š Founder https://nitter.cf/t.co/fe4hCOgLI0 š Engineer @ETH_enā š° Ex @pixable ($26.5M exit)ā š Ex https://nitter.cf/t.co/zCX5TZ6j7s š @TV_Magical_Key š https://nitter.cf/t.co/S50lPnmJka
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This is modern software engineering. It will be taught in a few years in university CS classes. But you can just learn it yourself now
Super happy with my @gmktecdotcom nucbox.
And no, they're no paying me (unfortunately).
Just a cool product. Now running my own 24/7 software factory on it. Also: Linux rocks! Great OS for running all these coding tasks
Phil No Chill retweeted
This is the key stat. The average Fortune 500 customer of Anthropic spends less than .1% of their expenses on Anthropic.
While that number likely will eventually asymptote somewhere (token prices coming down, and other large enterprise spend areas not going away), thereās no reason it canāt be 10-50X higher than this, reaching a āmereā 1-5% of spend. Which would still be a fraction of what most startups are spending.
Thus, you can see how big of a divide there is between the frontier users of AI and the rest of the world still.
Today, only a microscopically small set of companies are actually using agents for large amounts of background tasks and inside of workflows that arenāt human prompted. This Fortune 500 spend is likely dominated by coding tasks and broad employee productivity use-cases.
But the big jump will come as these enterprises start to rewire their underlying processes for agents, which weāre now in the earliest stages of seeing. Agents processing large amounts of data in the enterprises; swarms doing coding, bio research, cyber, and other large fan out tasks; background workflows executing tasks in processes, and so on. All of these use cases generally are 100X bigger consumers of tokens, and much closer to direct ROI on the spend.
This S-1 gives you a sense of both the strength of the frontier lab business model - even amidst massive competition - and how early we are in the build out of AI.
More than half of the Fortune 500 already qualifies as a significant customer, but Anthropicās revenue from those companies averages less than 0.1% of their expenses. Its ten largest Fortune 500 customers spend more than eight times the broader cohort average while still spending less than 1% of expenses.
This
Thereāll be times where you over-guide the AI. It knows better than you and is more intelligent than you.
Thereāll be times where you under-guide the AI. It can go off-track, have the wrong biases, or miss context.
How well you manage this superpower is the #0 skill I look for.
Phil No Chill retweeted
Thereāll be times where you over-guide the AI. It knows better than you and is more intelligent than you.
Thereāll be times where you under-guide the AI. It can go off-track, have the wrong biases, or miss context.
How well you manage this superpower is the #0 skill I look for.
Phil No Chill retweeted
I donāt trade public stocks but if I did I would bet on this thesis:
As consumers adopt agents to make purchase decisions, weāll see significant growth among the *quantifiably best* products and services.
Can't believe how good this speech model sounds
We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a personā¦full of emotion, nuance, pauses, and a natural cadence.
block.xyz/dessa
How do you get rid of idle processes on a ssh host without having to check manually whether or not a process is still relevant to a task? @AnthropicAI @OpenAIDevs ?
Phil No Chill retweeted
AI will be able to compute the perfect delta of what you understand and don't, helping you progressively learn any idea and unravel any mystery.
If you so desire, you'll reach the maximum of your capability and intelligence. The ultimate education. For free.
Phil No Chill retweeted
I've myself been programming since I was 11, so 33 years and counting for me. Professionally for 29 years, since landing my first full-time software development gig at 15
The work has never been boring: bootloaders, kernels, device drivers, compilers, interpreters, virtual machines, graph databases, query engines, blockchains, tactical robotics, hundreds of libraries in dozens of languages, and many a website and app
Recently, a twenty-something friend expressed surprise that I'd managed to adapt to the new way of working, herding instead of hacking. I was somewhat taken aback by his surprise, until I realized that he couldn't really have a personal intuition of the sheer immensity of the change that already took place in computing over the past 40+ years
There haven't existed many other professions with the rate of change that software engineering has been subject to. The only constant has been change, and a substantial portion of the work has been continuous learning, every year and every day
To a first approximation, then, those of us who make it to becoming graybeards in our field have already passed through a harsh filter that weeded out anybody constitutionally ill-suited to riding the curve. And what a wild ride it's been, and that was just the prelude!
Personally, this year has seen the single largest change to my own way of working for decades, but the magnitude of that change is still less than that of the previous three decades put together. And as a fan of individual superempowerment, nothing makes me happier than gaining a more powerful lever with which to move the world
May we all aspire to Alan Kay's neuroplasticity at age 85+ and be inspired by his continuing efforts to rebuild computing from the ground up. Remember, the best way to predict the future is to create it!
Phil No Chill retweeted
The biggest misread of anyone predicting the end of software engineering was imagining that the world only needed a certain amount of software, and bringing automation to the field would reduce the need for labor.
Exactly the opposite is happening where essentially most things are actually turning into software. Because agents can use tools, work with computers, write software on the fly, and process data like humans (but far faster), we can now throw āsoftwareā at problems that never could reasonably be digitized before.
What that leads to is the role of engineering taking on greater importance in the economy, because we can now engineer far more systems and processes than were possible before.
If youāre a bank trying to do new forms of trading, engineers are often your first bet. If you are in life sciences, you expect more and more research and lab work to be engineering centric. If youāre in manufacturing, youāre trying to automate more of the supply chain, all that is engineering. More and more work is shaped like software problems.
Yes weāre not going to be coding manually in the future. But almost everything will be code, which means those that understand it are in a fantastic position.
Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
What's happened to coding will happen in the physical world once we have (more and more capable) robots.
In the future, an architect will be a able to run a solo building company. His only employees will be robots who'll build buildings 24/7 on hundreds of construction sites
Good read
Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible.
We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial:
felixrieseberg.com/how-to-beā¦
1/3 You know what one of the most important skills will be?
What to think about YOURSELF and what to outsource to agents.
Sounds trivial, but it's super hard.
What do you need to read and think about yourself and what will you have the llm read for you (and summarize).
2/3 This is not trivial.
Basically, you need to constantly decide "do I store this information in the machine or do store this in my brain?"
Each of these two storage systems has its pros and cons. Just like picking infra to build software on.