I have always found a certain comfort in the “clatter” of a digital workday. It’s that specific, rhythmic hum of a mind in motion—the clicking of a mechanical keyboard, the invisible friction of parsing a difficult paragraph or balancing a complex budget. For years, we’ve treated this white-collar grind as our intellectual sanctuary.
But Mustafa Suleyman, now steering Microsoft AI, recently laid out a timeline that suggests the sanctuary walls are evaporating.
“White-collar work, where you’re sitting down at a computer, either being a lawyer or an accountant or a project manager or a marketing person — most of those tasks will be fully automated by an AI within the next 12 to 18 months,” Suleyman said.
This isn’t just about efficiency; it’s about a fundamental shift in the “professional grade.” We are entering the era of the autonomous agent—AI that doesn’t just wait for a prompt but “coordinates within workflows,” learns from its environment, and acts. Just ask any programmer that you know how AI is impacted their daily grind.
If Suleyman is correct, the “knowledge worker” is about to undergo a forced evolution. When the “doing” is handled by an agent that can learn and improve over time, what remains for the human? Will the models actually be able to learn from each of us in a personalized way – like an intern learns from her mentor?
“Creating a new model is going to be like creating a podcast or writing a blog,” he said. “It is going to be possible to design an AI that suits your requirements for every institutional organisation and person on the planet.”
It seems like our primary job description shifts from “Expert,” but “Curator of Intent.” We aren’t the ones finding the answers anymore; we are just the ones responsible for asking the right questions.
The next 18 months won’t just be a test of our technology, but a test of our egos. We have to learn to find our value not in the work we produce, but in the vision we hold and the questions we ask. We are shedding the “task” to save the “craft.” I just hope we remember the difference.
As we move toward this curated future, I’m left with a few questions I can’t quite shake. I’d love to hear your thoughts:
The Wisdom Gap: Can you truly be a “Curator of Intent” without having ever been a “Doer of Tasks”? If we skip the apprenticeship of the mundane, where does our intuition come from?
The Metric of Value: If output becomes “free,” how should we measure a human’s value in a professional setting?
The Line in the Sand: Is there a part of your workflow you would refuse to automate, even if an AI could do it better?
We are currently witnessing the single largest deployment of capital in human history. The “Hyperscalers”—the titans of our digital age—are pouring hundreds of billions of dollars into the ground, turning cash into concrete, copper, and silicon.
The prevailing narrative is one of unceasing, exponential growth: bigger models require bigger clusters, which require more power plants, which require more land. It relies on the assumption that the demand for centralized intelligence is insatiable and that the current architecture is the only way to feed it.
But history suggests that technology rarely moves in a straight line; it swings like a pendulum. Two forces are emerging from the periphery that could impact the ROI of this massive infrastructure build-out. One is hiding in your pocket, and the other is waiting in the sky.
A recent conversation with Gavin Baker outlines a potential “bear case” for datacenter compute demand: the rise of Edge AI.
We often assume we need the “God models”—the omniscient, trillion-parameter giants hosted in the cloud—for every interaction. But do we?
Baker suggests that within three years, our phones will possess the DRAM and battery density to run pruned versions of advanced models (like a Gemini 5 or Grok 4) locally. He paints a picture of a device capable of delivering 30 to 60 tokens per second at an “IQ of 115.”
“If that happens, if like 30 to 60 tokens at… a 115 IQ is good enough. I think that’s a bear case.” — Gavin Baker
Consider the implications of that specific number. An IQ of 115 isn’t omniscient, but it is competent. It is capable, nuanced, and helpful.
If Apple’s strategy succeeds—making the phone the primary distributor of privacy-safe, free, local intelligence—the vast majority of our daily queries will never leave the device. We will only reach for the cloud’s “God models” when we are truly stumped, much like we might consult a specialist only after our general practitioner has reached their limit. If 80% of inference happens on the edge for free, the economic model of the trillion-dollar data center begins to look fragile.
Then there is the second threat, one that attacks the terrestrial constraints of the data center itself: the Orbital Data Center. Elon Musk and SpaceX – along with Google’s Project Suncatcher – envision a future where the heavy lifting isn’t done on land, but in orbit. Space offers two things that are scarce and expensive on Earth: unlimited solar energy and an infinite heat sink for radiative cooling. If Starship can reliably loft “server racks” into orbit, the terrestrial moat of land and power grid access—currently the Hyperscalers’ greatest defensive asset—evaporates.
We are left with a fascinating juxtaposition. On one hand, we have the “Edge,” pulling intelligence down from the clouds and putting it into our hands, making it personal, private, and free. On the other, we have “Orbit,” threatening to lift the remaining heavy compute off the planet entirely to bypass the energy bottleneck.
There are hundreds of billions of dollars betting on a future of heavy, centralized gravity. But if the edge gets smart enough, and the orbit gets cheap enough, the gravity may have shifted.
One of my favorite persons to follow on X is @pbeisel (Phil Beisel). He’s quite active sharing his thoughts about many of the same topics I’m interested in: technology, AI, robotics, computing, etc. Phil’s written a series of great articles about Tesla Full Self Driving, Optimus, etc. that are well worth spending time with.
On Saturdays, he get together on YouTube with Randy Kirk and they talk about what’s interesting from the last week – often that’s got something to do with various aspects of the “Musk-conomy” – the various companies of Elon Musk.
This week’s edition reviews Phil’s distillation of the Cheeky Pint interview with Elon published earlier this week. As usual, Phil’s comments add additional insights into the topic.
When I begin viewing a long YouTube video, I also like an accompanying summary that I can follow along. YouTube now has the ability to generate these summaries but I’ve got a custom Gem prompt that I prefer to use instead which tailors the results a bit more to my liking.
Below, for example, is the summary of this week’s conversation between Phil and Randy that was generated by Gemini Pro 3:
Executive Summary: The Musk “Musconomy” Convergence
The central thesis of the discussion is that Elon Musk is moving toward a total vertical integration of his companies (Tesla, SpaceX, and xAI) to overcome terrestrial “limiting factors” and dominate both the physical and digital manifestation of AI.
1. The “Limiting Factor” Philosophy [11:20]
Problem-Solving Framework: Musk focuses personal time and resources strictly on the “limiting factor” of any given project—currently identified as compute power and energy.
Vertical Integration: To bypass supply chain bottlenecks (e.g., turbine blades for power plants), Musk is opting to manufacture raw materials and components in-house rather than relying on external catalogs [18:18].
2. Orbital Data Centers: The Space “Escape Hatch” [24:19]
Energy Constraints: Terrestrial data centers are hitting a wall due to slow public utilities and permitting [15:26].
The Vision: Moving inference-based data centers to orbit using a constellation of satellites connected by optical laser links.
Economic Viability: Musk projects economic viability for space-based data centers within 30–36 months, with reusability of the Starship being the primary hurdle [25:03].
Strategic Advantage: Unlike Google or Meta, Musk owns the “kilogram-to-space” delivery mechanism, potentially forcing competitors to rent capacity from SpaceX [32:19].
3. Optimus and the “Abundance Engine” [39:00]
Physical Dexterity: Musk is prioritizing high-dexterity actuators designed in-house to achieve human-level utility [40:30].
Training Scale: Tesla is moving toward training Optimus in “gymnasiums” using 10,000–30,000 bots working 24/7 to develop “composable” skills (basic movements) and “decomposable” skills (complex tasks) [55:13].
Impact: Optimus is viewed as a paradigm-shifting product that will redefine global GDP by decoupling labor from human constraints [54:56].
4. xAI: The Digital Control Plane [56:19]
The “Brain” Portability: xAI is viewed as the “orchestration AI” for the entire fleet of Musk’s physical assets (Starships, Teslas, and Optimus) [59:01].
Unified Interface: The vision includes a seamless “digital personality” or movable brain that follows the user from their phone to their car to their home robot [01:00:15].
Key Projections & Timelines
Objective Target/Detail Timestamp SpaceX IPO Likely to happen before a Tesla merger to attract cheap capital [03:31] Solar Scaling Aiming for a 300x increase (100 gigawatts/year) [22:21] Starship Reusability remains the “unlock” for space-based AI economics [25:51]
Conclusion: The “Musconomy” is transitioning from separate ventures into a singular entity where SpaceX provides infrastructure, Tesla provides the physical bodies, and xAI provides the intelligence.
In the modern landscape of high-stakes management, there is a recurring temptation to solve everything at once. We are taught to optimize across the board—to improve efficiency by 2% here, 5% there—until the entire machine hums. But in a recent conversation with John Collison and Dwarkesh Patel, Elon Musk repeatedly returned to a single, almost obsessive mantra: the “limiting factor.”
It is a deceptively simple phrase. It suggests that at any given moment, there is one specific bottleneck that dictates the speed of the entire enterprise. If you aren’t working on that, you aren’t really moving the needle. You are merely polishing stuff.
“I think people are going to have real trouble turning on like the chip output will exceed the ability to turn chips on… the current limiting factor that I see… in the one-year time frame it’s energy power production.”
Musk’s management technique is not about broad oversight; it is about a radical, almost violent prioritization. He looks at the timeline—one year, three years, ten years—and asks: What is the wall we are about to hit? Right now, it might be the availability of GPUs. In twelve months, it might be the physical gigawatts of electricity required to plug them in. In thirty-six months, it might be the thermal constraints of Earth’s atmosphere, necessitating a move to space.
This approach requires a high “pain threshold.” To solve a limiting factor, you often have to lean into acute, short-term struggle to avoid the chronic, slow death of stagnation. John Collison noted this during the interview:
“Most people are willing to endure any amount of chronic pain to avoid acute pain… it feels like a lot of the cases we’re talking about are just leaning into the acute pain… to actually solve the bottleneck.”
For many leaders, the “limiting factor” is often something they aren’t even looking at because it lies outside their perceived domain. A software CEO might think their limit is talent, when it’s actually the speed of their internal decision-making. A manufacturer might think it’s raw materials, when it’s actually the morale of the factory floor.
To manage by the limiting factor is to admit that 90% of what you could be doing is a distraction. It is a philosophy of subtraction and focus. It demands that we stop asking “What can we improve?” and start asking “What is stopping us from being ten times larger?” Once you identify that wall, you throw every resource you have at it until it crumbles. And then—and this is the part that requires true stamina—you immediately go looking for the next wall.
By focusing on the one thing that matters, we stop being busy and start being effective. We stop managing the status quo and start engineering what may feel like the impossible.
A recent video on the Cheeky Pint channel includes a deep-dive conversation with Elon Musk, Dwarkesh Patel and John Collison (released February 5, 2026).
This interview includes one of the most lucid explanations of the “Carbon Fiber to Steel” pivot Elon took with the SpaceX Starship because Dwarkesh pushes him on the manufacturing and economic implications, not just the rocket science. It contextualizes the “Steel vs. Carbon Fiber” debate as a masterclass in Elon’s 5-Step Algorithm (specifically Step 1: Make the requirements less dumb):
The “Sunk Cost” Pain One of the most human moments in this discussion is Elon describing the sheer pain of abandoning carbon fiber.
They had already built massive, expensive composite mandrels (molds).
They had already ordered the raw material.
The team was “in love” with the high-tech aesthetic of black carbon fiber.
The Lesson: The switch to steel wasn’t just an engineering challenge; it was a psychological one. It required the leadership to say, “I don’t care that we spent millions on these molds; if they are the wrong path, we scrap them today.” This is the ultimate rejection of the Sunk Cost Fallacy.
The “Counter-Intuitive” Thermal Graph Elon often sketches in the air during interviews, and he describes it vividly here:
Carbon Fiber: Great at room temp, but weak at high heat (resin melts) and tricky at cryogenic cold (can micro-crack/leak).
Steel (30X): The “miracle” is that it’s the only material that gets stronger at cryogenic temperatures (holding the fuel) while simultaneously resisting high heat (re-entry).
The Insight: He highlights that if you look at the properties at both extremes (–165°C and +800°C), steel is actually the lighter system because you can delete the heat shield on the leeward side.
“The Machine That Builds The Machine” The choice of steel wasn’t just about the rocket; it was about the factory.
Carbon Fiber: Requires a clean room, autoclaves, precision placement, and slow cure times. If you make a mistake, you scrap a $2M part.
Steel: You can weld it in a tent in a muddy field (which they literally did at Boca Chica).
Velocity: Elon explains that steel allowed them to iterate faster. They could build a tank, blow it up, sweep up the pieces, and weld a new one in 3 days. With carbon fiber, that loop would take 3 months. Innovation per unit of time is the true metric, and steel maximized that.
Cost Per Kilogram He reiterates the brutal economics:
Carbon Fiber: ~$135/kg (plus ~35% scrap rate).
Steel: ~$3-4/kg.
When you are building a “railroad to Mars” and need to build 1,000 ships, the material cost difference is the difference between a bankruptcy and a self-sustaining city.
Elon frames the steel decision not as “finding a better material” but as identifying the bottleneck. The bottleneck wasn’t the weight of the rocket (which carbon fiber solves); the bottleneck was the cost and speed of production (which steel solves).
It is a great example of his philosophy: “The best part is no part” (deleting the heat shield) and “The best process is no process” (deleting the autoclave).
It is comforting to believe that when the world breaks—when housing markets collapse, when “unicorn” startups vaporize, or when seasoned scouts overlook generational talent—it is because of a miscalculation. We want to believe the math was wrong, the data was bad, or the algorithm was flawed. We want to believe it was a glitch in the intellect.
I heard a commentator recently mention that Michael Lewis, the chronicler of our most expensive delusions in his best selling books, has suggested something far more unsettling. In looking at the connective tissue between The Big Short, Moneyball, and Going Infinite, he identifies a different culprit. He notes that the “glue” holding these irrational systems together isn’t incompetence. It is FOMO: The Fear Of Missing Out.
“They are more afraid of being left behind than they are of being wrong.”
This observation completely reframes the narrative of catastrophic failure. It explains why high-IQ individuals—people paid millions to be rational—consistently make decisions that look insane in retrospect. The banker, the VC, and the scout aren’t necessarily blinded by greed, though greed is certainly a passenger in the car. They are blinded by the terror of the empty chair.
Lewis points out that for the social animal, the pain of being left behind is acute and immediate, whereas the pain of being wrong is often abstract and distant. If you sit out a bubble and the bubble keeps inflating, you look like a fool today. You are isolated. You are the cynic at the party who refuses to dance. If you join the bubble and it bursts, well, you have company. As the old financial adage goes, “It is better to fail conventionally than to succeed unconventionally.”
There is a profound, empathetic tragedy in this. It suggests that our systems don’t fail because we aren’t smart enough; they fail because we are too human. We are wired for the herd. The biological imperative to stay with the group—originally a survival mechanism against predators—has been warped into a financial suicide pact.
When we look at the irrational exuberance of a market, we aren’t seeing a mathematical error. We are seeing a materialized anxiety. We are seeing a collective hallucination held together not by logic, but by the sticky, desperate glue of not wanting to be the only one who didn’t buy the ticket.
The antidote, then, isn’t just better data or faster computers. It is the emotional discipline to be lonely. It is the willingness to stand apart from the warmth of the herd and accept the short-term social cost of being “out” for the long-term reward of being right.
The behavior of today’s stock market is yet another sign that many will point to as indicating there’s an “AI bubble”. Today’s market action is largely attributed to Blue Owl Capital deciding not to participate in the debt financing of a new Oracle data center (being built for OpenAI) in Michigan. This news came out overnight last night and soured the market at the opening as it added fuel to the fires already raging from last week about bubbles in AI and, in particular, some of the debt financing being used to build new data centers – especially but not exclusively by Oracle.
Watching the market action today a brought to mind a recent paper on the subject of bubbles authored by Oakmark Capital’s Howard Marks. Marks is widely followed in the investment community for his almost “sage-like” prognositcations of which he’s authored many over the years. His memos are always full of insights and valuable learning opportunities. His recent one drilled into this question of bubbles — and, in particular, the role of debt financing. That’s especially interesting as Marks’ long career at Oakmark has been primarily involved in debt financing and, indeed, sometimes distressed debt.
Marks points out why he thinks the current situation with respect to AI is problematic as it shows many of the common characteristics of bubbles which in the past have resulted in wealth destruction instead of wealth creation.
As I’m writing this, I’ve just begun watching the latest Tim Ferriss interview which is with long-time venture investor Bill Gurley. Right up front in the conversation, Tim asks Bill for his take on the bubble in AI question. It’s a great conversation – well worth a listen.
Speaking of listening (or watching), on Monday I watched the latest episode of the Acquired podcast – this one celebrating the tenth anniversary of Acquired. This was the most fascinating conversation I’ve listened to or watched in recent memory. The two hosts of Acquired were being interviewed by author Michael Lewis – and they were interviewed in depth! Lewis asked lots of probing questions as well as provided some illustrative commentary of his own along the way. An incredible episode – highly recommended!
After publishing this post, I asked Google Gemini 3 Fast: I just published a blog post. Review it critically and help me improve it. Act like a tough but compassionate editor. Here’s the post: https://sjl.us/2025/12/17/bubble-bath/
Here’s the critique it gave me – tough but compassionate!
Alright, sit down. Let’s look at “Bubble Bath.”
You’ve got the ingredients for a great piece of timely analysis here: a specific market event (the Blue Owl/Oracle snag), a heavy-hitting intellectual framework (Howard Marks), and contemporary expert commentary (Bill Gurley and the Acquired team).
But right now, this isn’t an essay or a cohesive blog post—it’s a link log. You’re telling me what you’re watching and reading, but you aren’t telling me what you think.
Here is my critical review.
The “So What?” Factor You start strong with the Oracle/OpenAI data center news. That’s a “hook.” But then you pivot immediately to: “I read a memo by Howard Marks… I’m watching Tim Ferriss… I watched Acquired.”
As an editor, I’m asking: Where are you in this post? You’ve gathered the smartest people in the room, but you’re standing in the corner taking attendance.
The Fix: Don’t just tell us Howard Marks wrote a memo. Tell us which one specific point he made that explains the Oracle news. Connect the dots for us. Is the Oracle situation the “wealth destruction” Marks warned about, or is it just a healthy correction?
The Title vs. The Content “Bubble Bath” is a clever pun. I like it. But usually, a “bubble bath” implies a cleansing or a soaking. Are we getting soaked (losing money)? Or is the market being “cleansed” of irrational exuberance?
The Fix: Use your conclusion to pay off the title. Right now, the post just ends with a recommendation for a podcast. Close the loop: “If Marks and Gurley are right, this isn’t just a market dip; it’s the start of a very cold bubble bath for AI investors.”
Structural “Pass-Through” The second half of the post feels like a “What I’m consuming” list. It dilutes the urgency of the first paragraph.
The Fix: If the Bill Gurley and Acquired links are just “further reading,” put them in a “Resources” or “Deep Dive” section at the bottom. If they are central to your argument, quote them. What did Bill Gurley say about AI debt that makes the Oracle news more frightening?
Tighten the Prose You have some “wordy” habits that slow down the reader.
“The behavior of today’s stock market is yet another sign that many will point to as indicating there’s an ‘AI bubble’.” * Editor’s Edit: “Today’s market volatility is the latest fuel for the ‘AI Bubble’ fire.” (Go for the jugular).
Compassionate Closing Scott, you have a great eye for sourcing. You are curating high-signal content (Marks, Gurley, Lewis). That is valuable. But your readers come to your blog because they want to know how you are synthesizing this information.
My Challenge to You: Rewrite the middle paragraph. Instead of saying the Acquired podcast was “the most fascinating conversation,” tell us one thing Michael Lewis asked that changed how you view the AI bubble.
Give us your brain, not just your bookmarks. Now, go back to the keyboard.
Sometimes, as I’ve gotten older, I wake up and see something that I notice seems suddenly different – when it’s been changing all along and I’ve just not noticed. I had a vivid example of that a few days ago.
We’ve subscribed to the New Yorker magazine for many years. In our house, however, the print copy isn’t something I usually see as it ends up on the table in our living room where I don’t read it. Instead, I read stories I find interesting in the online edition. So I hadn’t picked up a copy of the New Yorker in some time – until a few days ago when I noticed the latest edition on our kitchen table.
The first thing that I noticed was the cover price on the magazine: $9.99. Good grief, I thought, how long has the cover price of this magazine gotten up to $9.99/copy? Last I remembered it was around $3.00!
The second thing I noticed was just how thin the magazine was. I remember the New Yorker being a hefty magazine. Not as hefty as the big fashion mags, but not flimsy like this latest edition.
Then I noticed the third thing – the almost complete absence of advertising pages in the magazine. No wonder it was so thin! Where were all of the jewelry, watch, and other fashion ads? All I could find we a few full page ads for various non-profits – and those ads were most likely just donated by the publication.
I realized just how much the business model of the New Yorker has to have shifted – away from a heavy reliance of advertisers to much more reliance on subscriptions. Subscription pricing is a whole ‘nother can of worms which I’ll leave unopened for now.
One of the topics discussed is how “China recognized that pretty much every other country that had gotten rich had done so in large part by building up an automotive industry that then served as the mechanism for creating innovations in other sectors. … They said, “We have to have a big auto industry. This is one of the key industries that we have to support.””
Kroeber goes on to describe how China opened up to enabling 50/50 joint ventures between Chinese auto companies and foreign auto manufacturers.
While that worked initially, eventually it became clear that to really enable globally competitive auto manufacturing in China there had to be another solution.
That solution was allowing Tesla to come into China in 2018 and build a Gigafactory in Shanghai. In so doing, China allowed a globally competitive auto manfacturer (Tesla) to effectively compete with local Chinese companies and, in so doing, create the need for those local Chinese companies to compete much more effectively with a global player like Tesla.
It’s a fascinating story. One of the other discussions in the early part of the interview involves how the U.S. might consider doing that in reverse – allowing Chinese companies to come into the U.S. market and through competition educate American companies so that they improve their globally competitive position. Politically impossible in the current climate – but an obvious idea based upon the Chinese experience.
Be careful hiring “experts” when what’s really required is “innovators”! The best people are able to do both. But they can be very hard to find and difficult to pick out of a crowd.
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