business

taken together, it appears that for most domains LLMs will continue to look like a cracked intern: quick and effective in the hands of an adult but not given run of the place. most firms will not be able to adopt fully autonomous AI, not for problems of skill issue or lagging technology diffusion but rather for structural reasons seemingly endemic to current architectures.

If you're new to Iroh, my mental model is roughly "Tailscale at the application layer instead of the network layer".
If your question is, "why not just use Tailscale?", look at it from an app developer's perspective. If you want to release an app and have instances of your app be able to easily connect to each other, you could theoretically embeded Tailscale functionality into your app, but then the users of your app need Tailscale accounts, and your app is dependent on Tailscale.
Iroh lets you embed this functionality directly, and provides public fallback relays. If your app gets too big for the public relays, using your own relays is the flip of a switch.

Drawing on telemetry from over 10,000 developers across 1,255 teams, Faros’ recent landmark research report confirms:
Developers using AI are writing more code and completing more tasks
Developers using AI are parallelizing more workstreams
AI-augmented code is getting bigger and buggier, and shifting the bottleneck to review
Any correlation between AI adoption and key performance metrics evaporates at the company level

The core insight is this: AI agents don’t save you time by finishing your work. They expose how much work was always possible but never attempted. Five constraints now govern how much of that backlog any organization can actually capture: judgment, planning, coordination, evaluation, and absorption. Understanding those five constraints tells you exactly which new roles are being created and why.

Every SaaS business will become a harness around a model, whether or not they’ve realized it yet.

If this continues, there’s a world where 3rd party harnesses become less valuable when used with frontier lab models because the 1st party harness behavior is already baked in. And there’s no longer a fine tuning escape hatch to generalize this behavior away.

It’s going to be funny if the network effects the AI labs have been searching for end up looking just like the network effects of the SaaS era. Coding harnesses, managing the environments around the agent, look a whole lot like the SaaS platforms of old.