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 Daxโs 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 OpenCodeโs business model
36:33 Why inference is profitable
39:11 GPU bottlenecks
40:54 AI hype
45:50 AI spending
48:47 Daxโs memo
55:41 Daxโs 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 Daxโs work setup
1:12:35 The role of engineers and EMs
1:15:50 Advice for engineers
1:18:12 Book recommendation
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Three interesting thoughts from Dax:
1. No AI-native coding agent company is โwinningโ by being better with AI.
Dax says that none of OpenCodeโs 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 โ unless you change incentives!
Dax says the natural way for software engineers to โcash outโ 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. Thereโs nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output.
3. AI code generation mutes the โguiltโ 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 youโd often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devsโ judgment and results in less tech debt being cleaned up.