Jonathan

[Louisa Nicola @louisanicola_]

Your Brain Was Not Built For 15-Second Context Switching

I just read a study showing TikTok-style short form videos significantly impair prospective memory. The ability to remember and execute intended actions.

Not motivation. Not discipline.

Your brain literally becomes worse at holding intention after rapid context switching.

That is a neurological cost most high performers still underestimate.

[Ricky Ho @rickyho_1989]

Everyone is focusing on the soaring memory cost in the Vera Rubin rack. But the real shocker in this Morgan Stanley slide is actually power, because the industry is now talking about moving from roughly 120kW per rack today toward potentially 600kW per rack by the Vera Rubin Ultra generation in 2027, which is an almost unimaginable escalation in power density within an incredibly short period of time.

To put this into perspective, many traditional enterprise datacenters historically operated at only a few kilowatts per rack, while even modern hyperscale campuses today often consume only tens of megawatts in total facility power draw. But once you begin deploying hundreds or thousands of 600kW AI racks simultaneously, the math becomes almost absurd because a large-scale Vera Rubin Ultra cluster could eventually consume gigawatts of electricity, effectively rivaling the energy demand of a mid-sized city.

And this is where the market still massively underestimates the second-order implications of the AI boom, because the bottleneck is no longer simply semiconductors, GPUs, or memory supply. The bottleneck increasingly becomes electricity itself.

The US power grid can barely keep up with current AI infrastructure demand already, while transmission congestion, transformer shortages, substation constraints, cooling limitations, permitting bottlenecks, and aging grid infrastructure are becoming increasingly visible across major datacenter hubs. Importantly, grid infrastructure cannot scale at semiconductor speed. You can accelerate chip production with enough capital expenditure and engineering talent, but building transmission lines, substations, generation capacity, cooling systems, and interconnection approvals often requires many years due to environmental reviews, local opposition, labor shortages, and physical construction constraints.

This is precisely why we continue believing the AI buildout is not a two-to-three-year investment cycle, but instead a decade-long industrial transformation that increasingly resembles the buildout of railroads, electricity networks, and telecom infrastructure during previous industrial revolutions.

And this is also why energy infrastructure is quietly becoming one of the most important and underappreciated AI trades globally.

The winners are no longer just GPU companies. The winners increasingly include utilities like Constellation Energy and Vistra, nuclear-related plays like Oklo and NuScale Power, gas infrastructure companies like Kinder Morgan and Williams Companies, grid and electrical equipment suppliers like GE Vernova, Eaton, Schneider Electric, and Vertiv, as well as transformer, cooling, and datacenter infrastructure providers that now sit directly inside the physical backbone required to support next-generation compute.

Hyperscalers themselves are starting to understand this reality. Companies like Microsoft, Amazon, Alphabet, and Meta are no longer simply software companies buying servers. They are increasingly becoming quasi-energy infrastructure companies because securing long-duration power availability is becoming strategically inseparable from securing compute capacity itself.

That is why nuclear power is quietly returning to the center of the conversation. Hyperscalers may eventually fund or directly partner on nuclear generation projects out of pure necessity because renewable intermittency alone cannot reliably support ultra-high-density AI clusters operating continuously at scale.

In many ways, AI is beginning to collide with physical reality. You cannot run trillion-dollar next-generation compute infrastructure on transmission systems and grid architectures that were largely built decades ago for a completely different industrial era.

The semiconductor story may have started the AI race, but energy infrastructure may ultimately determine who wins it.

[Ihtesham Ali @ihtesham2005]

A Hungarian psychologist raised three daughters to prove that any child could become a chess grandmaster through early specialization. He succeeded. Two of them became grandmasters. One became the greatest female chess player who ever lived.

Then a sports scientist looked at the data and found something nobody wanted to hear.

His name is David Epstein. The book is called "Range."

The Polgar experiment is one of the most famous case studies in the history of deliberate practice. Laszlo Polgar wrote a book before his daughters were even born arguing that geniuses are made, not born. He homeschooled all three girls in chess from age four. By their teens, Susan, Sofia, and Judit were dominating tournaments against grown men. Judit became the youngest grandmaster in history at the time, breaking Bobby Fischer's record. The story became the gospel of early specialization. Pick a domain young, drill it hard, and you can manufacture excellence.

Epstein opens his book by telling that story honestly and then quietly demolishing the conclusion most people drew from it.

Chess works that way. Most things do not.

Here is the distinction that took him four years of research to articulate, and that almost nobody who quotes the 10,000 hour rule has ever read.

There are two kinds of environments in which humans develop expertise. Psychologists call them kind and wicked. A kind environment has clear rules, immediate feedback, and patterns that repeat reliably. Chess is the cleanest example. Every game ends with a winner and a loser. Every move is recorded. The board never changes shape. The pieces never invent new ways to move. A child who plays ten thousand games will see most of the patterns that exist in the game, and pattern recognition is exactly what chess mastery is built on.

A wicked environment is the opposite. Feedback is delayed or misleading. Rules shift. The patterns that worked yesterday may be exactly the wrong patterns to apply tomorrow. Most of the real world looks like this. Medicine is wicked. Investing is wicked. Building a company is wicked. Scientific research is wicked. Almost every job that involves a complex changing system with humans in it is wicked.

The Polgar sisters trained in the kindest environment any human can train in. Their success was real and the method was correct. The mistake was generalizing the method to fields where the underlying structure of the environment is completely different.

Epstein's research is what made the implication impossible to ignore.

He looked at the careers of elite athletes outside of chess and golf and found that the pattern was almost the inverse of what people assumed. The athletes who reached the very top of their sports were overwhelmingly people who had played multiple sports as children, specialized late, and often switched disciplines well into their teens. Roger Federer played squash, badminton, basketball, handball, tennis, table tennis, and soccer before tennis became his focus. The kids who specialized in tennis at age six and trained year-round for a decade mostly burned out, got injured, or topped out at lower levels of the sport.

The same pattern showed up everywhere he looked outside of kind environments. Inventors with the most patents had worked in multiple unrelated fields before their breakthrough work. Comic book creators with the longest careers had drawn for the most different genres before settling. Scientists who won Nobel Prizes were dramatically more likely than their peers to be serious amateur musicians, painters, sculptors, or writers.

The skill that mattered in wicked environments was not depth in one pattern. It was the ability to recognize when a pattern from one domain applied unexpectedly in another. That kind of thinking cannot be built by drilling a single subject. It can only be built by accumulating mental models from many subjects and learning to move between them.

The deeper finding is the one that should change how you think about your own career.

Specialists in wicked environments often get worse with experience, not better. Epstein cites studies of doctors, financial analysts, intelligence officers, and forecasters showing that years of experience in a narrow domain frequently produce more confident judgments without producing more accurate ones. The expert builds elaborate mental models that feel comprehensive and turn out to be increasingly disconnected from the actual structure of the problem. They stop noticing what does not fit their framework. They mistake fluency for understanding.

Generalists do better in wicked domains for a reason that sounds almost mystical until you understand the mechanism. They have less invested in any single mental model, so they abandon broken models faster. They are used to being a beginner, so they are not threatened by the discomfort of not knowing. They have seen enough different domains that they can usually find an analogy from one field that unlocks a problem in another. The technical name for this is analogical thinking, and the research on it is one of the most underrated bodies of work in cognitive science.

The single most useful sentence in the entire book is the one Epstein puts almost as a throwaway.

Match quality matters more than head start.

A person who tries six different fields in their twenties and finds the one that genuinely fits them will outperform a person who picked one field at fourteen and stuck to it on willpower alone. The lost years were not lost. They were the search process that produced the match. Every field they walked away from taught them something they later imported into the field they finally chose.

The reason this is so hard to accept is cultural, not empirical. We tell children to pick a path early. We reward the prodigy who knew at six. We treat the late bloomer as someone who failed to launch on time, when the data suggests they were running an entirely different and often more effective optimization process underneath.

The Polgar sisters were not wrong. The conclusion the world drew from them was.

If your environment is genuinely kind, specialize early and drill hard. If it is wicked, and almost every interesting human problem is, then the people who win are the ones who refused to specialize until they had seen enough to know what was actually worth specializing in.

You are not behind. You were running the right experiment all along.

[Nic @puckrin]

Wow.

A former US Treasury Secretary suggested US authorities prepare a back-up plan in case of a collapse in demand for Treasuries.

This is the same guy who served during the Great Financial Crisis.

Nothing to see here...

[bob coleman @profitsplusid]

This is one of my most under appreciated posts but may have the largest impact on market activities.

Chicago is the epicenter of the US futures and options markets. Ask yourself, Why are there no more CFTC enforcement attorneys working in the Chicago office?

[Rep. Nancy Mace @RepNancyMace]

Hundreds of gallons of sulfuric acid ordered to a private residence raised no red flags?

[Thomas Massie @RepThomasMassie]

Within hours of the Epstein file release,

a superPAC funded by Israel-first billionaires Miriam Adelson, Paul Singer, and John Paulson, who himself appears in Epstein’s black book,

bought another $800,000 of TV ads against me.

I’ll still win, but if I lose, it was worth it.