1. They're low stakes to get wrong.
2. The most common is MCPs or similar ai-tooling.
3. Making them look good takes time and effort still. It's a multiplier, not a replacement.
4. Quality and maintainability require investment. I had to restart an agentic project several times because it painted itself into a corner.
It’s an absolute game changer, and it can now multiply your productivity fivefold if it’s a solo greenfield project.
Maybe half a year ago it was as you said. You had to wait for the agent to finish, you had to review carefully, and often the result was not that great. You did not save a lot of time.
Now I can spin up 3+ parallel conversations in Codex, each in a git worktree. My work is mainly QA testing the features, refining the behavior, and sometimes making architectural decisions.
The results are now undeniable. In the past I could not have developed a product of that scope in my free time.
That is what is possible today. I suspect many engineers have not yet tried things that became feasible over the last months. Like parallel agents, resolving merge conflicts, separating out functionality from a large branch into proper PRs.
I have heard this statement every single day for 2 years and yet we still have no companies compressing 10 years into 1 year thus exploding past all the incumbents who don't "get it".
> if it’s a solo greenfield project
which is a pretty large caveat. Anecdotally, I've found my side projects (which are solo greenfield projects, and don't need to be supported to the same standards as enterprise software) have gained the boost the GP was talking about.
At work, it's different, since design, review, and maintenance is much more onerous.
The first line of code was written on November 25th. It achieved adoption in the "personal agents" space that far exceeded the other companies that had tried the same thing.
(Whether or not you trust the quality of the software you can't deny the impact it had in such a short time. It defined a new category of software.)
Like, look at e.g. YC minus the AI and AI ajacent companies. Are those startups meaningfully more impressive or feature-rich as compared to a couple years ago?
I expect we will start seeing the impact of the new coding agent enhanced development processes over the next few months.
If agents could really compress 10 years of development into 1 year, you'd see people making e.g. HFT platforms and becoming obscenely rich, not making a fun open-source project and getting hired by OpenAI as an employee.
https://tools.simonwillison.net/github-repo-stats?repo=OpenC...
I meant a month for the initial release, not current state.
Regardless, much like lines of code, number of commits is not a good metric, not even as a proxy, for how much "work" was actually done. Quickly browsing there are plenty[0] of[1] really[2] small[3] commits[4]. Agentic coding naturally optimizes for small commits because that's what the process is meant to do, but it doesn't mean that more work is being done, or that the work is effective. If anything, looking at the changelog[5] OpenClaw feels like a directionless dumpster fire right now. I would expect a lot more from a project if it had multiple people working on it for 5 years, pre-AI.
[0] https://github.com/openclaw/openclaw/commit/e43ae8e8cd1ffc07...
[1] https://github.com/openclaw/openclaw/commit/377c69773f0a1b8e...
[2] https://github.com/openclaw/openclaw/commit/ffafa9008da249a0...
[3] https://github.com/openclaw/openclaw/commit/506b0bbaad312454...
[4] https://github.com/openclaw/openclaw/commit/512f777099eb19df...
[5] https://github.com/openclaw/openclaw/blob/main/CHANGELOG.md
> (Whether or not you trust the quality of the software you can't deny the impact it had in such a short time. It defined a new category of software.)
I brought up OpenClaw here because the challenge was:
> we still have no companies compressing 10 years into 1 year thus exploding past all the incumbents who don't "get it".
I don't know anything about the code quality of OpenClaw, but telling me the number of commits tells me precisely nothing of use.
If that were true, all of these anti-AI greybeards who have been in the game for 30 years would all own their own jets.
Which is exactly why you can't use it as an example, there is no control. This is basic stuff.
https://www.reuters.com/technology/openclaw-enthusiasm-grips...
Cryptocurrencies? Barely any other use than money laundering, buying drugs and betting on the outcome of battles in war. And NFTs? No use at all other than money laundering and setting money ablaze.
It's like I never wrote them, because I didn't. I've got the gist of them, but it's the same way I get the gist of something like Numpy: I know how it works theoretically, but certainly not specifically enough to jump in and write some working Fortran that fixes bugs or adds features.
I now have a bunch of stalled projects I'm not very familiar with. I no longer do solo green field projects that way.
Why do I not see 5x as many interesting greenfield projects than before?
That's a big if. I don't have numbers but most professional engineers are not working on such projects
The degenerate side is clueless upper management and fad-driven engineering. We have talked extensively about this.
There is a more rational side to it that I've seen in my org: some engineers absolutely refuse to use AI and as a consequence they are now, clearly and objectively, much less productive than other engineers. The thing is, you still need to learn how to use the tool, so a nontrivial percentage of obstinate engineers need to be driven to use this in the same way that some developers have refused to use Docker or k8s or whatever.
Perhaps these “obstinate” engineers have good reason in their decision. And it should be their decision!
To be so confident in what is “the right way (TM)” and try to force it onto others is... revealing.
Sounds like a human? The ‘statistical’ part is arguable, I suppose.
I'm sure I will have no problem whatsoever remaining in the employ of a firm that trusts me to make products and tooling that still push the envelope of what's possible without having to resort to the sheer brute force of trillion parameter-scale models.
After 18 months the hard evidence is in place. And much like replacing bare-metal servers for many use cases where evidence shows that the burden of k8s or the substitution of shell scripts for Terraform, it's time to move on.
I don't really see a place for no AI usage in line-of-business software apps anymore.