I came across a LinkedIn post by Benedict Evans, an independent tech analyst, who called the thesis that LLMs will unleash a wave of everyone-coding-their-own-tools "utterly delusional". He's right. And the reason he's right reveals something important about how Silicon Valley consistently misunderstands the real world.
Let's play out the fantasy. The CEO, frustrated by the monthly board report format, vibe-codes a custom dashboard. Her assistant, tired of the scheduling back-and-forth, spins up a bespoke appointment system. The finance team builds their own expense tracker. Marketing creates a campaign analytics tool. Every department, every team, every individual, all conjuring their own solutions to their own problems.
Sounds productive. Now multiply that by a few thousand employees. What you have isn't innovation. It's chaos.
The Infrastructure Reality
Every one of those vibe-coded tools needs data. The CEO's dashboard needs sales figures, financial projections, HR headcount. The assistant's scheduler needs calendar access, contact databases, room booking systems. Each tool requires connections to shared data platforms, internal APIs, authentication systems.
Now imagine the IT team fielding requests from hundreds of amateur developers, each needing access to production systems. How do you prioritize? Who decides which tool gets API access? What happens when the CFO's custom spreadsheet conflicts with the finance team's homegrown solution?
Enterprise software exists precisely because coordination at scale requires standardization. That need hasn't magically disappeared because anyone can now, in theory, tell an AI to build something.
The Security Nightmare
Research shows that 45 percent of AI-generated code contains security vulnerabilities, according to analysis of code produced by over 100 LLMs. The code itself isn't necessarily more vulnerable per line than human-written code. The problem is speed: the removal of bottlenecks like code review, debugging, and team-based oversight means vulnerable code reaches production before anyone can examine it.
Professional developers with security training struggle to write secure code consistently. Now picture a thousand non-technical employees deploying untested applications that touch customer data, financial systems, and company sensitive information. The attack surface doesn't just expand, it explodes.
The Automation Fallacy
The excitement around tools like Claude Cowork illustrates the confusion perfectly. These tools can organize cluttered download folders, batch-rename files, build slides, extract data from documents into spreadsheets and even analyze it. Genuinely useful capabilities that, with time, will only get better.
But this is the "calculator makes everyone a mathematician" fallacy revisited. A calculator helps with arithmetic but doesn't teach mathematical reasoning. Similarly, automating file management doesn't make someone effective at their job any more than a spell-checker makes someone a good writer.
What actually makes people successful in corporate environments? Navigating politics. Building relationships. Knowing when to push an initiative and when to wait. Reading the room. Framing messages for different audiences. Balancing competing priorities without explicit criteria. Timing.
None of this is automatable because none of it is computable. The messy, human work of organizations isn't the overhead around the "real work". It is the real work.
The Silicon Valley Pattern
The vibe-coding vision is an engineering mindset applied where it doesn't belong: treating corporate work as a series of discrete technical problems waiting to be optimized away. It's the same pattern we've seen before.
Remember Hyperloop? Musk's 2013 proposal promised to revolutionize transportation. Faster than trains, cheaper than rail, immune to weather. Transportation experts rejected it, arguing it underestimated operational and safety complexity along with costs.
A decade later, the Boring Company's Las Vegas tunnel system offers Teslas driving through small tunnels at modest speeds, still with human safety drivers who "periodically" have to intervene and take control. The system combines the inflexibility of a subway, the limited capacity of cars, and the labor costs of taxis. A decade of hype produced, essentially, an inferior subway.
The pattern repeats: take a solved problem (in this case enterprise software), declare existing solutions outdated, propose a technologically impressive alternative that ignores the unglamorous reasons those solutions exist, generate excitement, underdeliver.
Why This Matters
The "everyone codes" thesis isn't just wrong. It's a distraction from more useful questions about how AI tools can genuinely help knowledge workers. The value isn't in replacing enterprise systems with a thousand individual solutions. It's in making existing work more efficient while preserving the coordination, security, and governance that organizations actually need.
Benedict Evans is right to call this vision delusional. The sooner we move past it, the sooner we can focus on what AI actually makes possible. Which is plenty, even without the hype.