Last week I was cutting the grass in a heat wave and it hit me: the thing most people are missing about AI's next decade was sitting right there.
I'd been listening to an interview with Emad Mostaque. His argument: economics has always been about managing scarcity, and intelligence has always been the scarcest resource. As intelligence gets closer to costing nothing, that foundation cracks. He calls it the Intelligence Inversion, a shift from scarcity to abundance.
I realized abundance isn't free. It has a physical floor. Right now, that floor is heat.
Every chip running these models generates heat. There's a hard ceiling on how much you can pull out of a rack of silicon before the system throttles or fails. That ceiling has become the defining constraint in AI infrastructure. Not the algorithms. The thermodynamics.
For years, the AI story was about training, the massive compute to build a model. But training happens once or twice. Inference, running the model every time someone sends a prompt or an agent takes an action, happens millions of times a day. Inference is quietly becoming the bigger cost.
That shift is why a company called Etched is worth watching. They are betting that a chip built for 1 job, inference, could beat Nvidia's general-purpose GPUs. Their chip, Sohu, runs at a fraction of the power of a typical AI chip. Whether the bet pays off is unclear. A specialized chip is faster at its one job, but riskier if the model architecture shifts.
OpenAI joined AMD, Broadcom, Meta, Microsoft, and Nvidia this year in a consortium built to standardize the shift from copper to light based connections in AI data centers.
As AI agents take on longer, more autonomous work instead of single prompts, inference demand compounds. Every interaction needs power and generates heat. The abundant intelligence future, depends on solving that constraint at scale, with light: fiber optics.
We flesh suits aren't running out of ideas about what abundant intelligence could do. We might run out of ways to keep it cool.
The day SpaceX went public, my phone lit up.
Texts from family. Calls from friends. "Phil, should I buy SpaceX?" The excitement was real. The urgency was real. What most of them didn't realize is that anyone with a retirement plan likely already has a small SpaceX allocation sitting in their portfolio right now, thanks to the accelerated market entry the stock received.
That dynamic is exactly the lens I want to bring to this week's news. SpaceX went public and reportedly acquired Cursor, the AI coding platform, for $60 billion. The commentary has been loud and mostly focused on valuations and personalities. But if you filter the noise and look at what's actually being assembled underneath, something structural and quiet is happening.
This isn't a rocket company buying a software tool.
SpaceX lowers the cost of putting computing hardware into orbit. Starlink provides borderless internet with low latency to every corner of the planet. xAI builds frontier models. And now Cursor embeds those models directly where software gets written. Not a chatbot window. The development environment itself.
So what piece is getting almost no attention: AI agents don't have bank accounts.
When an autonomous coding agent needs to rent GPU space to compute, pull an external dataset, or commission a specialized agent to audit its work, it cannot wait three business days for an ACH transfer to clear. It needs programmable, instant value exchange. Tokenized payment rails. That's why the stablecoin regulatory framework moving through Washington right now isn't just a crypto story. It's the financial layer of this same stack being laid in real time.
The workforce of the next decade won't be measured in headcount. It will be measured in how well humans architect, govern, and add judgment to the agents doing the work.
I'm not here to tell you what to buy or who to root for. What I am saying is that the plumbing of the global economy is being replaced while most people are still asking whether they should buy the pipes.
Earlier this week I talked about the "weird middle" of AI adoption and why most people are still using the word "agent" to describe something much closer to a really smart template.
Here's where it gets more interesting.
True AI agents, the kind that actually observe environments, select tools, and take autonomous action toward a goal, won't just process text and summarize documents. Eventually they'll negotiate. They'll transact. They'll book your travel, renegotiate your subscriptions, and manage purchases on your behalf in real time.
And when they do, they'll need to move money.
Not in three business days. Not through a legacy bank account waiting on an ACH transfer. Instantly. Programmatically. Without a human approving each transaction in the middle of the chain.
That's exactly why what happened in Washington a couple weeks ago matters more to the AI conversation than almost anyone is saying right now. The CLARITY Act cleared a major milestone in the Senate, advancing a bipartisan framework to regulate payment stablecoins in the United States. A stablecoin is essentially a digital dollar backed 1:1 by liquid reserves, a dollar that can move at the speed of code.
Most of the coverage has framed this as a crypto story. It isn't. It's an AI infrastructure story.
The financial rails that true agents will need to operate autonomously are being laid right now, while most people are still debating whether Siri's new voice sounds more human. The plumbing is going in before most people know a house is being built.
Reality Check: AI and digital value exchange are not parallel conversations. They are converging. The companies and people who see that now will read the next wave clearly. Everyone else will wake up surprised.
We are not just building smarter software. We are building a new operating system for how value moves in the world.
How many people in your network are connecting these two conversations?
"Oh! Phil I made an agent at work."
My family said that at dinner last week, genuinely excited. I smiled and nodded because I love that she's engaging with this stuff. But in my head I was doing the translation work I always do now.
She hadn't built an agent. She'd built a really smart template. And that distinction is quietly becoming one of the most important gaps in how people understand where this technology is actually going.
That same week, six different professionals told me they use Microsoft Copilot every single day at work. When I asked if they knew it could connect directly to ChatGPT under the hood, they all looked at me like I'd spoken another language. To them it's just a helpful corporate utility. Something the company installed. They're using the technology. They're just not in a relationship with it yet.
This is the "weird middle" of AI adoption. And Apple's WWDC announcements this week made it impossible to ignore.
The next generation of Siri and Apple Intelligence aren't just feature updates. They're the moment AI gets woven directly into the fabric of daily life, your photos, your emails, your personal context. The moment it stops being a tab you open and starts being infrastructure you breathe.
Reality check: we've started using the same words to describe completely different things. A workflow follows steps you define. A GPT follows instructions you provide. A true AI agent observes its environment, makes independent decisions, selects its own tools, and takes action toward a goal with minimal human oversight. It doesn't pause to ask permission at every step. It determines its own next move. That's a different species entirely.
The "weird middle" is where mainstream is just arriving at conversations builders were having two years ago. That gap is frustrating if you're waiting for the world to catch up. It's a gift if you're paying attention.
What tech term do you hear getting misused most right now?
Yesterday I called out Token Maxing: the 2026 version of busy work where you run circles with prompts and feel productive while your actual outcomes stay flat.
So how do you actually Output Max?
It's not a magic prompt. It's a system. Here's the framework I use.
Start with the friction, not the tool. Most people open Gemini and ask "what can this do?" Wrong first question. The right question is: where is my workflow leaking energy? Is it the two hours you spend synthesizing research? Is it manually moving notes from a meeting into your project board? Output Maxing starts with a real problem, not a feature list.
Build the conveyor belt. Once you have the problem, don't just throw a chatbot at it. Map the flow first (
Mermaid.ai). Chain the tasks (Google Workflows). Connect the pieces (Google Stitch). Instead of running 10 prompts manually, you build something where data flows in, gets processed, and lands on your desk ready for a final judgment call. That's a system. That's Output Maxing.
The Taste Test
This is where the real work lives. Once the output arrives, you don't just hit send. You ask: does this sound like a human or a machine trying to pass as one? Does this solve the client's actual problem, or just the one they asked for? Is it grounded?
AI provides the scale. You provide the soul.
Here's the reality: Output Maxing isn't about working more with AI. It's about working less on "stuff" so you can spend your 3 billion seconds of experience on the things that actually matter.
Which step in your workflow is leaking the most energy right now?
#AI #SystemsThinking #FutureOfWork #OutputMaxing #HumanFirst
P.S. The framework only works if you're honest about where the friction actually lives. Most people already know the answer. They just haven't stopped moving long enough to look at it.
I will be making this next post a two parter for tomorrow! Token Maxing is the 2026 version of looking busy.
I'm watching teams run into the same wall right now. Chasing the newest model. Testing the latest prompt. The screen is moving, but business is still stuck.
There is a difference between AI activity and AI output. Token Maxing is tool-focused. It's running 50 prompts to get one mediocre blog post. It's opening 12 tabs just to summarize a meeting. It feels like progress because your screen is full.
Output Maxing is outcome-focused. It's the person who uses Claude, Gemini, Grok ect. to eliminate three steps of a process entirely so they can go get a coffee and actually think.
AI Isn’t Just a “Tool”. It’s a System.
Most people treat AI like a hammer. If you have a hammer, you look for nails. But AI is closer to the plumbing system for your business. You don't "use" plumbing. You design your house around it so water flows where it needs to without you thinking about it.
If you focus on what AI can do, you'll keep adding tools until you're overwhelmed. If you focus on what needs to get done, you'll start building systems that compound your effort. Those are two completely different games.
The "Human" Reality Check
I'm not a cheerleader for the software. I'm a cheerleader for the humans trying to navigate this without losing their minds. The winners of this decade won't be the ones with the most tokens used. They'll be the ones who kept their agency, their judgment, and their strategic taste while letting the agents handle the heavy lifting.
Stop collecting tools. Start refining outcomes.
Tomorrow I'll share the actual framework I use to move from Token Maxing to Output Maxing.
How many AI tabs do you have open right now?
#AI #FutureOfWork #SystemsThinking #TechLeadership #OutputMaxingLet me take a break from the macro AI conversation for a second.
While everyone debates AGI timelines and "The Bubble," the most effective operators I know are quietly automating 40% of their Monday. No PhD. No $100M budget. Just the right tools and the willingness to actually use them.
Here are four worth exploring right now, all free:
Gemma 4. Google's open model is lightweight and runs locally. If you've ever hesitated to use AI because you didn't want sensitive business data sitting on someone else's cloud, this is your answer. Good entry point for understanding how models actually work beyond just prompting.
Mermaid.ai. This one is underrated. You describe a process in plain text and it generates a clean visual diagram. If you lead a team, teach, or regularly have to explain complex systems to people who don't live inside your head, this saves you real time.
Google Stitch. Instead of manually copying information from one tool to another, Stitch lets you start building simple automations that do it for you. This is where AI stops feeling theoretical and starts feeling useful.
Google Workflows. Think of this as Stitch with more horsepower. You chain actions across systems so entire processes run with minimal input. If you're in operations, this is where the real time savings show up.
The goal isn't to master all of this overnight. It's to pick one, break something, learn from it, and build intuition that way. You don't become a Director by watching. You become one by doing.
If you want help figuring out how any of these fit your specific business or workflow, I'm happy to have that conversation.
Which one of these would move the needle for you this week?
#AI #AITools #Productivity #Automation #FutureOfWork
P.S. "AI Native" isn't a title you earn. It's a habit you build. Start small. Start today.
Stop thinking of AI as a "calculator." It’s much closer to an "Analogy Machine."
That reframe hit me while listening to Geoffrey Hinton, one of the actual godfathers of AI, break down how these models do "think."
Here's the reality: your brain has roughly 100 trillion synaptic connections. You are an architectural giant. But a long human life is only about 3 billion seconds of experience. We're built for depth. We're limited by time.
A modern LLM has about 1 trillion connections, which is 100x smaller than us structurally. But it's been trained on the equivalent of thousands of human lifetimes worth of patterns, language, and ideas. Fewer connections. Infinitely more experience. So what does it do? It gets ruthlessly good at finding patterns across everything it's ever absorbed.
Hinton's conclusion isn't a "Terminator" moment. It's quieter than that, and honestly more interesting. AI will surpass human expertise one field at a time. Mathematics. Protein folding. Legal analysis. Not all at once. Just steadily, domain by domain, as raw experience starts to outrun our architectural head start.
You know what that means for us? We don't need to out-know the machine. We need to out-judge it, wisdom.
AI can read every medical paper ever published. It still doesn't know what words to use when it's 2am and a family is scared. It can solve the equation but it can't tell you why that problem was worth solving in the first place. That part is still ours.
We're becoming Directors in a world full of the most capable Associates who ever existed. The question is whether we're ready to lead them well.
So I'm genuinely curious: if AI became the undisputed expert in your field tomorrow, what's the one human-only skill you'd lean all the way into?
#AI #FutureOfWork #HumanFirst #AIConsulting #TechLeadership
P.S. The fact that a model with fewer connections than the human brain can hold more functional knowledge than any single person ever could… that should humble us and excite us at the same time. Both. Simultaneously
