Great talk by @davispalmie on how @FactoryAI enables enterprises to build their own sovereign software factory: a self-improving autonomous system that translates input signals into production code, with everything managed within your perimeter.
Factory focuses on delivering the highest-leverage outcomes from your investment. Metrics we track include signal-to-production time, median time to repair, and cost per PR.
This allows leadership to measure tangible results while engineers experience the dramatic improvements their software factory delivers.
The @FactoryAI founder mafia is just getting started…
Thrilled to announce our acquisition of @Mentlio (YC S26) and to have the super talented @ashankshah_x@developerboi joining us to build the most intelligent and token-efficient agent systems at scale
Factory is a product company. In a year when a lot of smart money is betting on neoservices that is starting to sound contrarian.
The AI wave makes it tempting to sell outcomes and deliver them with people. We think the bigger opportunity is software that gets better every time it's used. That's what we build for our customers, and it's how we run Factory.
The products we build should help our customers build their own capacity to ship better software, faster. We think of the product as everything from the first time you hear about Factory through your Nth renewal.
Building the right product starts with the old advice: listen to your customers' problems, not their solutions. In 2023, many people believed we should have built autocomplete. We understood that we instead had to build toward self-improving software and software factories, since that is what would come next.
That is our product goal: self-improving software, which continuously improves from the data feedback loop of its users. Self-improving software doesn't happen on its own. You have to be data-backed and optimize the signal → deploy loop. That requires a software factory operating continuously and evolving alongside the software it produces.
The factory also has to know what good looks like. It has to help the organization extract its tacit knowledge and embed it into the software that operates the business. Taste is not sufficient. You must enforce the unique decisions that define your product. And it doesn't stop at the product. The entire company is going to be defined by software that listens to the rhythm of the business and enforces it. The clear goal of a leader is to ensure the quality and correctness of these systems.
We run Factory this way. Our unique product and technical decisions are codified as customizations to our harness, automations, CI/CD, lint rules, etc. Factory agents sit in our shared Slack channels, where they build context and execute tactical work on demand.
It also changes how we organize. Your Engineering, Product, and Design team will ship your org chart, so you must design an organization where incentives are maximally aligned. Our pod structure maps directly to our product taxonomy, which gives each pod deep expertise and agency. We work carefully to make sure the product feels cohesive and not like multiple disconnected teams. This is not easy, and we still haven't perfected it. We keep the minimal number of layers between leaders and the organizational lines. 1:25 is not unreasonable for a true people manager.
Our product operating system is itself self-improving software. It aggregates feedback, tracks launches across the team, and organizes our work. With it, our Head of Product @TaylorTheSavage runs a product surface that would otherwise require an army of PMs with just a handful of high-ownership, high-leverage ones.
When an org is designed this way, its impact scales with software, not headcount. That is the real difference between a product company and a services company. Selling outcomes is right. Delivering them with people is services. Neoservices arbitrage the gap between how fast the technology moves and how fast buyers adopt it, and some will become large businesses. But that gap closes every time a product company sells the same buyer a better long-term solution. The market already knows how to value services: Infosys does about $20B in revenue and trades at roughly 2x, not 50x, and it's down about 40% this year.
Being a product company is harder. @danlovesproofs once told me something I very much agree with. Product companies are like movie studios. You have to keep producing blockbusters, and that helps you build talent, brand, and resources. Gone are the days when you could coast on one hit. Do that, and you'll be acquired by Bending Spoons at a fraction of your value. The war is always being waged, and no winner is ever truly ahead by much. And demand for the movies is up.
That's true even against the model labs. There is a value gap between the models and outcomes for users. Anyone can close that gap, and there's no reason to believe any company has an automatic right to win the products that matter most. If anything, access to the full pareto frontier of cost and quality gives model-independent, product-focused companies a focus, cost, and quality advantage.
If someone asked me what to build today, I'd say go build a product company. It is harder, but the biggest outcomes are only possible through product.
Proud to back Factory as they build the system that automates the entire software lifecycle: planning, review, testing, security, documentation, all of it.
Coding has gone from $550M to $30B+ in two years, but generation was only one piece. Factory closes the rest of the loop with a shared, governed, model-agnostic platform.
Excited to partner with @matanSF and @EnoReyes, and to witness their talent and vision firsthand.
Announcing our new and improved Analytics, giving engineering leaders transparency into consumption, model efficiency, and adoption, broken down by model and by user across every session in the organization.
Describe a recurring workflow, pick a schedule or event trigger, and Droid runs it to the intended result. You choose the model, the machine, and Connectors for each automation.
Factory is a launch partner for the OpenAI B2B Marketplace.
Eligible @OpenAI enterprise customers can now apply part of their existing OpenAI commitment toward Factory, driving engineering efficiency and accelerating software development with autonomous AI agents.
Sonnet 5.5 is live in Factory. Some initial observations:
- High is a strong default - Checks that the real requirement is met, not just the nearest failing test - Questions explanations carried over from earlier work
How does self-improving software work? Listen to our CTO @EnoReyes talk about building the machine that builds the software powering thousands of developers globally.
Frontier models have blind spots for legacy code, what’s benchmarked is improved. Legacy-Bench tests how well AI models can debug, extend, and migrate historic systems, to drive the future of software.
Opus 5.5 is live in Factory. Some initial observations:
/ Medium is a strong default / 20–25% fewer output tokens than @AnthropicAI Opus 5 at the same effort / Clear, actionable answers on long investigations