Inevitability Weekly #6
AGI, Robotics, Everything being Computer and More
From Farmer to Banker by Alfred Lin
Technology is simply applied knowledge. Tools spread because they deliver real value: saving time, lowering costs, extending life, and expanding capability. You can slow adoption through regulation or social pressure, but you cannot suppress tools that materially improve human outcomes and expand human potential.
Everything is Computer by Ryan McEntush
https://a16z.com/everything-is-computer/
We no longer live among truly distinct technological paradigms, but within a world of variations on one single idea: the smartphone, endlessly turned inside and out and scaled across every domain. Everything is a smartphone. Everything is computer.
Consumer electronics underpin all of the most important technologies of our time. And everything, absolutely everything, is based on the indispensable paradigm of the smartphone. An electric vehicle is a smartphone with wheels. A drone is a smartphone with propellers. A robot is a smartphone that moves. The differences matter, of course, but the family resemblance is impossible to ignore.
This explains why so many electronics companies — especially in China — now appear to be building many different types of products at once. From the outside, this looks like reckless sprawl. From the inside, it is straightforward reuse. The products change; the components do not. A company that builds smartphones at global scale already understands batteries, sensors, compute, thermal management, wireless stacks, and high-volume manufacturing. To build electric vehicles, it doesn’t need much more than that.
Everything that can be electrified will be electrified, because electric systems are the native substrate for code. Power electronics become the transmission, motors become the engine, and software becomes the differentiator. Across land, sea, and air, mobility is shifting to battery-electric and hybrid architectures. Rockets are the lone exception — chemical propulsion still dominates on thrust-to-weight ratio — but even these systems are increasingly going electric. Starship carries hundreds of kilowatts of power electronics and the same Tesla batteries you’d find in a Model 3 or Powerwall. The rocket equation stays chemical; everything around it becomes electrical
Today, the United States lacks serious modular middle firms that bind together the electronics ecosystem. Exceptional figures like Elon Musk have succeeded despite this by aggressively plugging into global supply chains and then pulling critical modules in-house over time. But that is not a reproducible strategy for the world ahead. We cannot make national success contingent on finding a hundred more Elons able to vertically integrate down to a screw. If we want the default choice to be American — fast, competitive, and reliable — we need to rebuild the missing layer. Reclaiming our electro-industrial future starts with building an American modular middle.
Standing Out in 2026 by Lulu Cheng Meservey
In 2025, the din of direct-goers turned into cacophony as even the stodgiest of companies started factory-farming for “CEO content” and “storytelling.” The edge now came from winning attention, primarily through video. Cue the avalanche of corporate podcasts and cinematic launch videos. Some built quality dedicated audiences while others, in blind Icarian pursuit of engagement, went the way of Talk Tuah.
In 2026, narrative alpha will come from doing real things.
That means:
Putting in real effort to create beautiful durable things that exhibit taste and craftsmanship, with zero tolerance for slop. As Thomas Paine wrote during the American Revolution: “What we obtain too cheaply we esteem too lightly; it is dearness only that gives everything its value.”
Showing real evidence for real outcomes, not flashy claims or vagueposts
Designing real world events and artifacts, leaving people with memories that far outlast the cheap “impressions” generated by brainrot content troughs
Showing up as real humans, with real flaws and foibles, instead of ultra-polished personas following AI scripts
Forming real relationships that will weather time and tide
Many Small Steps for Robots, One Giant Leap for Mankind by Packy McCormick and Evan Beard
If you believe there’s a massive set of economically valuable tasks waiting on the far side of some threshold, then the optimal strategy is to straight-shot it. Lock your team in the lab. Scale models. Scale compute. Don’t get distracted by deployments that might slow you down. Leap.
If you believe, like we do, that there is a continuous spectrum of economically valuable jobs, many of which robots can do today, then the best thing to do is to get your robots in the field early and get to work.
Each deployment teaches you where you are on the gradient. Success shows you what’s stable, failure shows you where the model breaks, and both tell you exactly what to work on fixing next. You iterate. You take small steps.
It’s widely accepted in leading LLM labs that data is king. The optimal data strategy is to climb this spectrum one use case at a time. You don’t need “more” data. What you really want is diversity3, on-policyness4, and curriculum5. Climbing the spectrum iteratively is the strategy that best optimizes for these three dimensions of good data for any given capital budget. Real deployments on your bots get you on-policyness (nothing else can), the market intelligently curates a curriculum, and both provide rich and economically relevant diversity.
We’ve learned this lesson over years of deployments.
Whenever robotics evolves to incorporate another aspect of the job spectrum between automation and autonomy, it also unlocks another set of jobs, another set of customers, another chunk of the market. One small step at a time.
Take screwdriving. It is much easier to use end-to-end AI to find a screw or bolt than to try to put everything just so in a preplanned and fixed position. Search and feedback is cheap for learning systems. Our robot can move the screwdriver around until it feels that it’s in the right place. It wiggles the screwdriver a little. It feels when it drops into the slot. If it slips, it adjusts. And when our robots figure out how to drive a screw, it unlocks a host of jobs that involve screwdriving. Then we start doing those and learn the specifics of each of them, too.
We learn on the job and get better with time. Many of these robots are imperfect, but they’re still useful. There’s no magic threshold you have to cross before robots become useful.
Vanity Fair Piece About TBPN
https://www.vanityfair.com/news/story/the-technology-brothers-have-silicon-valley-in-their-thrall
In an era when AI researchers are being poached in 10-figure deals that make NBA free agent negotiations look like child’s play, Coogan and Hays cover the AI talent wars like ESPN analysts. They count on the fact that their audience can keep up with the inside baseball. “A lot of other shows would say Amazon Web Services, Amazon’s cloud computing business,” explains Coogan. “We just say AWS.”
A great piece about TBPN. I’m a huge fan of them and also got a little shoutout in this piece.
What if they could bottle the energy of a Silicon Valley insider group chat? They liked being able to bounce ideas off each other without things getting too crowded—plus they could juice their networks for a call-in show dynamic, even booking big-name guests over DM while live on camera.
In an early video for YouTube, they printed out a post by a popular anonymous X account, @NetCapGirl, and analyzed it on camera. She couldn’t resist retweeting their video, getting them free exposure to 100,000 new viewers. Onto the next viral account. Rinse, repeat. “Our first growth hack,” Coogan tells me.
2026: This is AGI by Pat Grady
The AI applications of 2023 and 2024 were talkers. Some were very sophisticated conversationalists! But their impact was limited.
The AI applications of 2026 and 2027 will be doers. They will feel like colleagues. Usage will go from a few times a day to all-day, every day, with multiple instances running in parallel. Users won’t save a few hours here and there – they’ll go from working as an IC to managing a team of agents.
Today, your agents can probably work reliably for ~30 minutes. But they’ll be able to perform a day’s worth of work very soon – and a century’s worth of work eventually.
What can you achieve when your plans are measured in centuries? A century is 200,000 clinical trials no one’s cross-referenced. A century is every customer support ticket ever filed, finally mined for signal. A century is the entire U.S. tax code, refactored for coherence.
The ambitious version of your roadmap just became the realistic one.