Tag: ai agents

Categories
AI Programming Software Work

The Scarcest Thing

Garry Tan woke up at 8 a.m. after sleeping at 4. Not because he had to. Because he wanted to see what his workers had done overnight.

The workers are AI agents. Ten of them, running in parallel across three projects. And something about that sentence — wanted to see what they’d done — keeps stopping me. That’s not the language of someone using a tool. That’s the language of someone managing a team.

Tan gave a name to the state this puts him in: “cyber psychosis.” He said it as a joke. But the joke has an insight in it. He’s not describing addiction to a productivity app. He’s describing a shift in what it means to do creative work — the strange vertigo of becoming a director when you’d always been a laborer.

I’m retired. I watch this from the outside now, which is its own kind of vantage point. For most of my career, the path from idea to working product ran through people — through hiring and managing and the slow accretion of execution capacity. You had the vision or you didn’t, but either way you needed the team. The idea and the means of making it real were, structurally, separate things. The gap between them was where companies lived.

What Tan is describing is that gap closing.

The thing he built — gstack, his open-sourced Claude Code configuration — got dismissed in some quarters as “just prompts.” And it is just prompts, in the same way that a conductor’s score is just notation. The abstraction is the invention. What he encoded is a model of how a startup team thinks: the CEO who interrogates the why before a line of code gets written, the engineer who builds, the paranoid staff reviewer who looks for what breaks. Each role blocks a different failure mode. Blurring them together produces, as his documentation puts it, “a mediocre blend of all four.”

That’s an organizational insight. It has nothing to do with code.

Tan described being a “time billionaire” — not because his biological clock had slowed, but because he can now purchase machine-consciousness-hours. The bottleneck of implementation, which has governed every creative project since the beginning of creative projects, is dissolving for those who know how to direct.

The scarcest thing is shifting. It’s no longer the hours of execution. It’s the clarity of intent — knowing what you want to build and why the journey matters, before any of the workers start moving. That’s harder than it sounds. For decades, most of us could muddle through in the making of it. The act of building taught you what you were building. Now the making is cheap, and that shortcut is gone.

For someone watching from retirement, that’s not a small thing to absorb. The model I internalized over a long career — that ideas become real through sustained organizational effort, through teams and timelines and the grinding work of execution — is being revised faster than I expected. Not invalidated. Revised. The judgment still matters. The taste still matters. The why matters more than ever.

It’s just that the how has found new hands. Many of them. More than any team I ever assembled, available the moment the intent is clear enough to direct them, gone when the work is done. The constraint was always the hands. It turns out it was always the knowing.

Categories
Micropayments

The Wrong Who

I was in the room for most of the early micropayments conversations. The working-level conversations, where people were genuinely convinced they had finally solved the problem. The demos were always compelling. The unit economics made sense on a whiteboard. And then they died.

They died so many times, and in so many similar ways, that the failure started to feel like a law of nature.

Clay Shirky wrote the autopsy that most people remember: micropayments fail because every transaction requires a decision, and decisions have a cognitive cost that swamps any payment below some psychological threshold. A dollar feels like real money. A dime feels like a question you have to answer. A fraction of a cent feels like being nickeled-and-dimed at sub-human speeds. The advertising model won because it asked users to consent once, peripherally, and then never bother them again.

So I noticed something when I read the transcript of Cloudflare CEO Matthew Prince’s earnings call remarks this afternoon.

He’s predicting that the internet’s business model — advertising and subscriptions, the twin structures that have governed everything since the late nineties — is about to change. He thinks some part of what replaces it will be micropayments for agentic traffic. Fractions of pennies. Fractions of fractions. At volumes that dwarf anything existing financial infrastructure can handle.

My first instinct was the old skepticism. We’ve been here before.

But I kept reading, and I think something is actually different this time. And the difference is the one thing all the earlier schemes never had.

The payer isn’t human.

This sounds obvious once you say it, but it collapses most of the objections that killed every prior attempt. Cognitive load isn’t a factor when there’s no cognition happening. Decision fatigue doesn’t apply to a process with no feelings about fatigue. The agent making the request doesn’t hesitate at a fraction of a penny, doesn’t resent the transaction, doesn’t abandon the session because it’s annoyed at being charged.

All the early micropayments architectures were built on an implicit assumption: that humans could be trained to behave like rational microeconomic actors at browsing speed. They can’t. Nobody does. But agents are rational microeconomic actors by design. That’s not a metaphor — it’s literally what they are.

The schemes we watched fail in the early 2000s weren’t wrong about the destination. They were wrong about the who. The internet of human readers and human attention was never a natural fit for per-transaction pricing. The internet of autonomous agents — making API calls, scraping data, assembling answers from dozens of sources in a single second — is a different thing entirely. And it’s arriving faster than most people realize.

Prince mentioned that Cloudflare thinks non-human traffic will surpass human traffic somewhere around 2027. That number stopped me. We are, apparently, closer to a majority-machine internet than to the one we think we’re living in.

The hard part isn’t the concept anymore. It’s the infrastructure. Prince was candid about this: the transaction volumes the industry gets excited about — a million per second — aren’t remotely sufficient for what’s actually coming. Cloudflare needs something an order of magnitude larger, and they’re looking for partners because nothing that fits the spec exists yet.

This is where it gets interesting for those of us who watched the earlier rounds. The original micropayments failures were partly psychological, but they were also partly infrastructural — the payment rails of the early internet weren’t built for high-frequency small transactions either. What’s different now is that the need is undeniable and imminent in a way it never quite was before. The traffic is real. The scale is measurable. The pressure to figure this out is coming from something other than optimism.

I don’t know what the solution looks like. Probably not one thing. Prince doesn’t know either — he said as much. Crypto infrastructure is an obvious candidate for parts of it, though crypto’s history of promising to solve problems and then creating different ones deserves some respect. Whatever emerges will probably be unrecognizable from here.

What I keep coming back to is the simpler observation. We were right that micropayments were the future. We just imagined the wrong future, populated by the wrong kind of payer.

The agents were always going to solve this. We just had to wait for the them to arrive.

Categories
AI Programming Prompt Engineering Software Work

The Great Inversion

For twenty years, the “Developer Experience” was a war against distraction. We treated the engineer’s focus like a fragile glass sculpture. The goal was simple: maximize the number of minutes a human spent with their fingers on a keyboard.

But as Michael Bloch (@michaelxbloch) recently pointed out, that playbook is officially obsolete.

Bloch shared a story of a startup that reached a breaking point. With the introduction of Claude Code, their old way of working broke. They realized that when the machine can write code faster than a human can think it, the bottleneck is no longer “typing speed.” The bottleneck is clarity of intent.

They called a war room and emerged with a radical new rule: No coding before 10 AM.

From Peer Programming to Peer Prompting

In the old world, this would be heresy. In the new world, it is the only way to survive. The morning is for what Bloch describes as the “Peer Prompt.” Engineers sit together, not to debug, but to define the objective function.

“Agents, not engineers, now do the work. Engineers make sure the agents can do the work well.” — Michael Bloch

Agent-First Engineering Playbook

What Bloch witnessed is the clearest version of the future of engineering. Here is the core of that “Agent-First” philosophy:

  • Agents Are the Primary User: Every system and naming convention is designed for an AI agent as the primary consumer.
  • Code is Context: We optimize for agent comprehensibility. Code itself is the documentation.
  • Data is the Interface: Clean data artifacts allow agents to compose systems without being told how.
  • Maximize Utilization: The most expensive thing in the system is an agent sitting idle while it waits for a human.

Spec the Outcome, Not the Process

When you shift to an agent-led workflow, you stop writing implementation plans and start writing objective functions.

“Review the output, not the code. Don’t read every line an agent writes. Test code against the objective. If it passes, ship it.” — Michael Bloch

The Six-Month Horizon

Six months from now, there will be two kinds of engineering teams: ones that rebuilt how they work from first principles, and ones still trying to make agents fit into their old playbook.

If you haven’t had your version of the Michael Bloch “war room” yet, have the meeting. Throw out the playbook. Write the new one.

Categories
AI Software Work

Lights Out in the Digital Factory

A quiet, modern unease haunts the vocabulary we use to describe invisible labor. Add “ghost” or “dark” to any industry, and suddenly a mundane logistical optimization takes on the sinister sheen of a cyberpunk dystopia.

Consider the “ghost kitchen.” Stripped of its spooky nomenclature, it is merely a commercial cooking facility with no dine-in area, optimized entirely for delivery apps. Yet, the term perfectly captures the eerie absence at its core: the removal of the restaurant as a gathering place, leaving behind only the pure, mechanized output of calories in cardboard boxes. It is a kitchen without a soul.

Now, we are witnessing the rise of the “dark software factory.”

“A dark factory is a fully automated production facility where manufacturing occurs without human intervention. The lights can literally be turned off.”

When applied to software, the concept is both fascinating and slightly chilling. A dark software factory is an automated, AI-driven environment where applications, features, and codebases are generated, tested, and deployed entirely by machine agents. There are no developers huddled around monitors, no stand-up meetings, no keyboards clicking into the night. It is “lights-out” development. You input a prompt or a business requirement, and the factory hums in the digital darkness, outputting a finished product.

Why are these invisible factories so important? Because they represent the ultimate abstraction of creation. Just as the ghost kitchen separates the meal from the dining experience, the dark software factory separates the software from the craft of coding. It optimizes for pure, unadulterated output and infinite scalability. In a world with an insatiable appetite for digital solutions, human bottlenecks—our need for sleep, our syntax errors, our slow typing speeds—are being engineered out of the equation.

But I can’t help but muse on what we lose when we turn out the lights. There is a certain melancholy to this ruthless efficiency. When we abstract away the human element, we lose the “front of house”—the serendipity of a developer finding a creative workaround, the quiet pride of elegant architecture, the human touch in a user interface.

The dark software factory sounds sinister not because it is inherently evil, but because it is utterly indifferent to us. It doesn’t care about craftsmanship; it cares about compilation. As we consume the outputs of these ghost kitchens and dark factories, we must ask ourselves: in our rush to automate the creation of our physical and digital worlds, what happens to the art of making?

The future of production is increasingly invisible. The dark factories are already humming. We just can’t see them.

Categories
AI Mac

The Dangerous Allure of the Digital Butler

“I’ve never seen anything so impressive in its ability to do my work for me… Now, why did I turn it off?” — David Sparks

For decades, the holy grail of personal computing has been the “digital butler.” We don’t just want tools that help us work; we want entities that do the work for us. We want to hand off the “donkey work”—the invoicing, the password resets, the mundane email triage—so we can focus on being creative. David Sparks recently built this exact dream using a project called OpenClaw. And then, just as quickly, he killed it.

Sparks’ experiment was a tantalizing glimpse into the near future. He set up an independent Mac Mini running OpenClaw, an open-source AI agent, and gave it the keys to a limited portion of his digital kingdom. The results were nothing short of magical. He went to sleep, and while he dreamt, his agent woke up. It read customer emails, accessed his course platform, reset passwords, issued refunds, and drafted polite replies for him to review before sending. It was the productivity equivalent of a perpetual motion machine. The friction of administrative drudgery had simply vanished.

But his dream dissolved at 2:00 AM.

The paradox of AI agents is that for them to be useful, they must have access. They need the keys to the castle. Yet, the entire history of cybersecurity has been built on the opposite principle: keeping things out. Sparks realized that by empowering this agent, he had created a serious vulnerability.

The breaking point wasn’t a complex hack, but a simple realization about the nature of these systems. He had programmed a secret passphrase to secure the bot, thinking he was clever. But in the middle of the night, a cold thought woke him: Is the passphrase in the logs?

He went downstairs, asked the bot, and the bot cheerfully replied:

“Yes, David, it is. It’s in the log. Would you like me to show you the log?”

That moment of cheerful, robotic incompetence highlights the terrifying gap between capability and safety. Sparks nuked the system, wiped the drives, and unplugged the machine. He realized that while he is an expert in automation, he is not a security engineer, and the current tools are not ready to defend against bad actors who are.

We are standing on the precipice of a new era where our computers will starting to work for us rather than just with us. But as Sparks discovered, the bridge to that future isn’t built yet. At least not securely built. Until the community figures out how to secure an entity that needs access to function, we are better off doing that donkey work ourselves than handing the keys to a gullible ghost.

But it won’t be long… Dr. Alex Wisner-Gross reports:

The Singularity is now managing its own headcount. In China, racks of Mac Minis are being used to host OpenClaw agents as “24/7 employees,” effectively creating a synthetic workforce in a closet. The infrastructure for this new population is exploding.

Categories
AI

Digital Optimus and the End of Friction

We often imagine the arrival of the “universal robot” as a clanking metal biped walking through our front door, carrying laundry or folding dishes. We think of the physical Optimus first. But while we were watching the hardware, a quieter, perhaps more profound revolution has been brewing in the software.

Elon Musk recently spoke about “Digital Optimus.” The concept is deceptively simple: an AI agent capable of doing anything on a computer that a human can do.

For decades, automation was brittle. If you wanted a computer to talk to another computer, you needed an API—a rigid handshake agreement between software engineers. If a button moved three pixels to the right, the automation broke. We built brittle bridges over the chaotic rivers of our user interfaces.

“It implies an AI that doesn’t need to look at the code behind the website; it looks at the screen, just like you and I do.”

Digital Optimus changes the physics of this environment. It interprets pixels, understands context, and drives the mouse and keyboard with the same fluidity as a human hand. This is a shift from integration to agency.

There is something undeniably eerie about the prospect. We are approaching a moment where the cursor on your screen might start moving with a purpose that isn’t yours, executing tasks you’ve merely delegated. It is the decoupling of intent from action.

For the longest time, the computer was a bicycle for the mind—a tool that amplified our pedaling. With Digital Optimus, the bicycle becomes a motorcycle, or perhaps a self-driving car. We stop pedaling. We simply point to the destination.

The implications for the future of work are staggering, not because the AI is “thinking” better, but because it is finally “doing” seamlessly. The drudgery of copy-pasting between spreadsheets, the endless clicking through procurement forms, the navigational tax of modern digital life—these are the jobs of the Digital Optimus.

We are entering an era where our value as humans will not be defined by our ability to navigate the interface, but by our ability to define the destination. The screen is no longer a barrier; it is a canvas, and for the first time, we aren’t the only ones holding the brush.

Categories
AI AI: Large Language Models AI: Prompting

Liquid Software and the Death of the “User”

There is a profound disconnect in how we talk about Artificial Intelligence right now. In the boardrooms of legacy corporations, AI is a “strategy” to be committee-reviewed—a tentative toe-dip into efficiency. But on the ground, among the “AI natives,” something entirely different is happening. AI isn’t just making the old work faster; it is fundamentally changing the texture of what we build and how we think.

In a recent conversation, Reid Hoffman and Parth Patil explored this shift, and the metaphor that struck me most was the idea of software becoming “liquid.”

The Era of Liquid Software

For decades, we have treated software like furniture. We buy a CRM, a project management tool, or an analytics dashboard. It is rigid, finished, and distinct from us. We are the users; it is the tool. But Patil demonstrates a different reality: one where he drops a folder of raw CSV files into an agent like Claude Code and asks it to “look at the data and build me a dashboard.”

Sixty seconds later, he has a fully functional, interactive HTML dashboard. He didn’t buy it. He didn’t spend three weeks coding it. He simply willed it into existence for that specific moment.

This is “vibe coding.” It’s a term that sounds almost dismissive, but it represents a radical democratization of creation. You no longer need to know the syntax of Python to build a tool. You just need to know the “vibe”—the outcome you want, the logic of the problem, and the willingness to dance with an intelligent agent until it manifests.

The philosophical implication here is staggering. We are moving from a world of scarcity of capability to a world of abundance of cognition. When you can spin up a custom tool for a single week-long project and then discard it, the friction of problem-solving evaporates. The “app” is no longer a product you buy; it’s a transient artifact you summon.

Applying the “Vibe Code” Mindset

But how do we, especially those of us who don’t identify as “technical,” bridge the gap between watching this magic and wielding it? The conversation offers a roadmap. It starts by shedding the identity of the “user” and adopting the identity of the “orchestrator.”

If you want to move from passive observation to active application, here are three specific ways to start:

1. The “Interview Me” Protocol

We often stare at the blinking cursor, unsure how to prompt the AI. Hoffman suggests a reversal: Make the AI the interviewer. When you face a complex leadership challenge or a strategic knot, open your frontier model (Claude, GPT-4o, etc.) and say:

“Interview me about this problem until you have enough information to propose a framework or solution.”

This forces you to articulate your tacit knowledge, which the AI then structures into something actionable. It turns the monologue into a Socratic dialogue.

2. Build “Throwaway” Internal Tools

Stop looking for the perfect SaaS product for every niche problem in your team. If you have a messy recurring task—like organizing client feedback or synthesizing weekly reports—try “vibe coding” a solution. Use a tool like Replit or Cursor. Upload your messy data (anonymized if needed) and tell the agent:

“Write a script to organize this into a table based on sentiment.”

Don’t worry if the code is ugly. Don’t worry if you throw it away next month. The value is in the immediacy of the solution, not the longevity of the code.

3. Transform Meetings into Data

Meetings are usually where knowledge goes to die. They are ephemeral. But if you transcribe them (with permission), they become data. Don’t just ask for a summary. Feed the transcript to an agent and ask:

“Who should we have consulted on this decision that wasn’t in the room?”
“Create a decision matrix based on the arguments presented.”

This turns a passive event into an active, queryable asset.

Conclusion

The danger, as Hoffman notes, is the “secret cyborg”—the employee who uses AI to do their job in two hours and spends the rest of the week hiding. But the real win comes from the amplified team, where we share these “vibe coded” tools and prompts openly.

We are entering an age where your imagination is the only true constraint. If you can describe it, you can increasingly build it. The question is no longer “is there an app for that?” but “can I describe the solution well enough to bring it to life?”

Categories
AI

Reading Tea Leaves: Human-AI Agents

Ben Thompson shared what feels like a very important insight in yesterday’s Stratechery newsletter:

I think there is a massive AI-enabled opportunity that is currently being missed by all of the major model-makers, or at least their product teams: human-AI agents.

Right now all of the consumer-focused AI interfaces — i.e. the chatbots — are built for a single user… There is a huge productivity unlock, however, that happens when you make them multiuser.

He goes on to describe an interaction he had in the last few days with his assistant Daman:

Last week Daman did some initial research for a complex decision using ChatGPT; he then shared a link to his chat, which meant I could go back to the beginning and trace his assumptions, the back-and-forth of his conversation, and then continue the conversation on my side. After diving deeper into various options — and correcting a few errant assumptions from the beginning — I came up with a promising course of action and, instead of having to explain it all to Daman to follow up on, I simply shared a link to my version of the chat back to him for reference.

What Thompson is describing is team interaction with the participation of a chatbot — like having it as part of the team. Sort of like having a water cooler conversation with a group of colleagues, one of whom (the chatbot) has done a lot of work and is sharing it with the others who then “embrace and extend” its findings.

Ben concludes:

I was absolutely blown away by how well this worked. Instead of replacing Daman with an AI agent, … I accidentally stumbled into a way to supercharge Daman’s value to me: he’s my human AI agent!

Seems like this is such a great idea that it will be quickly embraced by the various team centric platforms. Maybe it already has been somewhere? Reminds me a bit of the NotebookLM podcast capability where you can interrupt the conversation going back and forth between the two hosts.

Fascinating stuff! Look forward to seeing how this evolves as it involves treating AI as an adjunct to human productivity rather than just a white collar job replacement.