Weekend Food For Thought WFFT
On Today's Menu: AI Shock vs The China Shock for the US, The Solar + Battery Atlas, The Great Reorg, Complex Adaptive Systems, and much more...
Hello from Paris,
I hope you had an interesting and productive week.
Carlo Rovelli stated that: “Quantum theory invites us to see the physical world as a new of relations…This is a radical leap. It is the equivalent to saying that everything consists solely of the way in which it affects something else”
Take Note…Look for the patterns of interconnections…
1 Getting Visual
2 If You Read One Thing Today - Make Sure it is This
3 Consequential Thinking about Consequential Matters
4 Big Ideas
5 Big thinking
6 Audacity
1 Getting Visual
Got the Minerals? A small group of countries controls the world’s largest reserves of many of the minerals that power modern economies. Australia holds the largest reserves of five major minerals, more than any other country in the dataset. China leads in rare earths and graphite reserves, giving it a major position in several strategic supply chains. Some minerals are heavily concentrated in a single country, including platinum-group metals in South Africa (83%) and phosphate in Morocco (69%).
Learn more: https://www.visualcapitalist.com/mapped-the-worlds-biggest-mineral-reserves/?mc_cid=49fd81c0f5&mc_eid=814179b7ef
K Street: Follow the Money - The U.S. government spends hundreds of billions of dollars each year on private-sector contracts, but a relatively small group of companies receives a large share of that spending. In fiscal year 2025, Lockheed Martin led all contractors with $73.7 billion in obligated spending, more than twice the amount awarded to the next-largest recipient. Defense firms dominate the ranking, accounting for eight of the top 14 contractors. Healthcare and IT companies also receive billions in federal spending through benefits administration, pharmaceutical distribution, and cybersecurity contracts.
Learn More: https://www.visualcapitalist.com/ranked-americas-biggest-government-contractors/?mc_cid=4438e33698&mc_eid=814179b7ef
K Shaped: “The K-shaped economy remains firmly intact. That’s according to our updated estimate of personal outlays by income group for 2026q1, based on the Fed’s Financial Accounts and Survey of Consumer Finance. Americans in the top 20% of the income distribution (those who earn over $175k annually) account for an astounding nearly 60% of outlays. The top 20% is driving spending and the economy. Their outlays increased by 6.5% over the past year and by 7.4% per annum over the past 3 years, well above CPI inflation of 2.7% and 2.9%, respectively. But outlays by those in the bottom 80% fell short of inflation. No wonder most Americans are upset with their financial situations and the broader economy. ICYMI, our estimates have come under some criticism. Fair enough. While we use a methodology devised by Fed researchers long ago, even with the modest adjustments we have made, these estimates may overstate the case. But they make an overwhelming case that the economy is K-shaped and becoming increasingly so. - Moody’s Analytics
Spotlight: Jevons at Play: AI Tokens demand appears elastic - As prices fall usage grows faster…
Big Picture: AI Scaling - The General Purpose Technology is scaling 3 times faster than any IT wave…
Home to AI - Texas leaping into top data centre hub…Texas is projected to overtake Virginia as the world’s largest data center market by 2030.
Texas’ Advantage: Cheap and abundant Energy: Wind and solar combined met 36-40% of Texas grid demand through the first nine months of 2025. Texas didn’t embrace renewables out of a love for protecting the environment and the climate. It just freed up the private sector to chase the cheapest electron and in doing so, outbuilt everyone else (except China). Texas’ energy abundance across both fossil fuels and renewables has positioned the state to dominate the next great industrial buildout: AI data centers.
Spotlight: Robotics: “Venture funding for robotics and physical AI set a record in the first quarter. Investors concentrated capital in companies that can manufacture at scale, win paying customers and cut the cost of deploying autonomous systems. Robotics startups raised $16.3 billion across 492 deals in Q1 2026, according to our recently released Q1 2026 Robotics and Physical AI report, up 154% from the previous quarter and 256% from a year earlier. Rounds for Shield AI, Saronic and Neura Robotics added about $5 billion. Strip those out, and investment still reached a record $11.3 billion. Exits are opening up. Robotics companies generated $6.2 billion in exit value, more than the previous five years combined, led by six public listings. The buyer pool now reaches past defense and industrial incumbents to Amazon, Mobileye, Symbotic and Bentley Systems, widening the paths to liquidity.” - Pitchbook
2 If You Read One Thing Today - Make Sure it is This
Agglomerations Substack takes a look at the lessons, right and wrong, of the so-called AI Shock vs the China Shock for the US. Go form your own informed opinions here:
Some Takeaways
“The China Shock refers to the widespread job loss that certain workers and communities suffered because of the rapid rise of trade with China in the 2000s.
The AI Shock refers to the same thing happening to white collar workers from the rise of Artificial Intelligence.
For now, the AI Shock is merely a potential shock. But a number ofeconomists, journalists, and other experts have drawn worrying parallels between these two shocks.
We understand why. Across a large body ofliterature, researchers have found that the China Shock had big and enduring impacts on the local economies and workers who were most exposed to it.
Even more concerning, the extent of the China Shock’s damage was a surprise to economists and other experts. It had long been known that expanding trade will produce winners and losers, but economists tended to think that jobless workers would adjust by moving to the better job opportunities created in a growing economy. Outmigration and flexible wages would restore balance to local labor markets, while many factories would be repurposed to make different products or provide services.
For too many communities, however, especially certain manufacturing towns in the Midwest and South, those adjustments never came. Jobs were lost and not replaced. Jobless people who stayed in their towns simply stopped looking for work. Health, crime, and family formation deteriorated.
Will AI do to laptop workers what the China Shock did to so many factory workers?
“…after a closer look at the details of the China Shock, combined with our understanding of workers exposed to AI, we arrive at a different conclusion.
The real lessons of the China Shock emerge not from its similarities with the AI Shock, but from its differences. And these lessons should mitigate concerns about a massive upcoming disruption, not exacerbate them.”
“The most China Shocked places had populations with lower than average education levels at the time, while the opposite is true for the most AI exposed.”
“We have presented the evidence for why the China Shock was far worse than the AI Shock is likely to be. What might the opposite case look like? We’re happy to play devil’s advocate, both because it’s a good intellectual habit and also because the case ends up looking so much weaker than ours.
One possibility is that the AI shock materializes much faster than the China Shock. If AI agents prove to be as capable as their creators suggest, displacing a worker whose primary tasks occur on a laptop could be as simple as installing and initializing the AI agent.
A second possibility is that the AI Shock ends up displacing a much bigger share of workers than we expect. In our most aggressive AI Shock scenario, 27 percent of workers would be exposed, 5.7 times larger than the 5 percent exposed to the China Shock. If most or all of these exposed workers lose their jobs — especially if, coinciding with the first possibility, they lose their jobs quickly — an utterly massive share of the labor force will find itself suddenly unemployed. The job losses and subsequent demand destruction could be enormous.
That’s the worst case scenario: AI hits faster and hits bigger than even pessimistic forecasts. We find this outcome unlikely, for two reasons.
First, rolling out AI at such scale requires significant computing hardware, which takes time to build. Data center construction is a real constraint and is already causing policy blowback. The prices of information processing equipment have also surged, rising at the fastest pace since 1959. The current AI buildout is already facing big obstacles. Imagine how much bigger they would be for the kind of buildout required to replace millions of workers.
Second, if the AI Shock were to produce vast job losses, the magnitude of the effect itself makes economic policy more powerful in offsetting the effects on demand. Recall that monetary policy, for instance, was of limited use for helping local economies destroyed by the concentrated China Shock. If the AI Shock proves to be bigger and more widespread, monetary policy regains in potency.
What is more, significant losses of jobs because of AI would be accompanied by rapid economic growth. Both economic growth and AI augmentation are helpful for smoothing the economic adjustment in a variety of ways, including boosting the demand for other types of workers.
Nevertheless, we admit that an AI Shock large enough to upend the labor market overnight would be unprecedented, which means that reasoning about it involves uncertainty in both positive and negative directions — about adjustment costs but also benefits, layoffs but also progress, unemployment but also innovation. The economy would be entering uncharted territory.
In such a world, comparisons to the China Shock, or to any other great historical shock, would therefore be pointless.”
A tale of two shocks
It is true that the impacts of the China Shock were more negative than many economists expected. But it is also the case that those negative effects are no longer a mystery. Thanks to a growing body of research, quite a bit is known about which types of people and places were more affected than others.
In sum, the China Shock was disruptive because it 1) disproportionately hurt workers with less education, 2) was geographically concentrated, and 3) inflicted its worst damage on places with lower levels of human capital.
It is still early days for AI and so a large dose of humility is appropriate. But so far as we can tell, the AI Shock is aimed at the kinds of people and places that actually weathered the China Shock just fine.
The old cliché is that history does not repeat itself but does often rhyme. If the AI Shock turns out to be as bad as the China Shock despite such different underlying characteristics, it would turn the cliché on its head: an instance in which history did repeat itself but skipped the rhyming. It’s not impossible, but we find this outcome unlikely.”
3 Consequential Thinking about Consequential Matters
The Electrotechnical Revolution Substack shares an overview of their new data tool: the Solar + Battery Atlas. Plenty of consequential thinking on consequential matters here - Go explore it in full here:
https://solarbatteryatlas.electrotech-revolution.com/deployment/scrollytelling/
https://solarbatteryatlas.electrotech-revolution.com/deployment/
Some Takeaways
“Solar is super abundant. Suitable land could generate around 125 times today’s electricity use; more than 90% of people live in places where local solar potential is at least 10 times current electricity demand.
Batteries turn solar into high-uptime power. With storage, solar can move from intermittent generation to firm, high-availability power: nine out of ten people live where solar-plus-battery systems can exceed 80% uptime, and in the sunniest regions they can reach 99% uptime. The main difference across regions is storage need and cost, not technical feasibility.
Solar plus battery is already cheap where most people live. Four in five people can get 80%-uptime power for under $100/MWh; for half of humanity it is under $80.
The opportunity is biggest where electricity is weakest. Around 760 million people still lack electricity, close to 2 billion have unreliable grids, and 95% of this demand sits in sunny regions where solar plus batteries can already beat planned fossil generation.
New fossil capacity is increasingly exposed. Of roughly 850 GW of planned coal and gas capacity, about 590 GW sits in regions where solar plus batteries can already deliver 80%-uptime power for under $100/MWh.
Costs will keep falling. By 2030, with projected solar and battery cost declines, solar-plus-storage power meeting the same uptime benchmark is likely to cost under $80/MWh for over 75% of people, and under $100/MWh for nine in ten.”
“For most of solar’s history, the argument against it was cost. Twenty-five years ago, a watt of solar cost about $5 – its power more than ten times the price of coal’s. A quarter of a century is a long time for a manufactured technology growing at double digits. Today that watt costs around 10 cents and the IEA now calls solar the cheapest electricity in history.
But what use is the world’s cheapest electricity if it vanishes at sunset? Having beaten every expectation on cost, solar now faces a harder question: reliability. Demand does not set with the sun. The engineer’s answer is storage: batteries to catch the day’s surplus and serve it back at night. Silos for surplus sunshine. And since batteries have fallen in cost just as solar did, what was once an engineer’s answer is becoming an economic one too. So how far can batteries go, at what cost, and where?”
“The problem is much larger than the 760 million people without electricity access. Another 2 billion people have a grid connection, but not reliable power. Their electricity is not available around the clock, and for about half of them, uptime is below 60%. That means daily outages, load shedding, voltage swings, damaged appliances, lost business hours, and expensive backup power.
For these people, the benchmark for solar plus battery reliability is not 100%, but the availability of the grid they have access to today. In many places, 80–90% uptime from solar plus batteries would be a major improvement, especially if it comes with lower delivered costs.”
“Today’s affordability map is therefore not fixed. As time moves on and electrotech costs continue to fall on learning curves, regions that are already cheap become cheaper, and regions just outside competitiveness cross the line. The global area where solar plus batteries can provide high-uptime electricity at low cost expands over time.
By 2030, our analysis suggests that more than three quarters of the world’s population can get 80% uptime solar-plus-storage power for less than $80/MWh, and 90% of the world for less than $100/MWh.
This matters for investment decisions being made now. Power plants are long-lived assets: a coal or gas plant planned today will be expected to operate for decades. But over that same period, solar-plus-storage is likely to continue moving down the cost curve. That means the comparison is not between a fossil plant and today’s solar-plus-storage costs; it is between a long-lived fossil asset and an alternative that keeps getting cheaper throughout its lifetime.
What is competitive today will become irresistible in the 2030s.”
Conclusion
For most of solar’s history, the question was cost. Could it ever be cheap enough to matter? That question has largely been answered.
The harder question is reliability. Can solar, with batteries, keep the lights on for enough hours of the year, in enough places, at a price that matters?
This analysis suggests it can. Sunlight is abundant across most of the world. Overbuilding turns cheap solar into surplus energy; batteries move that surplus into the hours when it is needed. Together, they can already deliver high-uptime power across much of the world, especially where demand is growing fastest and grids are weakest.
The last few percent of reliability do not overturn the case. They turn it into a system design problem: a small amount of backup, grid connection, demand flexibility, or complementary generation can cover the remaining hours.
And the economics are still improving. Solar and batteries are manufactured technologies, still moving down learning curves. A fossil plant planned today will run for decades; over that same period, the solar-plus-storage alternative is likely to keep getting cheaper.
The question is no longer whether the sun can power the world. It is how quickly we build the systems that let it.”
4 Big Ideas
This conversation builds on Foundation Capital’s recent essay on the great reorg (https://foundationcapital.com/ideas/the-great-reorg), which argues that AI’s full impact will only arrive when companies redesign how work gets done from first principles. Today, AI is already helping individuals work faster, but many organizations are struggling to translate that into gains at the team or company level. Azeem calls this problem “congestion.” When one part of the company speeds up, the next downstream process becomes the bottleneck. As AI takes on more work inside organizations, it reveals where they are too slow, too rigid, or too dependent on processes built around human constraints. Joanne and Azeem explore what it will take to build AI-native companies, from new human roles like system architects, validators, and accountability owners to new tools designed for agents rather than humans. For founders, Azeem argues that the most important metric may be cycle time: how quickly a company can learn from customers, ship, adapt, and repeat. The org chart is being redrawn, and the companies that thrive will be the ones that learn how to collaborate with agents instead of simply bolting AI to old workflows.
Watch it here:
What they covered:
00:00 Cold open: Why this moment favors startups
00:28 The great reorg, explained
02:16 AI’s productivity paradox
03:36 Congestion as the new bottleneck
06:30 What congestion looks like in practice
09:16 Bottlenecks across compute, healthcare, and drug discovery
13:44 The new human roles in AI-native organizations
15:33 The growing importance of accountability as agents do more work
22:09 The future of expertise
27:27 Are you managing the agents, or are the agents managing you?
31:28 What remains human in venture capital
33:08 Throwing out old assumptions about process
37:12 Designing for agents, not humans
39:19 Cycle time as a key AI-native metric
44:15 The mental model that stalls AI adoption in large orgs
48:27 Azeem’s advice for early-stage founders
51:19 New startup opportunities in the great reorg
55:08 What changes in the next three years
5 Big thinking
The FS Blog team shares some perspectives on complex adaptive systems - Provides plenty to ponder - go explore it here in full:
https://fs.blog/mental-model-complex-adaptive-systems/
Some Takeaways
“The interior of a car, at first glance is complicated. There are seats, belts, buttons, levers, knobs, a wheel, etc. Removing the passenger car seats would make this system less complicated. However, the system would remain essentially functional. Thus, we would not call the car interior complex.
The mechanical workings of a car, however, are complex. The system has interdependent components that must all simultaneously serve their function in order for the system to work. The higher-order function, driving, derives from the interaction of the parts in a very specific way.
Let’s say instead of the passenger seats, we remove the timing belt. Unlike the seats, the timing belt is a necessary node for the system to function properly. Our “driving system” is now useless. The system has complexities, but they are not what we would call adaptive.
To understand complex adaptive systems, let’s put hundreds of “driving systems” on the same road, each with the goal of reaching their destination within an expected amount of time. We call this traffic. Traffic is a complex system in which its inhabitants adapt to each other’s actions.”
“…cars will slow to observe the wreck. As the first cars slow, the cars behind them slow in turn. The cars behind them must slow as well. With everyone becoming increasingly agitated, we’ve got a traffic jam. The jam emerges from the interaction of the parts of the system.
With the traffic jam formed, potential entrants to the jam—let’s call them Group #2—get on their smartphones and learn that there is an accident ahead which may take hours to clear. Upon learning of the accident, they predictably begin to adapt by finding another route. Suppose there is only one alternate route into the city. What happens now? The alternate route forms a second jam!”
“The key element to complex adaptive systems is the social element. The belts and pulleys inside a car do not communicate with one another and adapt their behavior to the behavior of the other parts in an infinite loop. Drivers, on the other hand, do exactly that.”
“Like traffic, the complex, adaptive nature of the market is very clear. The participants in the market are interacting with one another constantly and adapting their behavior to what they know about others’ behavior. Stock prices jiggle all day long in this fashion. Forecasting outcomes in this system is extremely challenging.
To illustrate, suppose that a very skilled, influential, and perhaps lucky, market forecaster successfully calls a market crash. (There were a few in 2008, for example.) Five years later, he publicly calls for a second crash. Given his prescience in the prior crash, market participants might decide to sell their stocks rapidly, causing a crash for no other reason than the fact that it was predicted! Like traffic reports on the radio, the very act of observing and predicting has a crucial impact on the behavior of the system.
Thus, although we know that over the long term, stock prices roughly track the value of their underlying businesses, in the short run almost anything can occur due to the highly adaptive nature of market participants.”
“Failure to use higher-order thinking when considering outcomes in complex adaptive systems is a common cause of overconfidence in prediction making”.
6 Audacity
Have a Great weekend when You get to that stage,
Sune