Arif Padaria

@arifp

Building, Enabling and Investing in disruptive order-of-magnitude innovations @ Faris Capital | Tech Executive | Operator | Investor | 3x Founder

Boston, MA

Joined May 2008

972 Following

690 Followers

1.7K Posts

Arif Padaria @arifp

2 days ago

Check out my latest article: Performance Gets You the Demo. Certifiability Gets You the Factory Floor. https://t.co/M1p2YRQYst via @LinkedIn

Arif Padaria @arifp

5 days ago

#AmericanAirlines crew and pilot making nonsense excuses for a very long delay in getting our flight going from ORD to BOS tonight. 3 hr delay and counting. Be warned. #AA is not the airline it used to be

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Arif Padaria @arifp

10 days ago

Check out my latest article: The Model That Beat Its Teacher Has a Speed Problem https://t.co/xqEaGf21VR via @LinkedIn

Arif Padaria @arifp

21 days ago

I'll be in #Chicago next week for #Automate (June 22-24). If you're attending and want to grab coffee, compare notes, or just talk shop about #automation, #robotics, or #AI moving into the #physical world, reach out. Always glad to meet people building in this space. C u there!

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Who to follow

𝚁𝚘𝚋 𝙳𝚊𝚢

@cleantechinv

Pragmatic solutions to big problems. Nothing here is investment advice, viewpoints are personal only, YMMV...(https://t.co/xxdvsSaGDh)

Eric Graham

@begraham43

Entrepreneur, father, innovator, taking on the comfort, efficiency, & safety of buildings with the help of all of you.

Dave Ross

@porbafingos

I probably don't hate you.

Arif Padaria @arifp

25 days ago

@noisyb0y1 Why not Obsidian + Claude ? Are you running Kimi locally? What’s the hardware config and cost ?

206

arifp retweeted

Milk Road AI

@MilkRoadAI

about 1 month ago

Alex Karp was asked the question every Palantir investor has been dreading, can the large language model companies just replicate what Palantir does? (Save this). His answer was that the investors asking that question are reading reports about something they do not understand technically. The LLM companies solve the simplest, easiest problems that sell tokens and they fundamentally do not understand how disliked they are outside of San Francisco. There is a real product argument underneath that. When you need to put a missile on an adversary's head and bring Americans home safely, you do not hand that task to a probabilistic model built to be right 51% of the time. Manufacturing a car part, launching a rocket, running battlefield intelligence, these are problems where being wrong is catastrophic, and no serious enterprise would trust that to a system designed to generate convincing text. He also dropped a line that most people missed. Most of what Anthropic talks about in public is actually running on Palantir. Dario Amodei builds the frontier model, Karp likes him personally but the deployment layer underneath is Palantir's. The frontier model and the operating system are two different businesses, and Karp is betting the next seven years belong to whoever controls the operating system. The bull case is not subtle at this point. Palantir just reported 85% revenue growth in Q1 2026, its fastest expansion since going public with US revenue up 104% year-over-year and US commercial revenue up 133%. They posted a Rule of 40 score of 145%, raised full-year 2026 guidance to $7.65 billion implying 71% growth, and the US business is on pace to double again in 2027. The reason Palantir is hard to replicate is exactly what Karp described, it is not a software product, but rather an embedded operating system for the world's most complex organizations. Switching costs are enormous, trust takes years to build, and the cultural gap between Palantir's defense-first DNA and a San Francisco LLM startup is not something you close by hiring good engineers. That is not a company being disrupted by large language models but rather the company that everything else runs on top of. Come join Milk Road Pro for our full breakdown on Palantir, what the Anthropic partnership means for long-term revenue, and our entire AI thesis. Link below!

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Arif Padaria @arifp

about 1 month ago

@ai_for_success When will it do what Wispr Flow provides but fully offline ?

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Arif Padaria @arifp

about 1 month ago

@monokern thank you! I've been using NotebookLM and Obsidian to build my knowledge vault, but having this automated research pipeline, that I extend to a variety of data sources both internal and external, is just beyond *superb*. Can't thank you enough !

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Arif Padaria @arifp

about 2 months ago

@sairahul1 @threadreaderapp unroll

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Arif Padaria @arifp

about 2 months ago

Check out my latest article: Physical AI Has a Data Problem That Software AI Never Had https://t.co/vIYMsfrSHo via @LinkedIn

Arif Padaria @arifp

2 months ago

The Fast Brain, the Slow Brain, and the Missing Layer. https://t.co/nGiP6dugK9

arifp retweeted

Ihtesham Ali

@ihtesham2005

2 months ago

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.

ihtesham2005's tweet photo. 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.

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Arif Padaria @arifp

3 months ago

https://t.co/O12CvILHgF

Arif Padaria @arifp

3 months ago

When AI stops recommending & starts executing, the value doesn't sit in the model. It sits in the harness that connects AI capability to real-world workflows. There's a parallel wave building in the physical world. Physical AI. And that's the next S-curve.