AI has now made proprietary algorithms along with the data they generate more valuable than ever as humans create the algorithms while AI optimizes them in ways no one can predict.
Have been at it since Nov 23rd 2025 with a first commit with Opus 4.5 - it was rough at first but after about year well worth it. You can also tell where so many just get stuck in the mud due to lack of direct experience with all the engineering silos (from bare metal sys admin to QA eng silos outside of standard software engineering core) it takes to roll out a new kind of technology company while scaling a new kind of commercialization model, also managed by AI and AI native from the ground up.
This works as we've been doing it since Opus 4.5 (Nov 2025) with every user request, feature and bug. It's the future without a doubt. The feedback loops evolve and get stronger depending on how well you fine-tune or train your system to optimize on its own.
Notice the word new, novel and proprietary. Would you like to ask your AI pipeline to duplicate ours? Unless someone knows what we do to parameterize, create and optimize new, proprietary and novel algorithm sets, it remains as part of any new scientific or engineering moat associated to an AI pipeline. This also include deep knowledge domain expertise and human decision trees applied along the way.
Well said. The anti-AI sentiment is strong in a few camps on HN. Praying that AI will go away so as a software engineer, only familiar with the SDLC, you become relevant again is not a winning strategy. AI is like a soup with many bubbles, some will pop while new ones begin.
After 22,948 commits, 13,720 bugs and -2,899,391 lines eliminated, I've seen this too but a properly designed AI pipeline's ability to optimize and course correct with a little badgering supersedes any Frankenstein-like outcomes.
Nobody is going to care about how much code an AI system generates in the very near future, some even now, once everyone understands the scale at which a vector space truly operates at. Complexity is not much of an issue for an AI system.
It's because they've been in FAANG silos with near zero experience in the pre-SDLC (basic research, practical application phases) through to the post-SDLC (tech transfer, commercialization etc) with most having near zero direct experience inside actual QA teams (with QA being beneath a lot of engs). A true AI pipeline hums when this is real end to end cycle. One of the reasons for that is based on how well it manages many-to-many connections inside vector space. One more thing, most soft devs have near zero comprehension of how language models work so leveraging them to manage complexity is very foreign with most not understanding that language models apply far beyond just human language and into any sequence of data e.g. DNA/RNA or just about anything you can chop up. This kind of scale is also a new concept to most.
What really needs to be done is to stop using the term “vibe coding” for what really is similar to the “owner-builder” movement in the general contracting/construction industry. Foreman and GCs are not owner-builders. We need another name.
In 1996/7 I had a chance to use Tcl/Tk to build one of the first stock tickers on the web called DigitalTrader [1], after that we used it to build some of the first vector embeddings in 2005 for early biological language models at Lawrence Berkeley National Lab [2,3] for space biosciences. Still a fan.
How can it be if we have yet to fully define what human intellect is or how it works? Not to mention consciousness. Machine intelligence will always be different than human intelligence.
And, at the heart of AlphaFold2 is the language model, the tip of the spear in AI today. 'Language' can come in many forms e.g. a protein or amino acid sequence.
This person is forgetting the entire operation is based on space biosciences, not just space. Vector Space Biosciences presents at DeSci London March 2024 - Min: 4:27:33
https://youtu.be/fbnFEvfKRO8?t=16052