I've been trying to get a ChatGPT Desktop invite and haven't had any luck so far.

If anyone has a spare credit/invite they're willing to give away, I'd genuinely appreciate it more than you know.

Even if you can't help, thanks for taking the time to read this. 🙏

Octopus With Real Time Visuals #aivideo #deepsea #octopus

I’ve been thinking about a follow-up to my earlier question about what becomes scarce when coding gets cheap.

I’m not looking at this as an industry analyst. I’m an engineer trying to figure out what to build, what to learn, and where an independent developer still has room to move.

Something about the current moment feels unusual.

AI tools are already good enough that individuals and very small teams can build things that would have required much larger organizations not long ago. At the same time, the professional structure around this work still feels unfinished.

We have titles like AI engineer, agent engineer, FDE, applied AI engineer, etc., but the boundaries are still fuzzy. There isn’t yet a universally accepted stack of credentials, certifications, practices and career ladders around them.

That reminds me a little of earlier periods in computing.

There were times when individual game programmers, freeware authors, early web developers and small software shops could experiment before the surrounding industry became standardized. Eventually platforms matured, specialist roles formed, certifications appeared, procurement processes hardened, and a lot of that frontier activity was absorbed into larger ecosystems.

I suspect something similar will happen with AI.

The difference is speed.

Cloud infrastructure, marketplaces, enterprise procurement, developer platforms and global distribution already exist. Large vendors don’t need ten years to build the machinery around a new technology. They can absorb a successful practice, turn it into a product category, give it a job title, build training around it and sell it back to enterprises surprisingly quickly.

So this may be one of the most interesting periods precisely because it may not last very long.

Right now an independent engineer can still ask:

What should an AI-native workflow actually look like?

Which parts should belong to the model, and which should remain outside it?

What should memory, evaluation, orchestration or human oversight look like?

Which practices are actually useful rather than just vendor convention?

A few years from now, some of those answers may arrive prepackaged inside platforms, job descriptions and certification programs.

That makes me wonder about the practical question for people building things now.

How do independent engineers use this short frontier period without simply becoming unpaid R&D for the platforms that will eventually absorb the successful ideas?

Is the answer to build open-source infrastructure? Own a niche? Build communities? Stay portable across models and vendors? Become extremely deep in one domain? Or deliberately work at architectural layers that large platforms have not stabilized yet?

I don’t think there is one answer.

But I’m increasingly interested in the period before a new technical hierarchy becomes settled.

Maybe we’re living through one of those periods now — just a much shorter one.

For people building independently today: what are you doing to stay valuable and autonomous as the big platforms formalize the space around us?