I’ve been thinking about this a lot through my consultancy work. I’ll jump from one call to the next, both with people who say they’re “building in crypto”, and the contrast can be pretty stark. Some are genuinely trying to build useful tech. Infrastructure. Tooling. Things that solve real problems and probably won’t get much attention for a while. Others are focused on memes and market mechanics. Attention. Velocity. Riding whatever the current narrative happens to be. And there are still people whose entire plan is just to farm. We always tell those where to shove it, though, but thankfully, most of them are filtered out before they ever get to me. What makes it tricky is that all of these people think they’re operating in the same space. They all say “crypto”. They all use the same language. But they’re often talking about completely different things. Moving between those conversations back to back really drives home how stretched the word “crypto” has become. We’re often comparing apples and pears, then acting surprised when nobody agrees on what good even looks like. At the very least, it’s changed how I listen.

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation @thdxr (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • @AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • @WorkOS – everything you need to make your app enterprise ready • @turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

Since I’m back in Ireland for Christmas I started to look through my childhood bedroom and I found all of my old Dynasty Warriors memorabilia!!! 🥰 Back the it was hard to get anything from Japan so I’m pretty sure I won everything on eBay auctions 😂