Claim◆Video · 47:53 · 2m
AI-assisted coding gives programmers an illusion of control while the actual code produced is often not truly understood by anyone, much like pulling a slot machine lever.
Jeremy Howard · Machine Learning Street Talk
Claim◆Video · 4:53 — 6:23
Generating code with AI has become a commodity and is now easy; the real, much harder challenge is producing code that meets an organization's standards, is secure, and is genuinely production-ready enough to be accepted.
Sidhant argues that while AI can write code easily, the true bottleneck in autonomous development is getting that code accepted—meeting standards, security, and production requirements. ✦ AI generated
Sidhant Pardeshi · The TWIML AI Podcast · 2026-03-10 · original ↗
starts at this moment · 4:53
Elicited by
“When you think about software development do you have a a way that you taxonomize the space and the opportunity?”
You can write a lot of code uh and code is a commodity now. Like getting AI to write code is is very easy. Getting any code is easy. Getting code that follows your standards, codes that code that is really good, uh code that is secure um code that is ready for production is a completely different story, right?
verbatim transcript · starts at 4:53
Transcript · around this moment
4:53category. And the idea here is that you hit build and out comes a PR, right? But the PR that comes out is um already tested, validated, everything works, and it's exactly how you intended it to be, right? There's there's no errors. The code is acceptable. Right? So the biggest challenge uh that we have on both sides of the spectrum is code acceptance, right? You can write a lot of code uh
5:19and code is a commodity now. Like getting AI to write code is is very easy. Getting any code is easy. Getting code that follows your standards, codes that code that is really good, uh code that is secure um code that is ready for production is a completely different story, right? Because you have uh on one hand you have these greenfield builds or like new products that you can build from
5:42scratch. Um and AI is really good at that. If you look at the demos that the labs put out, hey, I built this this uh you know, game and it looks amazing. I can't believe it. Uh but then when you put the same AI on an enterprise code base and you're supposed to work with the >> [laughter] >> it messes it up. >> more challenging. >> It's it's so way more challenging. It's
6:03an orders of magnitude high problem because the AI is dealing with so much information and so many and conditions um that causes tools to fail. So, the autonomous part of the spectrum is is is a much harder challenge because you have to simultaneously address all of these items and work for acceptance as your final metric. And so thinking back from acceptance through the agent, the AI writing some code on
6:32the other side of that uh there's got to be some specification that uh the code has to meet in order to be accepted. Are you essentially pushing all the complexity of coding into spec development? That's a that's a great point. Uh so, yes and no. Um let me explain the yes part like if you could write a spec, uh then you should write a spec, right? It's uh all
- ·Getting AI to write code is now very easy
- ·Code itself is a commodity
- ·Volume of output is no longer the bottleneck
- ·Code must meet organizational standards
- ·Security and production readiness are hard
- ·Acceptance — not generation — is the bottleneck
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