THIS is the future of creating movies with AI. I suspect that within a year or so I will be turning Quick Silver into a movie.
Thursday, April 30, 2026
Wednesday, April 29, 2026
Tuesday, April 28, 2026
Monday, April 27, 2026
Sunday, April 26, 2026
Peter Diamandis on AI conquering science
From Deep Blue to Move 37 to the AI Science Factory
Most people remember Deep Blue’s victory over Garry Kasparov in 1997. It was a brute-force triumph: the machine evaluated 200 million moves per second and simply out-calculated the greatest chess mind alive. It was impressive. It was also, in hindsight, primitive.
The real breakthrough came nineteen years later, when DeepMind’s AlphaGo defeated world Go champion Lee Sedol. Go has more possible board positions than atoms in the observable universe… you cannot brute-force it. AlphaGo had to learn something deeper: strategy, intuition, creativity.
And then came Move 37.
In Game 2 of the match, AlphaGo played a move so unexpected, so alien to conventional Go wisdom, that the commentators fell silent. Lee Sedol left the room for fifteen minutes. No human in the 2,500-year history of Go had ever played that move. It wasn’t merely correct, it was brilliant.The machine had discovered a creative strategy that humans had never imagined.
That single moment changed the trajectory of AI research. It proved that artificial intelligence could do more than optimize within known frameworks. It could discover entirely new knowledge.
Now ask yourself: what happens when that same capability is applied not to a board game, but to the entirety of science?
The Scientific Method at Machine Speed
Here is what’s happening right now, in 2026:
AI systems are running the scientific method autonomously, at machine speed, around the clock, and across every domain of science simultaneously.
These aren’t chatbots that help researchers write papers. These are autonomous agents that generate hypotheses, design experiments, operate physical laboratory equipment, analyze results, and iterate. All without human intervention. They are doing science the way AlphaGo played Go: by exploring a possibility space so vast that no human team could cover it in a thousand lifetimes.
Consider what DeepMind has already accomplished. AlphaFold predicted the 3D structure of virtually every known protein (over 200 million of them) solving a problem that had stumped biologists for fifty years. That work earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry. Their latest system, AlphaEvolve, recently had its own “Move 37 moment” when it discovered a novel method for matrix multiplication: a fundamental mathematical operation that underlies all of modern AI. No human mathematician had found it.
AlphaFold was just the beginning…
Lila Sciences: Building the World’s First AI-Driven Science Factory
One company that is leading the charge into scientific superintelligence is Lila Sciences (full disclosure, I’m an investor), founded by Flagship Pioneering, the same venture creation firm that built Moderna. Led by CEO Geoffrey von Maltzahn, Lila is building what they call “AI Science Factories”: fully autonomous laboratories where AI systems generate hypotheses, design experiments, operate lab equipment, analyze results, and iterate at machine speed with minimal human intervention.
What makes Lila extraordinary is scale. Their AI has accumulated over 10 trillion tokens of scientific reasoning data, generated entirely by AI models reasoning through the scientific method against experimental results. For context, the usable subset of the internet for training LLMs is roughly 15 trillion tokens. By the end of 2026, Lila’s scientific reasoning dataset will exceed twice the size of the internet used to train frontier LLMs. All of it is original scientific thought.
And crucially, Lila trains across all scientific domains simultaneously: life sciences, chemistry, materials science, energy. This matters because many of history’s greatest breakthroughs came from cross-domain insights. Penicillin was discovered by a biologist who noticed something strange about a mold. CRISPR was found by microbiologists studying bacterial immune systems. The transistor emerged from quantum physics applied to materials science.
AI systems that train across all of science simultaneously can find these cross-domain patterns at a scale and speed no human team can match. Lila calls these discoveries “Move 37 moments, — and they report that they’ve been happening across every domain since late 2025.
Lila’s AI, training on just 2% of available scientific data, already outperforms leading AI models (including the latest Claude Opus and GPT-5 models) across materials science, chemistry, and life sciences.
The Results Are Already Incredible
The early demonstrations of scientific superintelligence are producing results that would have seemed impossible just two years ago…
In mRNA therapeutics, Lila’s AI used a self-play approach (essentially playing a million games of mRNA design against itself, the way AlphaGo played millions of Go games), and achieved performance that is twice as effective as current mRNA technologies from the leading pharmaceutical companies. Expression lasting 15 days versus the 1.5 days achieved by conventional approaches.
A 10x improvement.
And in CAR-T cell therapy, one of the most promising frontiers in cancer treatment, an AI-driven program invested $3 million and six months to develop a therapy that outperformed a competing approach that was recently acquired for $2.1 billion based on traditional methods. The AI system explored 300,000 design variants. The traditional approach tested 13.
Read that again. Three million dollars (Lila) versus two billion (everyone else). Three hundred thousand variants (Lila) versus thirteen (everyone else). And the cheaper one won.
This is what happens when the scientific method compounds at machine speed.
The Bitter Lesson Applied to Science
There is a famous concept in AI research called “the bitter lesson,” articulated by Rich Sutton in 2019. The lesson is this: across the entire history of artificial intelligence, the approaches that ultimately win are not the ones that try to build in human knowledge, but the ones that leverage massive computation and learning.
Every time researchers tried to hand-code human expertise into AI systems, they were eventually outperformed by systems that simply learned from vast amounts of data. Chess, Go, protein folding, language… the pattern is always the same. Scale wins.
The bitter lesson is now applying to science itself. Narrow AI systems trained on a single domain are being outperformed by broad systems that train across all scientific domains simultaneously. Lila’s approach (training one unified intelligence across biology, chemistry, materials science, and more) is proving that the bitter lesson holds in the physical world, not just the digital one.
The Claude Code Moment for All of Science
If you follow AI, you’ve seen what happened when coding assistants like Claude Code, Cursor, and GitHub Copilot transformed software development. Suddenly, a single developer with an AI assistant could do the work of a team. Productivity wasn’t merely improved, it was transformed by an order of magnitude.
We are about to witness the same transformation across all of science.
Every scientist will soon have an AI collaborator that can search the entire scientific literature in seconds, generate novel hypotheses, design experiments, simulate outcomes, and iterate. All before the human scientist finishes their morning coffee. The question will not be “Can AI help with research?” It will be “How did we ever do research without it?”
And just like the software revolution, this won’t replace scientists. It will amplify them. The scientists who learn to collaborate with AI will produce breakthroughs at a rate that would have seemed impossible a few years ago. Those who refuse to adapt will find themselves working at a pace that’s no longer competitive.
Why This Matters for All of Us
This isn’t only a story about science itself. It’s a story about everything science touches, which is everything.
Medicine: Drug development that currently takes a decade and costs $2.4 billion per approved drug could be compressed to months at a fraction of the cost. Diseases we consider incurable today will face an onslaught of AI-designed therapies tested at a scale previously unimaginable.
Energy: New materials for solar cells, batteries, and nuclear fusion are being discovered through autonomous experimentation at ten times the speed of conventional research.
Materials: AI-designed materials with properties we’ve never seen before (stronger, lighter, more conductive) will transform manufacturing, construction, aerospace, and electronics.
Agriculture: AI-optimized crop varieties and agricultural processes will increase yields while reducing environmental impact.
So, what’s the the key point?
That we are on the verge of solving everything, in the fashion that Alex Wissner-Gross and I wrote about in our paper www.SolveEverything.org.
The Compound Interest of Knowledge
Charlie Munger once said: “The first rule of compounding is never interrupt it unnecessarily.”
The scientific method is itself a compounding phenomenon. Each discovery builds on previous discoveries. Each experiment generates data that improves the next experiment. Knowledge compounds.
Until now, the rate of compounding has been limited by human speed: how fast we can read papers, design experiments, run tests, and analyze results. AI removes that bottleneck. When the scientific method runs at machine speed, with machine-scale breadth, the compounding accelerates by orders of magnitude.
We are about to see more scientific progress in the next 5 years than in the previous century. Not because scientists suddenly became smarter, but because the tool they’re using to do science became superintelligent.
Deep Blue beat a chess champion. AlphaGo made a move no human had ever conceived. AlphaFold won a Nobel Prize. And now, in 2026, AI is making its Move 37 in every field of science simultaneously.
The Deep Blue moment for all of science is here.
Friday, April 24, 2026
Quantum Computers Just Confirmed Something "HORRIFYING" About Reality
Not horrifying at all, but evidence that there is an infinite number of universes where every decision’s possible outcomes exist.
https://www.youtube.com/watch?v=t_W2v6BQyK8
Too long, though.
Thursday, April 23, 2026
Tuesday, April 21, 2026
Monday, April 20, 2026
How AI Will "Feel" in 2 Years
By Peter Diamandis:
1. YOU WILL GIVE YOUR AI ACCESS TO EVERYTHING IN YOUR LIFE… I MEAN EVERYTHING
For 15 years, I’ve been talking about the day every human gets their own version of JARVIS: the AI from Iron Man that knows everything about Tony Stark and orchestrates his life in real time.
Inside of the next two years you’ll give your personal AI (your own JARVIS) access to ALL your data, all your interactions and your desires. The benefits from this level of disclosure will be so great that privacy concerns will dissolve… because the value is simply too enormous to refuse.
This week I massively upgraded Skippy’s capability and utility (Skippy is my OpenClaw agent). Powered by Opus 4.6 on two Mac Studios, sitting on top of Kimi K2.5. Connected Skippy to EVERYTHING possible… iMessage, WhatsApp, Google Drive, Calendar, email, Granola… and all of my files set up within an Andrej Kaparthy-style second-brain. The increase in “Feeling AGI” was palpable… and it’s only going to accelerate, which is the purpose of today’s newsletter.
Here’s the list of what your AI will consume…
· Listen to every phone conversation you have.
· Access to all your emails, every text, all meeting notes.
· Visual/auditory data feeds from your smart glasses.
· Every camera inside your home and every sensor on your body: continuously being monitored.
· Every calendar entry and every preference.
· Your financial transactions, investment portfolios, and spending patterns.
· Your genome, your blood work, your sleep architecture: every biological signal.
When your AI has all of this data and context, it stops being a chatbot and starts being a chief of staff.
Imagine this: You finish breakfast with your family and head towards the front door. Your AI knows your schedule and has already seen you moving toward the exit. Before you step outside, a Cybercab is already waiting in your driveway.
But here’s where it goes from convenient to auto-magical: your AI cross-referenced your Oura ring sleep data and knows you slept poorly, so it summoned a Cybercab configured with a reclined seat for a restorative nap on your commute.
You never asked for it. You never even thought about it. The world simply conformed itself to you. That’s the shift.
AI stops being a tool you reach for and becomes an invisible layer that anticipates you: making decisions on your behalf that you didn’t even know needed to be made.
Here’s another one: You’re preparing for a board meeting tomorrow. Your AI has already read every document in the shared drive, summarized the three contentious items, drafted your talking points based on your stated positions, and pre-scheduled a 10-minute call with your CFO because it detected a discrepancy in the Q2 numbers that you’ll want resolved before the meeting. You wake up and it’s all waiting in your briefing. No prompt. No request. Just done.
The shift isn’t from an “okay AI” to a “better AI.”. It’s from AI you talk to, to AI that acts on your behalf, before you even think to ask.
So, what does this mean? The winners of the next decade won’t be the apps with the best UIs. They’ll be the agents with the most trusted access to your data. The smartphone era rewarded app design. The agent era rewards context depth – and trust.
2. YOUR ENVIRONMENT STARTS ADAPTING TO YOU
Right now, you walk into a room and tweak it to your needs: adjusting the thermostat, the music, the lighting. In the very near future, your environment will magically adjust itself to your desires.
Sensors in your home, combined with continuous biometric feeds from your wearables, mean your home and workplace knows what you need before you do.
Music shifts based on whether your heart rate variability says you’re stressed or flowing.
Temperature nudges based on your metabolism.
Lighting moves from cool blue in the morning to warm amber two hours before your biological bedtime.
Did you have a stressful day? Your AI starts playing your favorite comedian on the TV as you walk through the door.
Your bedroom detects you’re approaching sleep, dims every screen in the house,
locks the doors, and shifts your phone to “Do Not Disturb,” all as you brush your
teeth.
And then there’s food, which may be the most underestimated transformation of the next 3-4 years.
Today we snack, we eat what we like. But what about a future in which your AI partners with your kitchen robot to feed you exactly what your body needs?
Your future meals are not based on a whim. They’re optimized specifically for your physiology, in that moment: your taste profile, your current blood chemistry (hydration, protein levels, vitamin levels) and balancing any nutrient deficit.
Your AI knows you have an upcoming workout, and adds extra protein and creatine to your lunch, again automagically.
No menu. No guessing. No asking. The meal shows up dialed in to the biochemistry of the person eating it.
Your home becomes a biological dashboard. Your environment becomes a real-time response to your body’s needs. A living space that literally keeps you healthier.
3. AUGMENTED REALITY CHANGES EVERYTHING
This is one element of what’s coming that most people underestimate.
Smart glasses (from Meta, Apple, and a wave of startups) are about to make augmented reality feel the way smartphones felt in 2010: inevitable. And once the display layer is live, everything about how you move through your day changes.
Travel: Interested in the history of a city while on vacation? Walk down the streets of Rome and your AI overlays historical imagery on every building you pass. The Forum reassembles itself in your field of view. The name and story of the artist whose sculpture you just walked past hovers in mid-air. The language on the menu in front of you translates before you blink.
Shopping: Shopping collapses in on itself, going from slow and frustrating to fast and fun. Imagine the following… You’ve been invited to a friend’s June wedding in Long Island, New York. Your AI knows the current fashions, the temperature that day and your budget. Without prompting, your version of JARVIS spins up a runway fashion show before your eyes. A dozen avatars of you, wearing different outfits, parade before your eyes. You pick one. It ships. Of course, it fits perfectly because your AI maintains a continuously updated 3D model of your body.
Education: Education transforms just as radically. Your child looks at a math problem through their glasses, and their AI tutor doesn’t just solve it… it identifies the specific concept they’re stuck on, generates a visual explanation calibrated to their learning style, and connects it to something they care about. A kid who loves basketball suddenly understands parabolic arcs. A child fascinated by Minecraft intuitively grasps geometry. Every child gets a world-class private tutor, 24/7, for free.
So, what does this all mean? Historical overlays everywhere you walk. Menus, signs, labels translated in real time. Commerce that happens without any interruption pattern: no ads, no pop-ups, no friction.
The “attention economy”—the entire scaffolding of advertising, interruption, and persuasion that funds today’s internet—starts to collapse. The new economy is built around agents making purchases on your behalf, not ads nudging you toward them.
4. AI GETS PHYSICAL: ON YOUR STREET, IN YOUR HOME
For the last few years, AI has been mostly a digital experience on your phone or computer screen. By 2028, AI will be walking out of the digital realm and into your driveway, your kitchen, and your workplace.
Autonomous vehicles go mainstream
At least five autonomous vehicle companies are already operating or actively testing on the streets of major U.S. cities. Waymo is already serving millions of rides. Tesla’s Cybercab is rolling out. Zoox (by Amazon), Volkswagen’s autonomous ID. Buzz (partnered with Uber), and Uber/Nvidia’s L4 platform expanding to 28 cities by 2028 are right behind them.
The generation of humans who will never need a driver’s license is already in elementary and middle school.
Humanoid robots move in
This is one that still sounds like science fiction, but it won’t for much longer. Figure, Tesla’s Optimus, 1X’s Neo, Unitree, Apptronik. These companies are shipping. And unit economics are collapsing.
The first humanoid robots are already working in warehouses, factories, and pilot homes: unmodified, no special infrastructure required.
You’ve heard me speak about Optimum (Tesla), Figure and Neo (1X) extensively, but there are many more. Recently, Apptronik just raised $520M at a $5B+ valuation with Google DeepMind as a backer.
In China, Unitree has taken the lead position. The company has filed for a Shanghai IPO, seeking to raise about ~$610 million to fund AI model development, new robot platforms, and manufacturing expansion. The Hangzhou-based humanoid/quadruped robot maker posted a 674% jump in adjusted net profit to $90M in 2025, becoming the world’s top humanoid robot seller.
Within 36 months, a humanoid robot in your kitchen will feel as normal as a smartphone on your counter.
The “AI hype” narrative ends the moment the robot cleans your kitchen while the Cybercab waits in the driveway. We’re 18 to 24 months from that exact picture.
5. YOUR HEALTH BECOMES A 24/7 AI-COACHED OPERATION
The annual physical is already obsolete. It’s just that most of the healthcare system hasn’t caught up yet.
Right now, the average biohacker is carrying three to four continuous health data streams: an Oura ring or Whoop, an Apple Watch on the wrist and a continuous glucose monitor on their arm. That’s over 100 biometrics a day, every day, feeding into an AI that can see patterns no human clinician ever could.
What this enables, in the next 24 months, is a shift from reactive healthcare (”I feel bad, I go to the doctor, they run tests”) to continuous optimization: “my AI notices something drifting and nudges me before I ever feel it.”
You just sat on a Zoom call for 90 minutes. Your AI pings you: “Do 20 squats your glucose is trending up and movement now saves you a spike.”
You’re about to take the elevator. Your AI suggests the stairs because your Zone 2 minutes are low this week.
Your hydration score is drifting. Your AI tells you to drink 16oz before your 2pm meeting because it already knows how you present when dehydrated.
Your AI detects a subtle shift in your heart rate variability pattern over the past 72 hours, cross-references it with your recent travel and sleep data, and recommends you take a specific anti-inflammatory supplement and schedule a blood draw — three weeks before you would have noticed anything was off.
You’re about to order your third coffee. Your AI gently intervenes: “Your cortisol is already elevated from this morning’s meeting. Switch to green tea, you’ll actually focus better.”
This is not a wellness app. It’s a preventive medicine engine operating on you, continuously, forever.
In 10 years, we won’t go to the doctor when we feel sick. We’ll be continuously optimized, and the rare visit will be for something AI flagged before we knew it existed.
THE BROADER PICTURE
Here’s the meta-pattern underneath all five of these shifts:
Today, AI is an app. Something you open. Something you prompt. Something you use.
In the next 2 to 3 years, AI becomes a ubiquitous, always on, always enabling. Something that surrounds you. Something that acts on your behalf. Something you stop noticing… the same way you stopped noticing electricity in the walls of your home.
And that’s the real signal. Transformative technologies eventually disappear into the background of life. The telephone was miraculous in 1900 and invisible by 1950. The internet was astonishing in 1995 and ambient by 2010. AI will follow the same arc, but compressed into a fraction of the time.
Here’s the question for you: are you positioning yourself to thrive in this new era, or scrambling to catch up?
Are you going AI-first in every part of your life? Are you connecting your data so your agents can actually serve you? Are you experimenting with the tools that will feel primitive in 18 months but will look like genius in hindsight for being early?
The next 24 months are going to feel, to anyone paying attention, like the single largest shift in the daily experience of being alive since the smartphone. Position yourself accordingly.
To an Abundant (and AWESOME) future,
Peter
Proof of Abundance… And How to Survive It
By Peter Diamandis:
1. Renewables Hit the Halfway Mark
Renewables just crossed 49.4% of global electricity capacity. Let me say that again: nearly half of all electricity generation capacity on Earth is now renewable. Solar drove 75% of new additions, bringing the total to 5.15 terawatts. We’re at the halfway mark and the curve is accelerating. This isn’t some future projection. This is today. The energy transition is already here.
2. Lithium Battery Prices:
In 1991, a lithium battery cost $10,000. And today? Less than $100. That’s a 99% price drop. Remember all those conversations about whether we could afford enough batteries to electrify transportation? Those are ancient history. The market solved it. Scale solved it. Technology solved it. And we’re not done: new battery chemistries are coming that will drive costs even lower. Every electric vehicle on the road is proof that Abundance doesn’t require central planning. It requires innovation and competition.
3. Lab-Grown Diamonds Below $1,000
The average price of a two-carat lab-grown diamond has fallen below $1,000: down 80% since January 2020. Compare that to a natural diamond at $22,000 to $28,000 for the same size. So much for De Beers. And yes, De Beers’ 3-months-salary campaign was one of the most successful PR brain-washings in history. They convinced generations that scarcity equals value. But technology doesn’t care about marketing. Lab-grown diamonds are chemically identical, optically perfect, and produced without child labor. Abundance wins.
4. AI Created 640,000 New Jobs
AI created 640,000 new jobs in the United States between 2023 and 2025, mostly in categories that didn’t exist three years ago. Not replacing jobs. Creating jobs. This is the pattern we’ve seen with every major technology transition: short-term disruption, long-term expansion. The printing press, the steam engine, electricity, the internet – all of them were supposed to end work. All of them created more opportunity than they destroyed. AI is no different. The question isn’t whether jobs will exist. It’s whether you’re building skills for the new categories or clinging to the old ones.
5. Robots + AI = Energy Abundance / The Maximo Robot
I LOVE this story… four Maximo robots are installing 100 megawatts of solar capacity in the California desert at one panel per minute. Think about that for a second. Robots deploying renewable energy autonomously, at scale, faster than humans ever could. Once you get robots, energy, and AI all reinforcing one another, Abundance stops being theoretical. It becomes mechanical. Inevitable.
And this isn’t just a Western phenomenon. Pakistan is now generating most of its energy via solar. Solar is exploding across Africa. This is global. This is real. This is happening whether you’re paying attention or not.
The Pattern
Abundance is a pattern across multiple domains: Materials. Manufacturing. Employment. Computation. Every one of them follows the same trajectory: exponential improvement, collapsing costs, expanding access. That’s not coincidence. That’s the signature of our exponential times, the signature of increasing Abundance.
But here’s perhaps the most important question:
How does society design institutions that distribute this Abundance in a reasonable way?
Technology creates Abundance. Institutions decide who captures it. Markets, governments, legal frameworks: those are the systems that determine whether Abundance pools at the top or spreads broadly. We have the technology.
Do we have the wisdom to build institutions that match it?
The Choice
Abundance isn’t coming. It’s in the data, right now. Renewables at 50% capacity. Batteries down 99%. Diamonds at $1,000. AI creating jobs. Robots building infrastructure.
The only question is whether you see it or whether you’re still watching the Crisis News Network.
I choose to see it. I choose to build for it. I choose to believe that we’re living through the most extraordinary moment in human history – not in spite of the challenges, but because of them.
This is what breakthroughs look like. This is what the future arriving ahead of schedule looks like. And if you’re paying attention, you can see it everywhere.
- Peter
Wednesday, April 15, 2026
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Tuesday, April 7, 2026
Sunday, April 5, 2026
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