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Few understand what Elon Musk’s companies are building right now.
• SpaceX is investing ~$100 Billion to build Starbase, Louisiana. Thousands of Starship launches per year. ~10,000+ jobs.
• @Tesla and @SpaceX building the world’s largest chip manufacturing facility: Terafab. It will produce over 1TW of AI compute capacity per year. These custom chips will be for Optimus, Cybercab, and space-based compute. Construction has begun.
• By the end of next year, SpaceX is targeting 10 GW of AI compute online with total power and cooling capacity aimed at ~20GW in Memphis.
• Tesla Optimus V3 will be starting production this year in Fremont. Future production at Giga Texas is expected to scale to 10M/units per year. Giga Texas factory is currently under construction.
• Tesla Cybercab production is underway, and launch is imminent. At scale, Tesla will produce over a million per year. Estimates from Ron Baron show each Cybercab earning ~$40K in profits every year.
• Tesla and SpaceX are each aiming for 100 GW per year of U.S. solar manufacturing capacity.
• SpaceX Gigasat factory in Bastrop, Texas will build the AI1 orbital data-center satellites (plus the solar cells, wafers, PCBs, and other components that power them). Construction is already underway.
• Starlink V3 Satellites have successfully
deployed and will scale. V3 delivers 10x the bandwidth of V2 Sats. With Starship capacity, total network capacity is expected to increase by over 100x.
• Starlink Mobile launching in 2027. Starlink Mobile V2 Satellites will launch from Starship, which will enable high-speed broadband data directly to unmodified phones. SpaceX will compete with AT&T, T-Mobile, Verizon, etc.
Hundreds upon hundreds of billions of dollars in construction is happening as we speak. The future is unfolding right in front of us.
I saw a post on Reddit today about AI that said, “AI is data, and data can only look backwards. Creativity looks forwards.” And I genuinely need to sit with the weight of that for a while.
study calculus.
not because you need to pass an exam.
because calculus teaches you how the world changes.
• derivatives → how fast something is changing right now
• integrals → how tiny changes accumulate into something larger
• differential equations → how systems evolve through time
• partial derivatives → how one variable changes inside a system with many moving parts
• gradients → which direction changes something fastest
• optimization → finding the best solution under constraints
then connect it to reality.
• velocity is the derivative of position.
• acceleration is the derivative of velocity.
• energy can be accumulated through integration.
• control systems are built around changing states.
• neural networks learn using gradients.
• physics is full of differential equations.
don’t memorize calculus as a collection of formulas.
draw it. simulate it. derive it. write code for it. connect it to motion and physical systems.
calculus becomes beautiful when you stop seeing x and y.
and start seeing change.
In 1948, a 32-year-old at Bell Labs published a paper nobody fully understood.
Engineers found it too mathematical. Mathematicians found it too engineering-focused. One prominent mathematician reviewed it negatively.
That paper - "A Mathematical Theory of Communication", became the founding document of the digital age.
The man was Claude Shannon. Father of Information Theory.
At 21, he wrote the most important master's thesis of the 20th century.
Working at MIT on an early mechanical computer, Shannon noticed its relay switches had exactly two states - open or closed. He had just taken a philosophy course introducing Boolean algebra, which also operated on two values: true and false.
Nobody had ever connected these two things.
His 1937 thesis proved that Boolean algebra and electrical circuits are mathematically identical, and that any logical operation could be built from simple switches.
Howard Gardner called it "possibly the most important, and also the most famous, master's thesis of the century."
Every digital computer ever built traces back to this insight.
At 29, he proved that perfect encryption exists.
During WWII, Shannon worked on classified cryptography at Bell Labs. His work contributed to SIGSALY, the secure voice system used for confidential communications between Roosevelt and Churchill.
In a classified 1945 memorandum, he mathematically proved the one-time pad provides perfect secrecy, unbreakable not just computationally, but provably, permanently, against an adversary with infinite power.
When declassified in 1949, it transformed cryptography from an art into a science. It laid the foundations for DES, AES, and every modern encryption standard.
At 32, he defined what information is.
His 1948 paper introduced one equation:
H = −Σ p(x) log p(x)
Shannon entropy. The average uncertainty in a probability distribution. The minimum bits required to encode a message.
Three things followed:
> He defined the bit - the fundamental unit of all information. His colleague John Tukey coined the name.
> He proved the channel capacity theorem, every communication channel has a maximum rate of reliable transmission. You can approach it. You can never exceed it.
> He unified telegraph, telephone, and radio into a single mathematical framework for the first time.
Robert Lucky of Bell Labs called it the greatest work "in the annals of technological thought."
Where his equation lives in AI today:
Cross-entropy loss - the function training every classifier and language model, is derived directly from H. Decision tree splits use information gain, which is H applied to data. Perplexity, the standard LLM evaluation metric, is an exponentiation of cross-entropy.
Every time a neural network trains, Shannon's formula runs inside it.
He also built the first AI learning device.
In 1950, Shannon built Theseus, a mechanical mouse that navigated a maze through trial and error, learned the correct path, and repeated it perfectly. Mazin Gilbert of Bell Labs said: "Theseus inspired the whole field of AI."
That same year he published the first paper on programming a computer to play chess. He co-organized the 1956 Dartmouth Workshop, the founding event of AI as a field.
The man:
He rode a unicycle through Bell Labs hallways while juggling. He built a flame-throwing trumpet, a rocket-powered Frisbee, and Styrofoam shoes to walk on the lake behind his house.
He called his home Entropy House.
When asked what motivated him: "I was motivated by curiosity. Never by the desire for financial gain. I just wondered how things were put together."
In 1985, he appeared unexpectedly at a conference in Brighton. The crowd mobbed him for autographs. Persuaded to speak at the banquet, he talked briefly, then pulled three balls from his pockets and juggled instead.
One engineer said: "It was as if Newton had showed up at a physics conference."
He died in 2001 after a decade with Alzheimer's, the cruel irony of information slowly leaving the mind of the man who defined what information was.
Claude, the AI model, is named after Claude Shannon, the mathematician who laid the foundation for the digital world we rely on today.
If you are a student reading this, I can only urge you to -
>Learn Mathematics
>Learn Physics
>Learn Mechanics
>Learn Electrical Circuits
>Learn AC/DC Systems
>Learn Electronics
>Learn Semiconductors & Silicon
>Learn Digital Logic
>Learn Signals & Systems
>Learn Control Theory
>Learn Communication Systems
>Learn Electromagnetics
>Learn Computer Architecture
>Learn Operating Systems
>Learn Programming
>Learn Data Structures & Algorithms
>Learn Embedded Systems
>Learn Real-Time Systems
>Learn Networking
>Learn Sensors & Instrumentation
>Learn Power Electronics
>Learn Motors & Actuators
>Learn Thermodynamics
>Learn Materials Science
>Learn Manufacturing
>Learn System Engineering
Basically, learn "How Things Actually Work". The money, shall follow.