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Technological progress occurs in a logical sequence. Each innovation rests on a foundation of prior discoveries, forming a dependency tree that constrains what we can develop, and when. You can't invent the telescope before discovering how to grind optical lenses, or develop electric lighting before learning how to generate electricity.

We did not design this tech tree; it arose from forces outside of our control. The evidence for this lies in two observations: first, technologies routinely emerge soon after they become possible, often discovered simultaneously by independent researchers who never heard of each other. Second, isolated societies converge on the same fundamental technologies when facing similar problems and resource constraints.

Thankfully those of us who want data ownership and agency in our web applications now don't have to wait. The AT Protocol (as in Authenticated Transfer, but also @) was ushered in by the folks at Bluesky, now with a network of over 30M people strong and increasingly spread across multiple interoperating platforms/communities like Blacksky or Tangled and so many more.

The upshot here is that when there's no platform you can trust to be real, the next best thing is a platform where you can trust everything is fake.

Is 90% of code going to be written by AI? I don’t know. What I do know is, that for me, on this project, the answer is already yes. I’m part of that growing subset of developers who are building real systems this way.

That said, none of this removes the need to actually be a good engineer. If you let the AI take over without judgment, you’ll end up with brittle systems and painful surprises (data loss, security holes, unscalable software). The tools are powerful, but they don’t absolve you of responsibility.

We are testing out systems for an extremely broad set of behaviors via ecologically valid benchmarks which ultimately tell us how well these systems can plug into ~44 distinct ‘ecological economic niches’ in the world and we are finding out they’re extremely close to plugging in as being the same as humans – and that’s just with today’s models. Soon, they’ll be better than many humans at these tasks. And what then? Nothing happens? No! Extremely strange things will happen to the economy!

It is very probable we will discover that intelligence is likewise not a foundational singular element, but a derivative compound composed of multiple cognitive elements, combined in a complex system unique to each species of mind. The result that we call intelligence emerges from many different cognitive primitives such as long-term memory, spatial awareness, logical deduction, advance planning, pattern perception, and so on. There may be dozens of them, or hundreds. We currently don’t have any idea of what these elements are. We lack a periodic table of cognition.

An LLM agent runs tools in a loop to achieve a goal. Let’s break that down...

The best AI-assisted craftsmen are often thinking about the design and arrangement of their context to get the AI to one-shot a solution. This is tricky and effortful, contrary to what the AI coding hype suggests.

If you don’t provide the necessary information in the context to do a good job, your AI will hallucinate or generate code that is not congruent with the practices of your codebase. It is especially brittle at integration points of your software system.

On the other hand, if you fill up the context with too much information, and the quality of your output degrades, because of a lack of focused attention.

If you think that you can achieve security through offering guidance to the LLM through cursor rules, then you are misguided. Cursor rules or any of those types of rules (i.e AGENTS.md) that are attached to your agentic coding harness are mere suggestions to the LLM. They are suggestions.

Right now, AIs own no responsibilities. If they get things wrong, they don’t guarantee to fix it. They take no responsibility for the trouble they may cause with their errors. In fact, this difference is currently the key difference between human employees and AI workers. The buck stops with the humans. They take responsibility for their work; you hire humans because you trust them to get the job done right. If it isn’t, they redo it, and they learn how to not make that mistake again. Not so with current AIs. This makes them hard to trust.

Every company, and probably every person, will have an AI agent that represents them inside the AI system to other AI agents. Making sure your personal rep agent has a high trust score will be part of your responsibility. It is a little bit like a credit score for AI agents. You will want a high TQ for yours. Because some AI agents won’t engage with other agents having low TQs. This is not the same thing as having a personal social score (like the Chinese are reputed to have). This is not your score, but the TQ score of your agent, which represents you to other agents. You could have a robust social score reputation, but your agent could be lousy. And vice versa.