Xeromaxi

I’m starting to think software engineering won’t die, it will just expand in role responsibilities as coding shrinks. You will be a software engineer + security engineer + data engineer + AI/BI engineer all rolled into a single role. That is a minimal expectation.

AI is stripping companies down to three key elements: 1) Expert Knowledge - this is the vertical specific understanding of what to do 2) Tribal Knowledge - this is knowledge of the fuzzy grey details that are poorly document and is what creates a dispersion in performance in companies doing roughly the same things 3) Hardware and Software - this is the glue that humans use to stitch Expert and Tribal Knowledge in order to perform If 1 and 2 are documented well, it allows you to rebuild how you do work. This isn't about replacing humans - in fact I think it allows you to grow faster and hire more people because with a great understanding of what to do, it requires more and more people to express judgement. This is why we built Software Factory and its being used by many established and large enterprises.

I think it is inevitable that Anthropic and OpenAI eventually roll out $1000/mo and $10,000/mo plans and then reserve the absolute best frontier models to metered access

Genuine question. If Claude can clone your entire SaaS in 45 minutes, why would anyone pay you $29/month for it? What is your actual moat?

Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.

MetaDAO reminds me of a classic idea from Charlie Munger about cash registers. Before cash registers, clerks handled money manually and small amounts would often “go missing.” Not because people were bad, but because the system made it easy to rationalize it: small, untracked amounts, no immediate visibility, and no clear sense of being caught. Cash registers didn’t rely on better behavior. They changed the system, making theft harder and accountability automatic. That’s the same intuition behind MetaDAO. It’s not about assuming founders are bad, it’s about designing a system where misalignment is constrained and outcomes are enforced. Good systems > good intentions.

This is big... Anthropic just announced a model so powerful they won't release it to the public out of fear over the damage it will cause 😨 Claude Mythos Preview found thousands of zero-day exploits in every major operating system and web browser... The numbers are hard to believe: > $50 to find a 27-year-old bug in OpenBSD, one of the most security-hardened operating systems ever built > Under $1,000 to find AND build a fully working remote code execution exploit on FreeBSD that grants unauthenticated root access from anywhere on the internet > Under $2,000 to chain together multiple Linux kernel vulnerabilities into a complete privilege escalation exploit For context: these are the kinds of findings that previously required elite security researchers working for weeks. Anthropic engineers with no formal security training asked Mythos to find exploits overnight. They woke up to working code the next morning. The results were so impressive Anthropic assembled Apple, Google, Microsoft, Amazon, NVIDIA, and seven other organizations into Project Glasswing: A $100M defensive coalition. They're not releasing this model publicly. Instead, they're racing to patch the world's infrastructure before models like this proliferate.

POV: April 2026

One person, 2 months, $20K bootstrap, no VC, vibe-coded software. $1.8B company. We’re about to see more such 1-person billion-dollar companies. AI is compressing 5 years of building, launching, scaling a team, and fundraising into a solo high-agency CEO + 2 months.