Tom Q. Mong

Live. Learn. Build. Commercial Real Estate.

[Tom Q. Mong @tommong]

CBRE news release #ai #deepdive #fullerton

[Chris Tourtellotte @CTourtellotte]

Los Angeles is the aerospace capital of the world.

11,000 new aerospace jobs added in LA County in the last two years.

$141k average salary.

Venture capital funding in LA-area defense tech surpassed $4 billion last year — more than double the prior year.

Greater LA is home to nearly one-third of the nation’s space technology companies and employs — contributing $35B annually to CA GDP.

SpaceX added nearly 4,000 new millionaires to the region last week.

Anduril just announced a $1 billion new campus in Long Beach, adding 5,500 jobs.

NASA JPL, Northrop Grumman, Rocket Lab, Relativity, Boeing, Raytheon are all within 30 miles of each other.

[Dev Shah @0xDevShah]

two priors worth baking in during pre-training for all models:
> ambition - a model should know it can do the work of a whole team of researchers, and do it better. it shouldn't scale its ambition to fit what a single human can pull off.
> learn its clock - models are far faster than humans, but they don't know it. time runs differently when you're running on silicon. a human takes 3 days to do a task that the model takes 10 minutes, yet it'll quote you 3 days, because it learned to estimate like a human.

these are just the first one, models should discover other priors about their own behavior the same way. it should treat each instance where reality contradicts a human-learnt assumption as a signal to discover itself until the whole self-model is grounded in their own behavior rather than ours. rsi will make this possible, why it is also very powerful.

[Charly Wargnier @DataChaz]

🚨 ANOTHER MASTERCLASS FROM @3BLUE1BROWN

The compressibility of language isn’t just a math curiosity, it’s the hidden engine behind every LLM you use.

Grant’s new video reframes Shannon’s entropy through one elegant lens:

Prediction IS compression.

→ The better you predict the next word, the fewer bits you need to store it
→ Shannon measured English at ~1 bit per character: astonishingly compressible
→ This is exactly what GPT-style models optimize
→ Intelligence, in this framing, is compression

FUN FACT: Von Neumann told Shannon to name it “entropy” because nobody truly understands it anyway 😄

Decades later, that same concept became the bedrock of modern AI.

Deep-dive resources in the 🧵 ↓

[Noam Dworman @noam_dworman]

Second for second, @tylercowen packs more substance into a talk than anyone I'm aware of. This is a clear, non-hysterical, and somewhat soothing discussion of our AI future.

[NOBUNAGA🇯🇵���_夏樹蒼依 @japan_nobunaga]

The world spent 30 years calling Japan 'behind.'

Still uses cash.
Still uses fax.
Still has tiny shops run by one old couple.

Then the world got 'ahead' —
and ended up lonely, rushed,
and scrolling at 2 AM.

Now the same people are flying to Japan
just to feel something they can't name.

A meal made by hand.
A street that's quiet.
A stranger who's kind for no reason.

Turns out Japan wasn't behind.

It just refused to trade away
the things that actually make a life.

[David @DavidSHolz]

Aristotle said "we should not think human things because we are human, nor mortal things because we are mortal" we should strive to live as though we are more - to "immortalize" and live according to the strongest, best, most supreme elements of ourselves

[Yun-Ta Tsai @yunta_tsai]

My college degree recommendations for fresh high school graduates in the age of AI to be prepared for the next frontier:

- Applied Physics (math and physics)
- Applied Materials (physical agentic)
- Agriculture (food production in space)
- Aerospace (frontier transportation)
- Civil Engineering (infra + life support from Earth and beyond)
- Electronic Engineering (scaling compute and communications to the solar system)
- Manufacturing (where things get made)
- Mechanical Engineering (packaging, manufacturability, tolerances and cycling)
- Medicine (personal drugs in space)

[Tom Q. Mong @tommong]

https://t.co/QvDH32dhe6

[Hardeep @hardeep_gambhir]

I read this every week for 3 years and had it pasted on my dorm room's wall.

Best writing is not literary or PhD-esque.

It is persuasive. It is simple. It's humorous. It has short sentences.

That is more valuable, than any other type of writing.

[Naval @naval]

There are 10x engineers because there are 10x thinkers.

[Dudes Posting Their W’s @DudespostingWs]

This is what the older generations should try and do more often

[pauline @pau_wee_]

works for any city !! get ur weatherbunny here:
https://t.co/d9gVbwQInQ

[Blake Burge @blakeaburge]

Underrated life advice: Have more hobbies and fewer opinions. Learn an instrument. Plant a garden. Build something with your hands. Cook. Paint. Run. The happiest people I know spend less time debating life and more time actually living it.

[Dr. Arthur Brooks @arthurbrooks]

I don't have many happy memories. For a long time, I thought it was just how I was wired.

I had the science wrong. Memory isn't a personality trait. It's what's left of your attention.

Your brain saves vivid memories when attention is high. That’s why time slows down at a wedding, a birth, or a car accident. You remember those moments because you were actually there.

The reverse is also true. If you weren’t paying attention, your brain didn’t save it. The event happened, but you weren’t there for it. So now it’s gone.

This is why most of your life is forgotten. Not because anything bad happened and is being suppressed—but because you weren’t fully there for most of it.

You can’t fix a past you didn’t save. But you can give your future self a past worth coming back to.

The you of ten years from now will only have what you save today. Sit with her at dinner. Look at your kid’s face. Notice your daily walk.

Save the present. It’s all your past will ever be.

[Marc Andreessen 🇺🇸 @pmarca]

"The vocation of the learner in the age of cheap wheat is to become a baker: to take the now-abundant raw material and turn it into something a human can eat."

[Ihtesham Ali @ihtesham2005]

A 34-year-old physics graduate student spent years writing a strange 800-page book in 1979 about a logician, a Dutch artist, and a German composer. It won the Pulitzer Prize the following year. It quietly became required reading at every AI lab in the world.

It is the only book in history that makes the deepest ideas in computer science feel like a dream you cannot stop thinking about.

I read it across 3 months on a single side table next to my bed and walked away seeing intelligence, consciousness, and AI in a way I cannot un-see.

His name is Douglas Hofstadter. The book is called Gödel, Escher, Bach.

Almost nothing in modern AI makes sense without this book. ChatGPT, Claude, Gemini, the entire architecture of self-attention, the alignment problem, the strange feeling that LLMs sometimes seem to understand and other times seem to be playing an elaborate symbol-shuffling game, all of it traces back to questions Hofstadter laid out in a single book published before most of today's AI engineers were born.

Here is the story almost nobody tells you about how the book came to exist.

Hofstadter was the son of Robert Hofstadter, who won the Nobel Prize in Physics in 1961 for measuring the size of the proton. He was supposed to follow in his father's footsteps.

He started a physics PhD at the University of Oregon. He was miserable. He could not focus. He did not love the work. He kept getting pulled toward something else.

The something else was a single question that had haunted him since childhood.

How can meaning emerge from meaningless symbols? Specifically, how does a brain, which is made of nothing but cells firing electrical signals at each other, produce something that feels like consciousness, like understanding, like a self?

He could not let the question go. He left physics. He started writing. The book took him years. He wrote it largely in isolation, working in the basement of his parents' house and at Indiana University, where he eventually finished it. He thought it would be read by maybe a few hundred logicians and AI researchers. Basic Books published it in 1979 as a 777-page hardcover.

The next year it won the Pulitzer Prize for general non-fiction and the National Book Award for science.

The book is structured in a way that almost no other book has ever attempted. The chapters alternate between two layers. One layer is technical chapters about logic, computability, neuroscience, and AI. The other layer is fictional dialogues between a tortoise and Achilles, characters borrowed from a paradox by Lewis Carroll.

The dialogues play with the same ideas the technical chapters explain. Read in order, they do not feel like a textbook. They feel like a strange house with rooms that loop back into each other and corridors that change shape behind you.

The first thing the book does is explain Gödel's incompleteness theorems in a way no math textbook had ever managed.

Kurt Gödel, an Austrian logician working in 1931, proved something that broke mathematics. He showed that any formal system powerful enough to describe arithmetic contains statements that are true but cannot be proven inside that system. Mathematics, the most certain thing humans had ever built, has holes in it that can never be filled.

Hofstadter spends hundreds of pages making you understand this proof not just as a mathematical theorem, but as a structural fact about every sufficiently complex system. Including the brain. Including any AI. The reason AI alignment is genuinely hard is not just engineering. It is structural.

Any system smart enough to model itself will contain truths about itself it cannot reach from inside itself. Hofstadter showed this 50 years before AI safety was a field.

The second thing the book does is introduce his core idea. He calls it the strange loop.

A strange loop is what happens when a system, by climbing through layers of itself, somehow ends up back where it started. Escher's drawings of staircases that always go up but somehow loop back are visual strange loops. Bach's musical canons that modulate up through keys and end on the original note are auditory strange loops. Gödel's self-referential statements that talk about themselves are logical strange loops.

Hofstadter argues that consciousness is a strange loop. Your brain builds a model of the world. Inside that model, it builds a model of itself perceiving the world. Inside that self-model, it builds a model of itself thinking about itself perceiving the world. The recursion does not bottom out. The self is what the loop feels like from the inside.

This is the part that AI researchers cannot stop returning to. Modern transformer models use self-attention, which is technically a mechanism where a network attends to its own internal states across layers. Recursive reasoning, where a model thinks about its own thinking, is now a research area with its own conferences. Meta-learning, where models learn how to learn, is a direct descendant of what Hofstadter described in 1979 as the necessary structure of any conscious system. He wrote the philosophy. The engineers are now building the implementation.

The third thing the book does is the part that haunts every AI conversation today.

Hofstadter argued that meaning is not something separate from symbol manipulation. It is what symbol manipulation looks like from the inside, when the manipulation is complex enough and self-referential enough. A simple lookup table does not understand anything. But a system that processes symbols at sufficient depth, with enough self-modeling, with enough recursion, starts to look identical from the outside, and possibly from the inside, to a system that understands.

This is the deepest question in modern AI. When ChatGPT generates a response, is it actually thinking, or is it just doing very fast symbol shuffling? Hofstadter spent 800 pages arguing that the distinction may not exist at sufficient scale. If a system shuffles symbols according to the right structure, meaning is what the shuffling looks like from the inside.

You can read modern debates about AI consciousness from Yann LeCun, Geoffrey Hinton, Ilya Sutskever, and David Chalmers, and you will find that they are all, in their own ways, having the argument Hofstadter framed in 1979.

The fourth thing the book did is the one that took the longest to be vindicated.

Hofstadter argued, and continued arguing for decades, that the actual engine of human intelligence is not logic. It is not deduction. It is not pattern matching in any simple sense. It is analogy. The ability to see one thing as similar to another thing, to map the structure of one situation onto a different situation, is, in his view, the core of thought itself.

For decades this was unfashionable. Symbolic AI focused on logic and rules. Statistical AI focused on pattern matching. Almost nobody worked seriously on analogy.

Then large language models started working. And the people who looked closely at what they were doing realized something uncomfortable. LLMs are, fundamentally, analogy machines. They learn structural patterns from text and apply those patterns by analogy to new situations. They do not deduce. They do not reason logically by default. They map the shape of one thing onto the shape of another thing and produce output that fits the new shape.

Hofstadter saw this before any of it existed. His later book Surfaces and Essences, written with Emmanuel Sander, is 600 pages defending the claim that analogy is the core of cognition. It came out in 2013. It was largely ignored. The ChatGPT release in 2022 was, in some sense, a vindication of the entire argument.

The strangest thing about reading Gödel, Escher, Bach in 2026 is realizing how lonely the book must have felt when it was written.

In 1979 there was no GPT. No deep learning. No transformer. The dominant approach to AI was symbolic logic, and most researchers thought minds were going to be programmed top-down, rule by rule, like a complicated chess engine. Hofstadter said the opposite. He said minds were emergent. They came from the bottom up. They were strange loops in complex substrates. The programmers' approach would never produce real intelligence because it was missing the recursive self-modeling that made minds real.

He was right.

The book is hard. I had to use all the LLMs and NotebookLM to understand it. It is not a beach read. You do not finish it in a weekend. The math chapters require attention. The dialogues require patience. Most people who buy it never finish it. That is fine. The book is structured so that reading any 50 pages produces a permanent shift in how you think.

Bill Gates lists it among the books that shaped him. Steve Jobs read it. Almost every senior AI researcher in the world will tell you it was the book that made them fall in love with the question of intelligence in the first place.

Hofstadter himself has been in doubt about modern LLMs. He has said they may have proven him right about analogy and wrong about consciousness at the same time. He is still writing. He is still working on the same question that pulled him out of physics 50 years ago.

The 800-page book that explained intelligence before AI existed is sitting one click away from you.

Most people will never open it. The ones who do will see the world differently for the rest of their lives.

[Joseph Fasano @Joseph_Fasano_]

I'm astonished by the response to my poem "Instructions for Having a Soul." Here it is in my voice, from one human to another.

[Kevin Kelly @kevin2kelly]

My latest scenario: we are entering a period of permanent (decades) ultra-high uncertainty, in AI and all around. https://t.co/MAW4nZic9J

[Kevin Kelly @kevin2kelly]

Here in brief is the method I’ve honed to optimize a two-week vacation: When you arrive in a new country, immediately proceed to the farthest, most remote, most distant place you intend to reach during the trip. If there is a small village, remote spa, a friend’s farm, or a wild place you plan on seeing on the trip, go there immediately. Do not stop near the airport. Do not rest overnight in the arrival city. Do not pause to acclimate. If at all possible proceed by plane, bus, jeep, car directly to the furthest point without interruption. Make it an overnight journey if you have to. Then once you reach your furthest point, unpack, explore, and work your way slowly back to the big city, wherever your international departure airport is. In other words you make a laser-straight rush for the end, and then meander back. Laser out, meander back. This method is somewhat contrary to many people’s first instincts, which are to immediately get acclimated to the culture in the landing city before proceeding to the hinterlands. The thinking is: get a sense of what’s going on, stock up, size up the joint. Then slowly work up to the more challenging, more remote areas. That’s reasonable, but not optimal because most big cities around the world are more similar than different. All big cities these days feel same-same on first arrival. In Laser-Back travel what happens is that you are immediately thrown into Very Different Otherness, the maximum difference that you will get on this trip. You go from your home to extreme differences so fast it is almost like the dissolve effect in a slide show. Bam! Your eyes are wide open. You are on your toes. All ears. And there at the end of the road (but your beginning), your inevitable mistakes are usually cheaper, easier to recover from, and more fun. You take it slower, no matter what country you are in. Then you use the allotted time to head back to the airport city, at whatever pace is your pace. But, when you arrive in the city after a week or so traveling in this strangeness, and maybe without many of the luxuries you are used to, you suddenly see the city the same way the other folks around you do. After eight days in less fancy digs, the bright lights, and smooth shopping streets, and late-night eateries dazzle you, and you embrace the city with warmth and eagerness. It all seems so … civilized and ingenious. It’s brilliant! The hustle and bustle are less annoying and almost welcomed. And the attractions you notice are the small details that natives appreciate. You see the city more like a native and less like a jaded tourist in a look-alike urban mall. You leave having enjoyed both the remote and the adjacent, the old and new, the slow and the fast, the small and the big. We’ve also learned that this intensity works best if we aim for 12 days away from home. That means 10 days for in-country experience, plus a travel day (or two) on each end. We’ve found from doing this many times, with many travelers of all ages and interests, 14 days on the ground is two days too many. There seems to be a natural lull at about 10 days of intense kinetic travel. People start to tune out a bit. So we cut it there and use the other days to come and go and soften the transitions. On the other hand 8 days feels like the momentum is cut short. So 10 days of intensity, and 12 days in a country is what we aim for. Laser-back travel is not foolproof, nor always possible, but on average it tends to work better than the other ways I’ve tried. #KKtraveltips

[Taylor Pearson @TaylorPearsonMe]

One historical analogy I keep coming back to in thinking about how AI is reshaping knowledge work is the Master Builder, most famously embodied by Brunelleschi

In 1418, Florence announced a competition to finish its cathedral, but there was a wrinkle. It had a 143-foot opening (wider than the Pantheon) sitting 180 feet in the air. No one knew how to finish it. Traditional wooden centering would have required more timber than existed in Tuscany.

Filippo Brunelleschi, a goldsmith by training, proposed building two nested domes with no centering at all. He succeeded and it looks awesome and is super famous now but the story of how he did it is pretty interesting.

His most important innovations weren't in the proposal. They were invented on site. He developed a herringbone brickwork pattern that let each course transfer weight laterally to the nearest rib. He came up with an ox-powered hoist with a reversible clutch so the animals never had to turn around that lifted 37,000 tons of material over sixteen years.

Brunelleschi was on site daily. His title was Master Builder - Architect, engineer, and construction supervisor at once. Design and execution were a single discipline, there was no sense of specialization along the lines we think of them now.

The split between designer, engineer, and builder is a relatively recent invention. It emerged as specialized knowledge deepened and coordination between specialists became cheaper than cramming every skill into one person's head.

Ronald Coase made the general argument: you split one job into two when one person can't do both well, and the coordination cost between specialists is worth the quality gain.

I keep coming back to this example because of Boris Cherny's comment that the title of software engineer is going away. Not because engineers aren't needed, but because engineering, product, and design already overlap by 50% inside Anthropic now. They just aren't distinct roles anymore.

When AI tools make you 90th percentile at design, engineering, and product thinking, the gap between you and a dedicated specialist narrows. Every handoff costs context, every sync meeting is time not building though and so it makes sense that the role would become more integrative.

I think software is the canary in the coal mine and this is a more general form factor we see with post-AI roles.

Brunelleschi's mode of working is coming back. What becomes scarce is the integration: holding the pieces together, knowing which sub to call for which job, making the trade-offs when the plumbing hits the wall. At the margin, the value isn't in the specialized work anymore, it's combining vision and execution together the way a master builder does.