Slop is probably more accurate than boring. LLM assisted development enables output and speed. In the right hands, it can really bring improvements to code quality or execution. In the wrong hands, you get slop.

Otherwise, AI definitely impacts learning and thinking. See Anthropic's own paper: https://www.anthropic.com/research/AI-assistance-coding-skil...

Here's a founder/product perspective. This maps well to the skateboard => scooter => bicycle => motorcycle => rocket ship product metaphor that's often used. Each phase teaches different design patterns, constraints, and failure (and success) modes for different inflection points of a startup's journey.

But here's the reality. What got you technically to PMF may hold you back from your Series A and next steps. Technical debt is just the natural cost of growth, but (here's the kicker) optimizing tech stacks too early can lead to slower execution time. Most startups never reach exponential scale anyways. Put another way, starting with "rocket ship" does not immune the startup from rewrites, refactoring or throw away code.

The real systems and management challenge is building architectures that are intentionally temporary or modular. Simple enough that throwing them away later isn’t traumatic and rebuilds aren’t a sign of failure but success.

Honestly, you might want to step outside tech altogether. Join a local civic or neighborhood organization or volunteer with a nonprofit. There was a nice thread last year about libraries.

Channeling Steve Blank, get out of the building! You’ll run into real problems faced by real people who often have limited exposure to both AI and tech, but who can still benefit enormously. Listening and engaging is always a good first step before jumping in to suggestions.

In this space, needs are far more data and visualization driven, which are not strictly AI related. It may also be both a useful and humbling antidote to hype cycles.

Go read The Founder's Dilemma by Wasserman. It's great and covers almost any problem a founder will run into. To really summarize, it's all about trade offs and prioritization. Patents vs trade secrets fits nicely.

Trade secrets are far cheaper and easier to maintain than patents. In short, patents are only as strong as your ability to enforce them. Also Alice Corp. v. CLS Bank International (2014) weakened software and process patents. That said, if you can’t realistically defend IP in court, you effectively don’t have it. From an early-stage founder perspective, that makes patents a questionable use of time and money and potentially what kills the company.

This may contrast from information you get from a lawyer or VC. Patents are attractive because they create an asset someone else can later buy or defend. For the founder, the incentives aren’t squarely aligned.

Neither approach is more right or wrong, but there are very real practical consequences. If you are pre-seed who is bootstrapped or done a family & friends round and are pre or early revenue, trade secrecy is by far your better option.

As an additional note, if you don't own the underlying AI models and are just a better wrapper for Claude or ChatGPT you at best have a very weak IP or patent position.

Agreed, Goodhart’s Law captures the failure mode well intentioned KPIs and OKRs may miss, let alone agentic automation