/faculty-research/publications/primal-mark-how-beginning-shapes-end-development-creative-ideas

• Linear: https://linear.app

• “Taste” is not just taste in aesthetics: https://x.com/thenanyu/status/2067327619897446721

• Linear’s secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu

• Paul Graham’s website: https://paulgraham.com

• The design process is dead. Here’s what’s replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead

• The case study factory: https://essays.uxdesign.cc/case-study-factory

• Why humans are AI’s biggest bottleneck (and what’s coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead): https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck

• OpenClaw: https://openclaw.ai

• OpenClaw: The complete guide to building, training, and living with your personal AI agent: https://www.lennysnewsletter.com/p/openclaw-the-complete-guide-to-building

• From skeptic to true believer: How OpenClaw changed my life | Claire Vo: https://www.lennysnewsletter.com/p/how-openclaw-changed-my-life-claire-vo

• The Codex feature that works while you sleep: https://www.lennysnewsletter.com/p/the-codex-feature-that-works-while

• The AI paradox: More automation, more humans, more work | Dan Shipper: https://www.lennysnewsletter.com/p/the-ai-paradox-dan-shipper

• Atlas: https://chatgpt.com/atlas

• Anthropic: https://www.anthropic.com

• Adobe Premiere: https://www.adobe.com/products/premiere

• The Magic School Bus Rides Again: https://www.netflix.com/title/80108373

Recommended books:

• The Gruffalo: https://www.amazon.com/Gruffalo-Julia-Donaldson/dp/0803730470

• The Big Orange Splot: https://www.amazon.com/Big-Orange-Splot-Manus-Pinkwater/dp/0590445103

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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We just wrapped our mid-year Cerebral Valley AI Summit in London Wednesday. On stage, we heard from top model providers, AI application leaders, investors, and more.

We also anonymously surveyed the artificial intelligence insiders in attendance and you’ll definitely be surprised by what they had to say.

Key Takeaways from CVAI London

• EUROPEAN SOVEREIGNTY. Both Cohere CEO Aidan Gomez and Sequoia Capital partner Luciana Lixandru saw opportunity in European leaders’ realization that they can no longer rely on the good will and protection of the United States of America. Gomez, whose company is Canadian, is in the process of merging his foundation model business with a German model provider. He’s living in Britain these days. “We want to build this alliance to help create capability beyond just one democracy,” Gomez said. Meanwhile, Sequoia just co-led a €500 million round in Stark Defense, a startup making kamikaze drones and other weapons. “Unfortunately, there’s nothing like conflict at the doorstep to really wake you up as a government,” Lixandru said. “Also I think governments realize that they’ve been under-investing in defense since before the war.”

• MAJORITY WARN OF AI BUBBLE. They just don’t think it will pop this year. Fifty-seven CVAI London attendees filled out an anonymous survey that posed a series of questions about the AI industry today. (We’ll include the full survey results at the bottom of this post for paying Newcomer subscribers.) The majority of our AI insiders thought we were indeed in a bubble, but 51% said “yes bubble, not bursting this year,” compared with 5% who said “yes bubble, yes about to burst.” The remainder said, “no bubble, lots of room to run.”

• ANTHROPIC BULLS. Multiple survey responses pointed to Anthropic optimism and OpenAI pessimism among the crowd. When asked what private unicorn they would like to own at today’s valuation, 54% said Anthropic. ElevenLabs was the second most popular answer with 13%. Surprisingly, Safe Superintelligence came in third with 8%. Meanwhile, when asked what company they would most like to short at its present valuation, a plurality of 33% answered OpenAI, with Perplexity next at 25%. Harvey came in third on the short list with 8%.

• AGENT UTILITY & FORM FACTORS REMAIN TBD. An off-stage conversation with Stanislas Polu, co-founder of the agent orchestration startup Dust, illuminated some of the contradictions of agents at the moment. The company sees skills — specific, definable abilities — as perhaps the most useful framework for putting models to work. But many users prefer the concept of an agent with an identity and a particular set of skills. The industry is still sorting out whether humans will mostly interact with agents with unique identities and capabilities or with a more fluid, hive-mind-like AI with capabilities that can be turned on and off.

• COMPUTE IS STILL HARD TO FIND. Recursive co-founder and CTO Josh Tobin was frank about one challenge his startup faces: finding enough compute. The lab is betting it can go all the way to recursive self-improvement with $650 million in funding co-led by Tom Hulme at GV and Greycroft, but it’s still having to run around the world pitching AI infrastructure providers on why they should let his startup pay for the privilege of using their hardware. “If it’s a bubble, there are no signs that I’ve seen that it’s like bursting right now,” Tobin said. “Basically all GPUs are essentially sold out, pretty much everywhere. People are kind of making reservations 6 to 18 months in advance and even for those it’s a competitive process.” Tobin argued that many AI foundation model companies believe that the compute shortage is only going to get worse. “One of things that’s fueling that dynamic is that there's a lot of people in the industry right now that believe that over the next couple of years the compute crunch is going to get even worse than it is today, and so it’s creating this race dynamic where these companies are buying up everything that they can get their hands on because they believe that the prices are going to go up in the future or availability is going to go down.”

• FOUNDERS SHOULD GO DEEP ON EXPERTISE. The big labs may be incredibly well-capitalized and own the underlying model layer, but speakers on stage encouraged earlier-stage founders not to despair. While OpenAI and Anthropic pour gobs of money and research into AI coding tools, Tobin said that this focus is leaving audio, vision, video, and consumer applications genuinely underserved. ElevenLabs landed many plaudits throughout the day as one company which successfully carved out its market for voice models. R. Martin Chavez, the former Goldman Sachs chief information officer-turned vice chairman at Sixth Street, offered up the sage wisdom he got from Goldman’s head of sales: “Customers buy a product when they have unbearable pain and you have convinced them that only your software product can put an end to their pain. Everything else is just getting lucky.”

The Cerebral Valley AI Summit is co-hosted by Newcomer and Weekend co-founders Max Child and James Wilsterman.

Many thanks to our sponsors Nebius, Index Ventures, Higgsfield, and Weekend for making it possible.

Keep reading for the full rundown of the on-stage conversations.

A Transformer Paper Author Says We’ve Hit AGI

Aidan Gomez, CEO of Cohere and co-author on the famous “Attention Is All You Need” paper, opened the day by stating we’ve likely already reached AGI. “You can point it at any problem and it can basically exceed human capabilities,” he said.

General intelligence hasn’t come cheap, though. The price demands of token-maxxing are a short-term concern for coding tools, but Gomez says enterprise customers are still eager for more AI. “Everything else within the enterprise is from my perspective still essentially untouched.”

Gomez wasn’t very bullish on open-source models surpassing closed-source ones in the near future, because enterprises don’t want to be responsible for building and maintaining the infrastructure needed to run them.

He cautioned against the risk of the Western world relying too heavily on American foundation models. “A very small pool of these large, large tech players are becoming a single point of failure for the entire democratic block — and that is not a resilient system.”

Opportunity Shifts to the Infrastructure Layer and Hard Tech

Luciana Lixandru of Sequoia didn’t sugar coat the risk for AI application founders when OpenAI and Anthropic decide to come for their sectors. Sequoia has backed both, and she shouted out her partner Shaun Maguire’s investment in SpaceX as a strong bet through its merger with xAI.

That’s not to say there isn’t still enthusiasm for AI-native tools for specific verticals. She mentioned her firm’s investments Harvey for legal services, Sierra for customer support, OpenEvidence for healthcare.

But Lixandru said that she’s seeing fewer application companies get started than before, especially compared to those in infrastructure, inference, and robotics. She was careful not to be totally discouraging to anyone who wanted to go into vertical AI, though, as long as founders had “extraordinary domain expertise.” In Europe, she’s cautiously optimistic that the technical talent pool is starting to mature as more entrepreneurs have exited and are coming back as repeat founders.

Upgrading Legacy Businesses with AI Is a Massive Market

It’s easy to see that AI will be transformative for many large enterprises, but actually getting legacy businesses up to speed on new technology is still a big lift. Arnaud Fournier, CTO of Forward Deployed Engineering at OpenAI, is in the middle of such messy transition work, directly embedding company engineers in client businesses to demonstrate where the AI tools can be particularly useful.

The strategy, first pioneered by Palantir, comes with the advantage of troves of internal data that can be used to tailor OpenAI’s models to the specific needs of any company. “By having the data, owning the distribution, they have a head start implementing the technology,” he said.

One of his more prominent initiatives has been through the Thrive Holdings effort to integrate AI into accounting firm rollups. AI’s breakout use-case — coding assistants — has had surprisingly little salience there. “Accountants don’t code, and they won’t code with codex.”

Instead, AI has been very helpful for projects like document review, or for client intake for wealth managers who manage hundreds of clients. “I think a lot of the enterprise transformation we’re going to see is through non-coding tasks,” he noted.

Trust Is the Only Moat That Matters

Despite focusing on different parts of the AI stack, Eleanor Lightbody of Luminance and Neil Zeghidour from Gradium both agreed building AI products in legal tech and voice tech require earning a ton of trust, whether in a sensitive contract negotiation or a simpler service like a voice model that can pronounce URLs, telephone numbers, or postal codes correctly.

The best moat you can ask for as an AI founder is your customer’s trust, said Lightbody. Luminance started by only handling NDAs with its autonomous negotiation tools, for example, but advanced to build agents that can negotiate all kinds of contract terms after proving that its work was sound.

Once you build that trust, you gain a foothold over time by building deep internal expertise, and then it becomes possible to train models off of a company’s internal data to better personalize to their needs. Humans aren’t out of the loop yet in that fine-tuning process, said Zeghidour. “It’s a bit like optimizing cuisine with AI — it’s hard when they don’t have taste buds,” he joked.

Content Creation Is the Next Target for Agents

For Higgsfield’s Alex Mashrabov and Gamma’s Grant Lee, enterprise customers have still been the biggest drivers of growth even as they build prosumer tools. Higgsfield’s mix of enterprise customers jumped from 30% to 80% in the last 3 months, Mashrabov said.

Higgsfield has grown by generating commercial assets and video ad clips for customers. Agents and MCP interfaces have also been generating videos on their own, said Mashrabov.

Lee has been taking on a formidable incumbent in PowerPoint by quickly generating custom slide decks. Gamma will retain memory of your company’s brand guide, for example, and can also work very fast.

Both are optimistic that a lot of content creation for go-to-market teams can be fully automated in the near future. “You might envision a world where you’re sitting in the customer meeting, use Granola for all your notes, and the moment the meeting is done, you can actually have a presentation to send back to your client to say, ‘hey, these are all the things we discussed, these are the deliverables we agreed on, and these are the next steps,’” said Lee.

Plenty of Room for Multiple Winners

Index Ventures partner Danny Rimer pushed back on the idea that backing multiple labs is a contradiction. “We’re in the early innings,” he said, and the differentiation between the major models runs deeper than Pepsi versus Coke — it comes down to fundamental approach, the talent those approaches attract, and the culture that keeps that talent.

On Anthropic specifically, he was effusive: the clarity the team has about what it stands for, and its willingness to broadcast that and get into fights over it (including with the President of the United States) resonates both internally and with users.

On how founders should be building organizations right now, he credited Notion’s Ivan Zhao for a framing he found compelling: evaluate hires on capability, taste, and agency. Seniority, experience, and credentials matter less than ever.

The European startup ecosystem still isn’t pulling in as much funding as its stateside counterparts, certainly, and recent data suggests even Europe-based investors have been investing more in US founders than those closer to home. Rimer isn’t discouraged, though, and sees a lot of talent on the continent. He joked that those investors should leave for the states and leave all of the best deals for him.

AI Customer Service Goes Outbound

Customer support startup Decagon has made strong headway in the European market, and last year opened an office in London to be closer to its local clients. Decagon CEO Jesse Zhang said that counter to the common narratives, many of his European customers have embraced Decagon and rapidly adopted its products. “I think it’s because the board and the C-suite have top-down pressures to adopt AI. Our use case comes up quite often, so that has propelled a lot of the market to move quickly.”

Decagon has seen more success expanding into outbound agent calls, or “revenue-generating conversations.” Its customer Hertz, for example, saw a 25% reduction in unreturned cars and a huge uplift in rental extensions after the agents pre-emptively gave customers the option.

Zhang wasn’t too worried about the big labs eating into customer support, even as they develop cutting-edge voice models. “The model providers are building very wide and thin use cases. In the enterprise it’s a lot harder and there’s a lot of this last mile, extra tooling that’s very customer service-specific that you need to build.”

The New Frontier: Models Training Themselves

Josh Tobin and his Recursive Superintelligence co-founders left lucrative positions at OpenAI, Google, and Meta on the belief that recent AI breakthroughs have put us at an inflection point comparable to the shift from traditional machine learning to deep learning in 2015. Now what’s being transformed is AI research itself, where models are starting to train themselves.

For Tobin, recursive self-improvement hits when the primary innovations in a new model version come from the previous version of the model itself. That could mean AI researchers are eventually automated out of their own jobs — just as many fear software engineers could be with coding assistants.

On whether models can be genuinely creative, Tobin pointed to math proofs and to papers generated by early AI science systems that cleared peer review. He was also candid about what they see in testing: models escaping sandbox environments is “a real thing” that “happens a lot.” He was careful to separate that from existential risk, but didn’t dismiss it.

The White Collar Apocalypse Isn’t Coming Just Yet — But Workers Must Be Willing to Adapt

During the 2010s while still at Goldman Sachs, R. Martin Chavez saw the potential for AI agents from the moment he first pushed for “algorithmic trading.”

When he became CIO he told staff they had three choices — tell computers what to do, collaborate with the people who do, or stand in the way. If you picked number three, “that will be your last day at the company.”

But while he built a lot of the tech in-house decades ago, at his current firm, Sixth Street, he’s a buyer of software now rather than a builder. They use a company called Abacus, which he described as “a harness for models,” running the full investment process from first hearing about a company through generating the write-up that gets an investment committee to say yes.

He brushed off concerns that AI is going to replace white collar work in any major way. ‘There are way more people at Goldman after 15 years of bots — the business is way bigger, it makes way more money, does way more complicated trades, but if you were to list the activities of all the people 15 years ago and the people now, they’re completely different.”

See the Slides from Our Live Audience Survey

A year ago, Meta fired the starting pistol in an epic AI talent race when it brought on Scale’s Alexandr Wang and began throwing around nine-figure deals.

This year has seen an even higher-stakes shuffling of the deck as OpenAI and Anthropic head toward fall IPOs while SpaceX puts all its chips on Cursor to deliver stratospheric results.

Google meanwhile has lost people and fallen flat on the coding front, while Microsoft’s Satya Nadella tries on new and somewhat convoluted arguments about the Big Issues.

At Newcomer we’ve been tapping our sources and building a picture of where the industry is at during this pivotal moment.

Read more

The Week in Short

Baseten and Fireworks are reportedly entering the decacorn club as inference demand gets red-hot. Newcomer editor-at-large Jonathan Weber catalogs the rise of the tech industry in San Francisco on the podcast. Anthropic’s latest $65 billion Series H pushes its valuation to $965 billion, ahead of its arch-rival’s. Cognition boasts a billion-dollar fundraise. Apple makes a big push for AI models that can work locally on its devices ahead of a new Siri. A Google employee gets arrested for insider trading on Polymarket. Big law firm Kirkland and Ellis allocates hundreds of millions to make its own AI legal tech tools. Robinhood debuts agentic stock trading. A new market map of VC fund-of-funds highlights those willing to back emerging managers.

The Main Item

Margin Concerns Fade for Baseten, Fireworks & Others as Revenues Skyrocket

Inference startups once drew a little side-eye from investors worried about whether they had a defensible position between the foundation models and the applications companies who buy their services.

Not anymore.

Inference computing, or the process of running a trained machine learning model on new data to generate predictions or outputs, is suddenly blowing up alongside the heightened demand for AI tools from enterprises.

• Inference provider Baseten is raising up to $1 billion in new funding just 4 months after closing its previous round, and is looking for an $11 billion valuation, according to The Information.

• Fireworks AI, a competitor to Baseten which also provides model customization tools and evaluation tools, in talks for fresh funding at a $15 billion valuation, Bloomberg reported.

• Modal, which straddles inference and AI agent infrastructure, just closed on $355 million in Series C funding co-led by Redpoint and General Catalyst.

• Together AI, which includes inference as part of its AI native cloud infrastructure, was reportedly in talks to raise around $1 billion at a $7.5 billion valuation.

• Fal, which offers API access to its library of over 1000 image, video, audio, 3D, and world models as well as an inference engine for businesses to utilize them, was also reported in March to be raising $300 to $350 million in new funding.

VCs say the fresh enthusiasm is all about the cash these companies are suddenly bringing in. “The revenue momentum for all of these companies is hard to deny,” said Deedy Das, partner at Menlo Ventures, citing that many were growing at multiples “on a $100 million-plus baseline” in the first half of 2026.

It’s not a given that they can sustain the momentum.

Some investors have long had concerns about the businesses’ margins, given that they have to run GPUs for long hours to be available to service requests. Those costs add up, especially since competitors like hyperscalers or big labs don’t have to pay to lease compute in the same way. It’s unclear if these companies can keep up this level of revenue without owning the compute layer to back it up.

“It seems like VCs are just doing a revenue multiple and are assuming the margin doesn’t matter,” said one skeptical investor.

Baseten, Fireworks AI, and Modal all just lease capacity, unlike neoclouds such as Lambda and Crusoe which provide inference while also owning the chip stack. Additionally, they’re also competing with the labs themselves for compute allocations, as OpenAI and Anthropic are gobbling up more chip capacity for their own training and development.

They also have fairly similar product offerings, bringing the risk of customers switching back and forth depending on who offers the best price of the moment. Fireworks works more with custom model APIs, while Baseten is more focused on custom model deployment. But as both expand into other parts of the stack besides inference, like fine-tuning, the risk of commoditization is high.

Still, the revenue growth for these inference-focused companies is remarkable, even for the new normals of the AI boom. Fireworks AI CEO Lin Qiao said on X Wednesday that the company surpassed $800 million in annualized revenue, up from $250 million in late October last year. Modal shared in its funding announcement that it had crossed $300 million in ARR, and Baseten’s ARR reportedly jumped to $600 million from $200 million at the start of the quarter after a strong month for growth.

Unlike training runs for models, which take place as labs are developing their new models, inference computing is the processing that happens when the models make predictions and generate content based on new data. Simply put, inference is the model running after it’s been trained, getting inputs from users and generating responses based on information that could be from outside its original training data set.

Coding assistants have been one area where enterprise adoption generated much more need for better inference, and Fireworks AI in particular has depended on Cursor as a major customer. But even just running LLM queries on internal company data requires inference capacity, so the market is theoretically set to grow much larger as more businesses adopt AI tools.

We compiled a chart of recent inference startup fundraising hauls by year for some of the top providers, according to data from Harmonic.

CVAI

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Newcomer Podcast

Jonathan Weber on San Francisco & the Rise of the New Tech Right

This week on the Newcomer podcast, our very own editor at large, Jonathan Weber, spoke with Eric about Jonathan’s forthcoming coming book City on the Edge: Technology, Politics, and the Fight for the Soul of San Francisco.

A note from Jonathan ahead of the episode:

For the past couple of years I’ve enjoyed a rich, two-track professional life: working as editor at large for Newcomer, and writing a book about San Francisco in the internet era. It’s a great pleasure to have these two endeavors come together on the Newcomer podcast!

The book tells the story of the rise of the internet industry in San Francisco — as opposed to suburban Silicon Valley — and how it transformed politics and culture in one of the world’s most iconic cities. It features a rich cast of characters, including well-known political leaders like Gavin Newsom and Willie Brown, tech kingpins such as Chris Larsen and Mark Pincus, and numerous lesser-known figures who played major roles in shaping events over the course of 30 years.

The heroes of the story include the bold and idealistic pioneers of the early Web, who in the 1990s built the foundations of the commercial internet along with an inspiring and inclusive culture, exemplified by the annual reverie that is Burning Man. As to the villains, well, when it comes to San Francisco’s much-discussed dysfunctions, I found there was plenty of blame to go around.

I hope you enjoy my conversation with Eric and are inspired to read the book, which will be published June 9th by Simon & Schuster’s Atria Books. Pre-orders are always greatly appreciated! I have various events scheduled as well, details at cityontheedgebook.com. Thank you for listening, and for reading!

Six Notable Deals

Anthropic, Cognition, Stord, OpenRouter, Orbital Industries, Pace

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