I'm the creator of a newsletter using AI to make the science behind a rare disease, Kabuki Syndrome, more accessible.
In this blog post I walk through the steps I took to translate scientific papers into sparkling prose that maintains tight links between generated sentences and the passages they're based on, and touch on some of the broader issues related to what an editor is in a publication that is AI-generated, and why I think rare disease reporting is a uniquely appropriate and ethical use of AI!
Philip Ball helped me understand this in his book "How Life Works." He makes the point that you can't understand language by understanding letters. Walking up to someone and just saying "L" won't have any effect. And given just an understanding of the alphabet, you would never be able to use that on its own to understand a whole book, you have to move up. The whole has its own kind of existence. I think about how hard it is for one individual to influence a culture. You kind of have to speak and operate at the highest level to change things. I know that's super vague but those are just the terms I've been thinking about it in.
yes it all comes back to iteration, the original "vibe coding". for me, programming has always been about making it up as i go along. like an artist starts with one stroke, i started when i was 10 years old typing '10 print "hello, world" 20 goto 10' and i've never really stopped programming that way 47 years later. For me programming is the same as refactoring, they both happen in a continuous Zone throughout the day. The idea of spending this big period at the beginning Defining the Architecture then letting AI fill in the blanks makes no sense because I only know what the architecture is, what the product is, as part of a process of typing all day for days and weeks and months, that never ends.
I've been reading "Elusive Cures" by Nicole Rust, about the failure of neuroscience to cure major ailments like schizophrenia and alzheimers, despite decades of work modeling neurons and other brain systems (costing far in excess of $500M).
Here are some thoughts that that book sparks ...
"the whole is other than the sum of its parts" - someone, based on Aristotle. The way Nicole Rust puts is that the whole->part relationship is one way. In other words, you can determine the parts from the whole, but not the whole from the parts. A cell is a complex dynamic system with many overlapping and interacting feedback effects and diverse homeostatic drives. Its state emerges into its own entity that, once formed, bears only a tenuous relationship to its parts.
Understanding of our bodies and minds may be more tractable at more common levels, levels where the life system is at (whole), not where it was (parts) is where I think she's going: language, art, kinship, etc. But I'm not done yet.
> the OS layer will "see" everything and be able to ingest everything you do into a context
that makes sense, i could see it being useful. though it would be a feature easily replicated by Apple or Android if it got some traction.
Maybe this idea of apps being "replaced by agents" is less literal than I'm thinking.
Like with music, maybe it could be an agent that in general helps me be a cooler, more informed music listener by constantly thinking about new ways of accessing myriad sources of info about what's cool, or what I would think would be cool.
it may be hard to measure, but it's definitely helping everyone who uses it to be more productive in certain aspects of their work. that's clear to me.
but it might be, say, 20% more productive in 20% of your workday, or 1000% more productive 2 days per month (the days when that perfect dream spec hits your desk that you can just paste into claude and get a slick working system back), which works out to just 4% more productive overall, or whatever, which is hard to measure with all the noise.
in the end companies will pay for these tools because their employees will be demanding them, same as they demand other things that make their workday more pleasant - email, coffee, air conditioning, a conveniently located office, etc.
that said, I see the intelligence itself being rapidly commodified/free. the companies that extract rent in the sector will be the ones that effectively bundle and sell corporate-friendly features with the core intelligence - compliance, tracking, productivity, systems integration, authentication, etc. etc. etc. Which is a competency companies like Salesforce, Microsoft, Google already possess, so they are likely to win. Plus a weird Euro variant of course.
| iterate on their product faster.
i've run into a situation where it really slowed iteration down, because i wasn't able at some point to explain in english what I wanted it to do and had to go into the code, which, lo and behold, I didn't understand. Ended up scrapping everything that had been generated and starting over by hand!
or democracy/civilization attack. DHS/Putin siccing this kind of thing on democratic voter rolls around Nov. elections could be the least of it.
i could see a long tail of impenetrable chaos as private correspondence gets hacked, ppl get divorced, fired, fight back, flood the zone with their own reputationslop so they have a grounds for denial, decide to take it ALL down to distract. recursive waves of tyranny/chaos. this isnt the singularity we were promised!
yes - i've thought that could work. returning to a more protected object oriented programming model (with hard-defined interfaces) could be a way - "make these changes but restrict yourself to this object" etc.
Let's take that vibe coded product and iterate what it gave you 100 times, as you tweak it to fit your vision. When you do that 101st iteration, can you prevent it from breaking something else, or changing it in a way you don't like it?
What if it doesn't understand what you're asking it to do and keeps failing and you have to keep rolling back? Can you understand the 20,000 lines it's generated so you can make the change yourself without tearing your hair out? Can you fix bugs in it that it can't, without starting from zero and having to understand the whole codebase?
with these posts I always wonder, what happens when this code runs into a customer? Or 1000 customers, or a million? All with their own divergent needs year over year.
I have just gotten off 3 years as a developer for that kind of project, and I used the best AI tools diligently every day. It often saved me time. Like from some small drudgery of half day of flailing about in config land. Or it could generate some nice rails controllers and a javascript front end from a well-written spec. writing tests was also a strong suit.
but just as often it failed to understand the depth of the product and its myriad concerns and led me down the garden path, reducing my efficiency.
Aside from that, a large part of my job was the parts that weren't coding - wrestling with specs that were far from ready for primetim, chaotic internal processes, deployment, internal coordination/communcation, talking to customers, etc.
In the end it seemed like it saved me maybe 20% of my time overall. Nothing to sneeze at.
I get that greenfield apps that have no customer contact can be created with a phrase now. That's pretty amazing. But I would love to see Opus 4.6 up against a real beast of a codebase that you're far from a master of.
i wonder if the skills will divide a bit. That there will be those who still program by hand - and this will be a needed skill, though AI will be a part of their daily toolset to a greater or lesser degree.
Then there will be the AI wranglers who act almost like DevOps engineers for the AI - producing software in a different way ...
don't see the harm in this? isn't this already the case for 99.9% of phoneline havers already?