The Software Factory Is Coming
A vision of autonomous software creation, where it stands today, and the five futures that could unfold from here.
What if building software looked less like hiring a development team and more like placing an order at a factory? Not a metaphorical factory — an actual, industrialized pipeline that takes a business idea as input and delivers a working application as output.
This idea came to me recently, and I spent time stress-testing it — examining the current state of autonomous software development, identifying the blind spots, and mapping out different futures that could emerge. What follows is the result of that thinking.
The Vision
Imagine a platform where anyone — a founder, a department head, a solo entrepreneur — can describe the software application they need. In the background, a coordinated fleet of AI agents turns that description into a working product. The platform provider continuously optimizes this internal factory: reducing time-to-value, lowering token costs, improving output quality. Think of what Amazon did with logistics infrastructure, but applied to software creation.
The user wouldn’t need to understand code. They’d need to articulate what they want — and the platform would support them in doing that well. A sophistication slider would let them choose between a quick prototype and an enterprise-grade system, with pricing scaled accordingly.
If this works, it would render large swathes of the current software market irrelevant.
The Blind Spots
The vision is compelling, but the more I examined it, the more I found areas that are easy to underestimate.
Integration is the real problem
Enterprise software is roughly 80% integration — data flows, permissions, compliance, edge cases — and 20% core logic. AI can increasingly nail the 20%. The 80% requires deep contextual knowledge about existing systems that’s extraordinarily hard to feed into any platform. This might actually be the hardest part of the entire vision, not the code generation itself.
Maintenance eats creation for breakfast
Building version one is perhaps 20% of total cost of ownership. Who handles the bug at 2am? Who updates when a third-party API changes? Who manages security patches? The platform that solves perpetual maintenance agents wins bigger than the one that solves creation.
The demand-side gap
Most people cannot articulate what they want in software. The gap between “I want an app that does X” and a specification precise enough to build something useful is where most projects fail today — with human developers. AI doesn’t automatically solve this. The factory might need to be as much a requirements discovery engine as a code factory.
One possible solution: the factory doesn’t just build what you ask for, but builds three variations, lets you interact with each, learns from your reactions, and converges. That’s not agile in the traditional sense — it’s evolutionary design, where the user is the selection pressure. This could actually produce better requirements than human articulation ever could.
Trust and liability
When the AI-built app miscalculates someone’s taxes, loses medical data, or makes a bad financial decision — who’s liable? The platform? The user who specified it? Similar to the self-driving car debate, some jurisdictions will be far more open to tolerating failure than others. This will shape adoption patterns geographically.
Domain knowledge has deeper moats than code
SAP’s competitive advantage isn’t its codebase — it’s the accumulated edge cases from millions of deployments across industries and jurisdictions. German manufacturing tax compliance, Brazilian invoice regulations, Japanese inventory accounting. Could agents learn all of that? Probably. But it requires the knowledge to be accessible and structured enough to consume. It’s all a question of tokens — and models are getting better — but the timeline for fully replicating this depth is uncertain.
Where We Actually Stand — February 2026
There’s a lot of talk about multi-agent development, software factories, and autonomous coding. Cutting through both the hype and the skepticism, here’s what the data actually shows.
The benchmark trajectory
SWE-bench Verified — a benchmark of 500 real-world software engineering problems sourced from GitHub — tells a remarkable story:
| Period | Best Score | System |
|---|---|---|
| Oct 2023 | ~2% | RAG + ChatGPT 3.5 |
| Apr 2024 | ~13% | SWE-agent + GPT-4 |
| Jul 2024 | ~43% | CodeStory Aide |
| Nov 2024 | ~48% | Claude 3.5 Sonnet agents |
| Jan 2025 | ~62% | Multi-agent brute force |
| Jan 2026 | ~80% | Claude Opus 4.5 |
That’s 2% → 80% in just over two years. But there’s a critical caveat: when researchers introduced SWE-bench Pro — a harder, more realistic version with private codebases across 123 programming languages — the best models dropped back to around 46%. Each time a benchmark is conquered, the next harder one reveals how far we still have to go.
Three landmark experiments
Cursor’s autonomous browser (January 2026): Hundreds of AI agents coordinated to build a web browser from scratch. Over one week, they generated 3 million lines of code. The CEO’s verdict? “It kind of works.” Independent reviewers found CI/CD pipelines failing throughout most of the experiment. Impressive at orchestration scale; questionable on output quality.
Anthropic’s C compiler (February 2026): 16 Claude Opus 4.6 agents worked in parallel for two weeks, producing a 100,000-line Rust compiler that can build Linux 6.9 on three architectures. It achieves 99% on the GCC torture test suite. Cost: $20,000 in API fees. But the code quality falls short of expert-level, and the researcher who ran the experiment noted the compiler has “nearly reached the limits” of the model’s abilities.
"The thought of programmers deploying software they've never personally verified is a real concern. So, while this experiment excites me, it also leaves me feeling uneasy."
Vibe coding platforms (ongoing): Lovable reached $100M ARR in 8 months. Replit jumped from $10M to $100M in 9 months after launching their Agent. The demand is validated. But users consistently report the same wall: around 15–20 components, context retention degrades and AI starts creating more bugs than it fixes.
The honest assessment
What's proven: AI can generate working prototypes and simple applications from natural language. Multi-agent orchestration at scale is feasible. Demand is massive.
What's not proven: Production-quality output without human oversight. Reliable handling of complex business logic and integration. Long-term maintenance of AI-generated codebases.
The gap: We can build the demo version of the factory today. The gap between "it kind of works" and "it runs a business reliably" is where most of the actual difficulty lives.
Within the model progression of the C compiler experiment lies perhaps the most telling data point. Previous Opus 4 models could barely produce a functional compiler at all. Opus 4.5 could pass test suites but couldn’t compile real projects. Opus 4.6 builds Linux. That jump happened within one model generation. The trajectory is undeniable.
Five Possible Futures
Rather than predicting a single outcome, I think it’s more honest to map the different paths this could take. Each has different implications for who captures value and who gets disrupted.
One or a few dominant platforms emerge as the "factory" — AWS-scale but for application generation. Winner-take-most dynamics. Most current SaaS vendors either die or become domain knowledge providers feeding into these platforms. The factory concept as described in the vision, operated by a standalone platform company.
The factory never becomes a separate platform because frontier model providers build it directly into their offerings. "Build me an app" becomes as natural as "write me an email." The orchestration layer gets absorbed into the model infrastructure itself. This is where things like Claude artifacts and ChatGPT's code interpreter are already heading.
No single factory wins. Instead, we get dozens of vertical-specific factories — one for healthcare apps, one for fintech, one for logistics. Each embeds deep domain knowledge that horizontal platforms can't match. The integration problem drives this fragmentation, as each vertical has unique compliance and workflow requirements.
Large enterprises resist this future far longer than expected. Procurement, security, compliance, internal politics, and existing vendor contracts create massive inertia. AI-generated software remains mostly used for internal tools and prototypes. Incumbents survive by adding AI features rather than being replaced. The boring outcome — but don't underestimate it.
The concept of "an application" itself disappears. Instead of building discrete software, people interact with AI agents that dynamically assemble capabilities on the fly. No app to build, deploy, or maintain. The user says what they need and the agent orchestrates data, interfaces, and logic in real-time. No factory needed because there's no product to manufacture.
Path A — the standalone platform — is most likely to happen partially, as a feature of Path B or C rather than as a dominant independent platform. The factory concept is sound, but it may not be a company. It may be a capability that every major player offers.
The Logical Extension
If you follow this thinking further, the factory doesn’t just build software — it manages entire IT landscapes. “We give you the IT landscape you need and maintain it. You think about your business.” This is what cloud providers promised but never fully delivered, because the abstraction layer was still too low. If the factory can generate, deploy, integrate, and maintain — that’s the real unlock.
You’re describing something closer to Business-as-a-Service rather than Software-as-a-Service. The company doesn’t buy tools. It subscribes to outcomes.
What Cuts Across All Paths
Regardless of which future materializes, the value of understanding what a business actually needs and translating between human intent and technical capability goes up, not down. The bottleneck shifts permanently from “can we build it” to “should we build it, and what exactly should it do.”
The generation layer is racing ahead. The reliability layer — trust, verification, governance, integration — is lagging behind. Whoever closes that gap first captures the most value.
The factory is coming. The open question isn’t whether, but when — and in what shape.
