@thdxr I think there's a lot we can learn from the new book from Eric Ries: Incorruptible
Instead of taking a stake in these companies, how about taking and shooting the ideas from that book?
It serves the whole world.
I've building software professionally for more than 25 years but I can safely say NOTHING compares to the exhilaration of building software with AI and watching it unfold in front of my eyes way better than anything I could ever build myself
We see that AI amplifies good engineering cultures, and bad.
So it is rational to better the engineering culture at any and all organizations. Good (engineering) middle management is pretty good at doing this.
So why are so many companies gutting middle management, and now?
@charliermarsh Sometimes they're a good reason to work on something. When people say a market is "crowded," what that often means is that there's a real problem and none of the solutions are good enough yet.
Excited to share our most powerful new Claude Code feature: dynamic workflows!
Mention "workflow" in a prompt and Claude will dynamically create an orchestration plan that it strictly follows, allowing you to confidently trust that every stage happens in the right order even across 100s of agents.
OpenAI and Anthropic are effectively telling the market they can't solve every problem with a generic AI coworker.
You don't pour billions into massive forward-deployed joint ventures if you think the next model release is going to take care of it.
In the cloud supercycle, semis led and software followed (and you didn't need Qualcomm or ARM to tell you the value was migrating up the stack).
In AI, the infra layer itself is telling us the application layer is a separate, massive opportunity they can't fully capture.
a16z's @joeschmidtiv on why the app layer isn't dead: https://t.co/84QN5Mj9T3
If you work in the software industry and have time to read only one long-form post today, read this one.
If you have time to read two, read this one twice.
Highly #recommend
tl;dr: Stay off the yellow brick road that the frontier model companies are racing down. There is plenty of opportunity to solve hard problems elsewhere. Focus on areas where you can build the system of work (workflows), capture compounding, non-public data and deliver deterministic outcomes that customers need.
@HarryStebbings For SaaS founders (like myself) what would you advise? Is this the era of going ever broader or more vertical?
(Co-founder and CTO at: https://t.co/cBPMVkSzOo)
CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI.
So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have to happen to get sustainable results from agents.
“Look I made this awesome product prototype”. Yes but you didn’t have to review the code before it went into production and fix a bunch of issues.
“Look I generated a contract”. Yes but you didn’t verify all the terms before it goes out to the counterparty and didn’t have to wire up all the past contracts to work with.
The best thing you can do as a CEO is to use AI a *ton* to figure out the real implications of agents in the enterprise, and come out the other side with an appreciation for both the upside and the real work that goes into them.
Today I was part of the 22% reduced by a San Diego startup.
The business is the strongest it’s ever been. So I think it’s important to be direct about what I’m seeing and why.
First, they made this decision and they own it. I was let go because the way to operate at the highest level of productivity is changing, and to win the future, I needed to change with it.
Second, this wasn’t about cutting costs. I was told most savings from this change will flow directly back into the people who stay. Apparently they’ll be introducing million-dollar salary bands. If you create outsized impact using AI, you’ll be paid outside of traditional bands.
Unfortunately, I only had 90x impact.
And in the new world, 90x doesn’t cut it.
THE 100X ORGANIZATION
The primary change is that we’re restructuring around what they call the 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago.
Incremental improvements to my existing workflows weren’t enough. I was still looking at the PRs I merged, instead of having an army of agents reviewing them.
I was still talking to other engineers, instead of my agents talking to their agents.
Sometimes I even deleted code and features, which means my output was technically negative.
The common narrative is that AI makes everyone more productive. It doesn’t. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems.
I was one of those workflows.
THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS
— THE BUILDERS: 90X ENGINEERS
I don’t think most employees have internalized what’s actually happening with AI in engineering.
The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level, that is the farthest thing from reality.
Here’s what we validated recently at this San Diego startup: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers.
They’re not writing code. They’re directing agents that write code.
The skill is judgment.
Unfortunately, I was still occasionally using mine manually.
AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down.
Think about it. The bottlenecks are orchestration and reviewing. Everything else is leapfrogged and no longer needed.
So who do you want orchestrating and reviewing code?
Apparently not me.
The new world is about enabling your 10x engineers to become 100x.
I was only at 90x, which is basically a performance issue now.
The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don’t match the volume of code being generated.
I actually made this worse by sometimes deleting code.
Less code is technically less output.
In hindsight, this was not aligned with the 100x org.
— THE SYSTEM MANAGERS
Ironically, the people who automate their jobs with AI will always have a job.
They become owners of the AI systems. Agent managers.
I, regrettably, was still a person.
I had agents, but not enough agents. My agents had tasks, but not managers. My managers did not have agents. And my agents were not yet talking to other agents’ agents.
The underlying systems in which we operate are absolutely critical to get right. I now understand most companies are delusional to think they can iterate on existing humans and compete in this new world.
You must create enough disruption so old systems are deprecated entirely.
In this case, I was the old system.
— THE FRONT-LINERS
In a world that will become saturated with AI communication, the human touch will matter more than anything to customers.
This is a bottleneck that you shouldn’t replace.
I was not customer-facing, so unfortunately I was replaceable.
REWARDING 100X IMPACT
In a world where companies are able to do so much more with less, where does that excess money go?
In their case, much of the savings in this new operating model will flow directly back to those who enabled it.
Not me, because again, 90x.
We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can’t afford to lose them.
You can, however, afford to lose the 90x people.
Compensation bands of today should be thrown out the door. They’re introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.
I was apparently ten x short.
THE FUTURE
Nearly every company will make changes like these. The ones that do it proactively will define what comes next.
The future is not fewer people. It’s different work, new roles, and better rewards for those who embrace it.
We’re already seeing entirely new roles emerge, like Agent Managers, that didn’t exist a year ago.
And we’re seeing old roles disappear.
Like “engineer who personally reads his own PRs.”
I’ve never been more certain about where we’re headed.
We've been building Swamp as a small team of AI Maximalists, and it's been incredibly fruitful. I got to talk to @adamstac at the @changelog show about my experience roughly 4 weeks in. We've only accelerated since then. Listen: https://t.co/5m0uH1EhHs
This is the early days of working exclusively through building the machine that builds the machine. But it's getting better every day, as a (still small) industry we're getting faster at it. The future is adaptive software like swamp, openclaw, etc - combined with thoughtful software engineers designing machines to generate the software they want to see in the world.
It's fucking glorious.
Principles > Ceremony
When I wrote Learning Domain-Driven Design, I made a deliberate choice: instead of pages of code listings, I focused on the principles behind the tactical patterns. I wanted to make sure the reader understands why only one instance of an aggregate participates in a transaction, why value objects must be immutable, and the rationale behind other tactical patterns. 🧵 1/3