The issue with this kind of report is that you inevitably end up thinking "They would say that, would they".
There simply is no way to separate what’s reporting and what’s advertising.
There simply is no way to separate what’s reporting and what’s advertising.
If you are already an advanced company filled with skilled people, you may not see the gains that a mid-tier company filled with mediocre seat warmers would see when their "best and brightest" start adopting AI to replace said seat warmers.
An example: the UI applications suffer from massive amounts of repetitive code. People on hacker news will probably instantly jump to some kind of componentization strategy. More wise individuals probably understand you need to draw a winning strategy before that’s possible. Identify common workflows, from that, identify common components, figure out a parameterization and so on. The footguns are the slop having unintended consequences which fixing might actually cause production bugs by being “features”.
Hypothetical now: a current alternative solution which may exist is two simple detail views (of which there may be 30+ in the application) are 1000+ LoC template files, hand-coded, copy-paste, with all the errors you could imagine. You can get a lot of work from that so people are/could be content just fixing slop with slop and calling it engineering while piling on more features. Maybe it works but it doesn’t allow for much engineering room.
With AI tooling, popular support becomes less necessary - correctness matters more. Instead of galvanizing people to realize this is wrong (and it’s not just a want thing, with offshoring and the general difficulty of software engineering, many people don’t understand these concepts. They are great people but struggle understanding for loops intuitively; I don’t know what to say). Now I can provide my strategy to the tooling and do the decomposition and componentization myself. On top of this, my strategy can be more intelligent and robust because I’m not doing it by hand like concurrently using the API layer as a source of truth to give my agents more context on what the UI was trying to do. The code itself is confused so part of the work is learning the intent behind each module and also capturing bugs that are “features” to prevent regressions. Basically, a clean room rewrite (but we have access to the source code so I know that’s not the exact correct term).
The engineering moat is still there. If I say, “Claude make my UI codebase smaller” it will burn tokens and fail. One day it will succeed but right now it will fail. Likewise if I say “Claude suggest ways to make my codebase smaller”, it may suggest my strategy but it may not capture all the critical wrinkles. LLMs are not good at simplifying so to simplify (which is always a good goal in software engineering as long as the abstractions still hold), requires human strategies when complexity is high enough. Especially in my case where the behavior of the code is almost more emergent rather than engineered.
Then carrying on, hypothetically, I started to devise a system for not only rewriting the UI but the rest of the application (API and DB layer) as well as the corporate processes around the application. This may sound rude but these corporate processes can be disempowering and wasteful. The trick is inversion. First build a conceptual interface around the layer. The model will understand the purpose without understanding or rewriting the internals. This saves context and allows it to do more powerful reasoning by having perspective instead of being in a vacuum. Then when you have bandwidth, use that interface along with all the context you developed working on other modules, to take control. An example, you have a team which is responsible for building and deploying. To build and deploy, you may need to fill out forms, contact layers of people, all to achieve the end of goal of “building and deploying”. Through inversion, you communicate with an agent endpoint that handles communications to that team. Escalating, writing forms, and doing whatever is needed to get the code built and deployed. For all intents and purposes, you are now in control of that process. What used to be, for example, “I want my code built and deployed to QA” -> “message A” -> “message B” -> “write form A” -> “monitor for responses” -> “build triggered” can now be “I want my code built and deployed” -> “activate agent process” -> “build triggered”. This is an application of inversion in a nutshell. AI allows to apply engineering principles not only to our code but corporate processes where applicable.
So in this case, there could be 100+ individuals supporting this application and process. The one or two or three engineers that used to own the development process (more or less) were drowned out yet still responsible for the overall process and integrity of the software. It was an impossible task but now it seems more possible. The existential angst part comes from the all processes that were sprung up to support a reality where this all wasn’t possible.
Last, I definitely do not think the solution is one hundred engineers. If you know the show “Breaking Bad”, I actually find the reality of software engineering to be more like this now. The difference between 60, 90, 95, and even 98 percent purity is staggering. And the chemist who takes the input (money and tokens) and produces the highest quality output (codebase and processes), dominates. Just as in Breaking Bad when Walter White inherently has power, despite all other chemists and structures of authority, because his process had the highest purity output. It’s just business sense and adding more chemists does not increase purity. Output quantity is no longer gated by number of bodies so that argument goes out the window. Mentorship will be key (as in Breaking Bad) and large teams of all-high-quality producers should be put together into super high-thoroughput teams. But for regular operations, prioritize the highest quality output where the input is guided by the highest quality producer because adding more cooks will seriously degrade the output, at an exponential rate.
Consider for instance the bicycle youtuber Berm Peak, who's been using AI to reverse engineer and hack shitty ebikes. This kind of work is outside his expertise, so the relative lift he gets is massive.
Note that I specifically mentioned that I'm skeptical of claims of insane lift in workplace contexts. Several factors work against it there, organizational dysfunction not least of all, not to mention a disconnect between the people doing the work and the people for whom the work has value. People being crunched by their boss to ship some corporate portal software faster are going to make shit. You making something for yourself, to solve your own problem to your own satisfaction, needing to clear problems outside but adjacent to your proficiencies, should be able to do much better. Many people have found it to be so.
These tools excel at greenfield development, but they genuinely struggle in mature codebases. Especially when it’s a legacy system with spotty documentation.
There is no measurement. Think about how much of a difference empirical science made in such a short amount of time. Or think about how it is impossible to optimize software without measurement etc.
Also there is the AI companies’ atrocious reliability even if you trust the measurements
The company is literally trying to sell itself via an IPO ... every single word they say is advertising.
They are offering a cure to the defining contradiction of capitalism: The tension between capital wanting maximal self-reproduction and labor wanting a fair wage and a good life.
Anthropics pitch is to finally redeem capitalism by getting rid of those annoying workers and their annoying demands - that is why C-Suites are so excited about it, that is why its talked about in messianic language.
Unless we somehow manage public control of AI, in which case we could hope for a Star Trek style technological utopia.
Rosa Luxemburgs word's have never rung truer - Socialism or Barbarism!
To the poster below who thinks anthropic is in this to destroy capitalism(lmao rofl), I guess maybe if you are interested in fuedal societies run by people who currently bomb schools, have chatbots and finely tuned recommendation engines designed to make children commit suicide but only after extracting as much capital as possible beforehand, try to sway elections in foreign countries, theottle the Internet so dictators can win wars, etc. Yea sure man they are all definitely interested in making the world better for everyone by also automating one of the few middle class jobs available in a country which has no manufacturing to fall back on. Super normal timeline.