Models are no longer the bottleneck for AI automation. Domain knowledge is.
@TomBlomfield nailed it: Every company’s know-how is trapped in Slack, email, and human memory. To run on AI, businesses don't need another chatbot.
They need a Company Brain. 🧠👇
Git’s deepest innovation is not just storing commits — it is the workflow around change: branches for parallel exploration, history for traceability, issues and PRs for human collaboration, and a distributed model for global contribution.
New on our Engineering blog: Creating in-house source control.
In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.
Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re…
Data is the center of the universe; agents are ephemeral.
In the Agent era:
Software as fixed SaaS becomes redundant.
Agent dynamically synthesizes software capabilities on the fly.
Data & Context remain the only core, enduring asset.
I've actually stopped using the word "harness." I now think in terms of two things: agent runtimes and agent applications.
Agent runtimes should be very small. Their core job is to connect an LLM API to an execution environment. That environment could be a full OS or a language VM, like a JavaScript runtime. Almost all that matters here is robustness. An agent runtime must be able to keep an agent running forever. Literally forever.
Agent applications are everything else. Yes, literally every piece of software in the world.
If software isn't built for agents, they use it through computer use or browser use. These capabilities can be built into the agent runtime or provided by another agent application.
If software is agent-native, agents should be able to use it directly. The experience of an agent using that software—what we call "agent experience"—needs to be carefully designed, at least for now, to make efficient use of both time and tokens.
Another key piece is agent identity. Agent applications should recognize each agent individually, rather than just the browser or computer it's using. This allows each agent's actions to be recorded in detail and audited in real time or after the fact.
Everything we're building at @botiverse follows from this view.
Assorted things I've been mulling over regarding agents in the economy:
• Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies.
• How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be.
• In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good!
• It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.)
• A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to).
• Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big.
• Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions.
Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
Could there be a “Wikipedia for products” built on Git?
Not just a page describing what a product is today, but a living history of how it evolved — versions, iterations, design decisions, user feedback, and improvements.
As a team building at the intersection of Git + Data + AI, we have to recommend this article.
It’s one of the most worthwhile Git guides we’ve come across — not because everyone needs to become a Git expert, but because understanding Git changes how you think about building.
What if Agents could branch data and run experiments against it? @sspaeti explains here:
ssp.sh/blog/git-for-data-the…
Branch the data
→ Let Agents experiment against it
→ Test on real data
→ Compare results
→ Deploy what works
→ Roll back when needed
The key idea is not just versioning data.
It’s giving Agents a safe, reproducible environment to experiment against real data — without copying massive datasets every time.
Metadata → zero-copy → delta → full copy
Want to understand how Git For Data works in practice?
Check out this deep dive by @sspaeti comparing Git-like tools that separate metadata from data:
ssp.sh/blog/git-for-data-too…
It covers:
1. LakeFS
2. Dolt
3. Nessie
4. MotherDuck
5. Neon, Supabase, Bauplan & more
no GitDataAI😂
Branching, isolation, reproducibility and merge are becoming fundamental primitives for data infrastructure — and even more important as AI agents begin to work directly with data.
This is exactly the direction we’re exploring with Git+Data+AI.
Thanks Simon for mapping out!
You're welcome. I wrote a two-part series on the topic too, in case of interest - here is Part 1:
ssp.sh/blog/git-for-data-the…
GitData.AI retweeted
Engineers care about two things in production, says Ciro Greco from Bauplan: downstream consumers never see corrupted data, and nobody debugs failures under pressure.
His answer is git for data. No job writes to production tables directly.
Engineers care about two things in production, says Ciro Greco from Bauplan: downstream consumers never see corrupted data, and nobody debugs failures under pressure.
His answer is git for data. No job writes to production tables directly.
Replying to @sspaeti
And as AI agents increasingly work with data, this becomes even more important. Agents need a versioned and legible data foundation they can safely experiment against — not just a production database they can read from.
Replying to @sspaeti
Thanks for mapping out the growing ecosystem. This is exactly the direction we believe the data infrastructure is moving toward.
The deeper shift is making data assets behave more like code: branchable, reproducible, inspectable, mergeable, and collaborative.
如果不是公司全力押注的方向,所谓的大场内的创新项目,有时候能够获得的资源还不如一个小创业公司
同时还要面临组织内的很多协作的摩擦
这里面对创业者来说,还是有很多机会的
非常同意国宝老师的观点。
今年以来看过不少来自大厂开源的万星项目,大部分都是跟随当下潮流推出的。但是几个月过去,仅有个别项目还在持续有效地更新迭代,或是没有被后浪打死在沙滩上。
我介绍layerfs的时候,遇到过不少开发者和投资人,闭着眼睛就说,“大厂如果做同样的项目你怎么办?”或是 “这赛道项目我已经看到一万个了🤡🤡。”
最早我还觉得 “嗯嗯,说得对,我得好好思考一下这些问题。“
但是问这种问题的人遇到多了之后,心里就想,这些人是哪个老师教的,怎么问的问题,语气,沟通方式都千篇一律,脑子也锈透到相等程度,没办法接受新鲜想法。
AI时代让很多工程问题的实现变得简单,但是决定一个项目好与坏更多是开发者/创始人的品味,能看得多远,以及走下去的决心。大厂除了可以规模化地克隆,试问能符合上面的特质有几点。
ps: 和一位来自大厂的老师聊过这些问题,他说想要把软件项目做好还是去初创,neo lab或者为爱发电走开源。大厂现在能期望地就是集中资源把大模型训练好,深耕软件创新不太可能了。
AI adoption is cheap. Organizational capability is not.
The frontier isn’t “more agents.” It’s a software factory where anyone can build, the best ideas propagate, experience compounds, and the machine starts noticing what nobody thought to ask.















