We often romanticize the “back of the napkin” idea. It is the symbol of spontaneous genius—the startup mapped out in a coffee shop, the ticker symbol hurriedly scribbled during a dinner party. But we rarely talk about what happens to the napkin afterwards.
Usually, it gets thrown away. Or lost. Or stuffed into a drawer, becoming just another artifact of a fleeting thought that had momentum but no direction.
In the first two parts of this experiment, I used Gemini 3 Pro to solve the friction of entry (transcribing my messy handwriting) and the friction of analysis (stress-testing the ideas against 10-K realities). But there was one final gap: Permanence.
An analysis that lives and dies in a chat window is barely better than one that lives and dies in a notebook. It is still ephemeral. To truly build a “Second Brain” for investing, the data needs to leave the conversation and enter a system.
“The goal of technology should be to stop us from losing the work we’ve already done.”
I tweaked my workflow one last time. I asked the AI to not just judge the stocks, but to format its judgment into a raw CSV block.
With a simple copy-paste, my handwritten scribble wasn’t just digitized; it was database-ready. It went from a piece of paper to a row in Google Sheets with columns for “Market Cap,” “P/E Ratio,” and “Primary Risk.”
Suddenly, I wasn’t just looking at a list; I was building a ledger. I can now track these ideas over months. I can see if the “Red Flag” the AI identified actually played out. I can measure my own batting average.
The goal of technology shouldn’t just be to make us faster at doing work. It should be to stop us from losing the work we’ve already done. By turning ink into data, we stop treating our ideas as disposable. We give them the respect of memory.
There is a seduction in the handwritten note. When I scribble down a company name in a notebook, it is purely additive. It represents potential upside, a future win, a brilliant insight caught in ink. The notebook is a safe harbor for optimism because it lacks a “Reply” button. It doesn’t argue back.
But optimism is an expensive luxury in investing.
After my initial experiment—using Gemini 3 Pro to transcribe my messy list into tickers—I felt a surge of productivity. But productivity is not the same as discernment or understanding. I had a list of stocks, but I didn’t have a thesis. I just had digitized hope.
So, I took the next step. I didn’t ask the AI for validation; I asked for a fight. I fed the tickers back into the model with a specific directive: “Act as a contrarian hedge fund analyst. Find the red flags. Kill my enthusiasm.”
“I didn’t ask the AI for validation; I asked for a fight.”
The results were immediate and sobering. The “promising tech play” I had noted? The AI highlighted a massive deceleration in user growth hidden in the footnotes of their latest 10-Q. The “stable dividend payer”? It flagged a payout ratio that was mathematically unsustainable.
In seconds, the warm glow of my handwritten discovery was doused with the cold water of 10-K realities. And it was fantastic.
We often view AI as a tool for creation—generating text, images, and code. But its highest leverage application might actually be destruction. By using it to stress-test our assumptions, we outsource the emotional labor of being the “bad cop.” It allows us to kill bad ideas quickly, cheapy, and privately, before we pay the market tuition for them.
My notebook is still where the dreams live. But the digital realm is now where they go to survive the interrogation.
There is a distinct friction that exists between the analog world and the digital one. For years, analog notebooks have been the graveyard of good intentions—lists of books to read, article ideas to write, and companies to investigate, all trapped in the amber of my barely legible handwriting.
I recently found myself looking at one of these lists: a scrawl of company names I had jotted down while reading an article discussing possible companies for investment in 2026. Usually, this is where the work begins—taking my handwritten notes, typing them out one by one, searching for tickers, opening tabs, etc. It is low-value administrative work that often kills any spark of curiosity before it can turn into useful analysis.
“The barrier to entry for deep research drops to the time it takes to snap a photo.”
On a whim, I snapped a photo and uploaded it to Gemini 3 Pro. “Transcribe this,” I asked. “Give me the tickers.”
I expected errors. My handwriting is, to put it mildly, not easy to read (even for me!).
Instead, the AI didn’t just perform Optical Character Recognition (OCR); it performed contextual recognition. It understood that the scribble resembling “Apl” in a list of businesses was likely Apple, and returned $AAPL. It deciphered the intent behind the ink.
But the real shift happened when I asked Gemini to pivot immediately into research. Within seconds, I went from a static piece of paper to a dynamic analysis of P/E ratios, recent news, and market sentiment. The friction was gone.
This experience wasn’t just about productivity; it was about the fluidity of thought. We are moving toward a reality where the interface between the physical world and digital intelligence is becoming permeable. When the barrier to entry for deep research drops to the time it takes to snap a photo, our curiosity is no longer limited by our patience for data entry. We are free to simply think.
There is a distinct texture to working with a truly capable person. It is a feeling of relief, specific and profound.
When you hand a project to a junior employee who “gets it,” the mental load doesn’t just decrease; it vanishes. You don’t have to map the territory for them. You don’t have to pre-visualize every stumble or correct every navigational error. You simply point to the destination, and they find their way.
I was thinking about this feeling—this specific brand of professional trust—when I read a recent observation from two partners at Sequoia regarding the current state of Artificial Intelligence:
“Generally intelligent people can work autonomously for hours at a time, making and fixing their mistakes and figuring out what to do next without being told. Generally intelligent agents can do the same thing. This is new.”
The phrase that sticks with me is “without being told.”
For the last forty years, our relationship with computers has been strictly transactional. The computer waits. We command. It executes. Even the most sophisticated algorithms have essentially been waiting for us to hit “Enter.” They are tools, no different in spirit than a very fast abacus or a hyper-efficient typewriter.
But we are crossing a threshold where the software stops waiting.
The definition of intelligence in a workspace isn’t just raw processing power; it is the ability to recover from failure without supervision. It is the capacity to run into a wall, realize you have hit a wall, back up, and look for a door—all while the manager is asleep or working on something else.
When Sequoia notes that “this is new,” they aren’t talking about a feature update. They are talking about a shift in the ontology of our tools. We are moving from an era of leverage (tools that make us faster) to an era of agency (tools that act on our behalf).
This changes the psychological contract between human and machine. If an agent can “figure out what to do next,” we are no longer operators; we are managers. And as anyone who has transitioned from individual contributor to management knows, that is a fundamentally different skill set. It requires clearer intent, better goal-setting, and the ability to trust a process you cannot entirely see.
We are about to find out what it feels like to have a digital colleague that doesn’t just listen, but actually thinks about the next step.
“Recursive self-improvement has graduated from a safety paper to a shipping manifest.”
For years, “recursive self-improvement”—the idea of AI building better versions of itself—was a concept relegated to academic safety papers and late-night philosophy forums. It was a theoretical horizon event, something to be modeled, debated, and perhaps feared.
But this morning, the tone shifted. As noted in a briefing this morning from @alexwg, recursive self-improvement has graduated from a safety paper to a shipping manifest.
The evidence is tangible. Anthropic confirmed that their new “Claude Code” wrote the entire Claude Cowork desktop app in a mere week and a half. This isn’t just code completion; it is code creation at a structural level. More importantly, this app grants the AI direct access to the file system. It is no longer trapped in a chat window, floating in the abstract void of the cloud. It has touched down. It can sort downloads, generate reports, and effectively reorganize “local reality.”
Simultaneously, the definition of “colleague” is dissolving. The CEO of McKinsey dropped a quiet bombshell, revealing that the firm now counts AI agents as “people” that the firm “employs.” The current census? 40,000 humans and 20,000 agents. The goal is parity within 18 months.
We are witnessing a fundamental agentic shift. When a consultancy firm—the bastion of human capital and billable hours—begins to view synthetic agents not as tools (CAPEX) but as employees (OPEX), the psychological contract of work changes. We are moving away from a world where we use software to a world where we manage it.
The org chart is no longer a biological tree; it is becoming a hybrid network. The recursive loop isn’t coming; it’s already clocked in.
Update Jan 12: Due to popular demand, I’ve posted 3 new advanced prompt styles (Dark Walnut, Folk Art, and Rough Hewn). [Read Part II here].
Check out the image below, which features a wood carving of my friend Doug and me. I made it from a photo we took during a workshop in Paris ten years ago.
The image is the result of a fun prompt for Google Gemini 3 Pro and Nano Banana. You upload a photo with a person or a couple of people then enter this prompt:
Create an image of a hand-carved wooden miniature figure of the people in the attached image, shaped with visible knife marks, natural grain texture, and smooth unfinished edges. Placed on a workshop table with carving tools, wood shavings, and soft warm directional lighting. 1080×1080.
Doug Kaye and Scott Loftesness
I wish I could remember where I got this particular prompt – I’ve been collecting a lot of them when they show up in my feed on X but fail to note the source. This was definitely one of the better creative image prompts that I’ve come across.
Three years ago, in the early days of AI chatbots, I used GPT-3 to write a Thanksgiving story. DALL-E provided the illustration. Good fun!
This morning I used Google’s latest Gemini 3 Pro to write another Thanksgiving story and it got pretty creative. The illustration was created by Nano Banana Pro based on the text of the story. See the prompts I used at the end of this post.
See what you think…Happy Thanksgiving 2025!
The Blizzard, the Blackout, and Uncle Arthur’s Tactical Turkey
Happy Thanksgiving morning, friends. As I write this, my own oven is already preheating, and the coffee is brewing strong. It’s a calm, crisp morning here. But my mind—as it often does on this specific Thursday—is drifting back twenty years, to the Thanksgiving of the Great Blizzard, and more specifically, to the Thanksgiving of Uncle Arthur.
Every family has that one relative who operates on a slightly different frequency than the rest of the known universe. For us, it was my father’s older brother, Arthur. Arthur was… prepared. He didn’t just have hobbies; he had impending doom scenarios. He was a mild-mannered accountant eleven months of the year, but come November, he vibrated with survivalist energy. He was the only person I knew who wore cargo pants to a formal dinner “just in case he needed the pockets.”
Balancing him out was Aunt Sally. Sally was a saint walking the earth in sensible shoes. She was soft, round, smelled perpetually of lavender and yeast rolls, and possessed the patience of a geological era. She loved Arthur deeply, usually communicating this love by gently patting his arm when he started talking about solar flares and whispering, “That’s nice, dear, but eat your celery.”
The year of the blizzard, the snow started falling on Tuesday and simply never stopped. By Thanksgiving morning, the drifts were halfway up the dining room windows. The wind was howling around the eaves of my parents’ old Victorian house like a distressed banshee.
We were all nervously watching the power lines sway when Arthur and Sally arrived. Their sedan looked like an igloo on wheels. Arthur burst through the front door in a flurry of snow, looking less like a dinner guest and more like a polar explorer who had just eaten his sled dogs. He wasn’t carrying a bottle of wine. He was carrying a military-grade waterproof duffel bag that clanked menacingly.
“Roads are treacherous!” Arthur boomed, shaking snow onto the rug. “Saw three cars in the ditch. Tell me, Bob,” he grabbed my father by the shoulder, “how’s your generator fuel supply looking? The grid is unstable.”
Behind him, Aunt Sally floated in, carrying her famous cranberry chutney and looking completely unruffled. “Arthur thinks the squirrels are plotting something,” she said pleasantly, handing my mother her coat. “It’s just the wind, dear.”
The afternoon was a study in escalating tension. Outside, the world was disappearing into a white void. Inside, my mother was stress-basting the turkey every nine minutes. Arthur, meanwhile, was pacing the living room, periodically checking a massive, complex barometer he’d set up on the coffee table and muttering about “barometric pressure plunges.”
We sat down to dinner at 4:00 PM. The turkey was golden. The gravy was glistening. We held hands. My dad started the grace. “…and we are thankful for the roof over our heads and the warmth of this—”
Zzzzip. Pop.
Darkness.
Not just a flicker. A profound, heavy, silence-filled darkness. The furnace blower groaned and died. The electric oven ticked its last tick.
Immediate chaos erupted. My mother let out a wail regarding the undercooked state of the sweet potatoes. My little cousins started crying.
Then, a beam of searing white light cut through the dining room.
We all shielded our eyes. There stood Uncle Arthur. He had produced a tactical flashlight brilliant enough to signal passing aircraft. He was grinning like a man who had waited his entire life for this exact moment.
“Don’t panic!” he shouted, his voice vibrating with glee. “Sally, the bag!”
Aunt Sally, sighing audibly in the dark, fumbled for the duffel bag by the door. Within ninety seconds, Arthur had distributed headlamps to everyone—red-filtered ones, “to preserve night vision,” he insisted. We all sat around the beautiful table looking like a convention of confused miners.
“The turkey,” my mother moaned from the shadows. “It needs another hour at 350!” “Step aside, Marie,” Arthur declared. What followed was the greatest piece of improvisation I’ve ever witnessed. Arthur, ignoring Sally’s gentle suggestions that we just eat sandwiches, decided we were going to finish the turkey in the living room fireplace.
He rigged a horrifyingly unstable spit using an antique fireplace poker and some galvanized wire he produced from his cargo pants. He commandeered my dad’s expensive oak logs to build a roaring fire that threatened to singe the eyebrows off anyone within five feet.
For the next hour, Arthur sat on the hearth, sweating profusely in the firelight, manually rotating a twenty-pound bird over open flames while wearing an oven mitt and a headlamp. He looked utterly demented. He also looked incredibly happy.
Aunt Sally sat in the armchair next to him, knitting by firelight, occasionally feeding him a cracker. “You’re doing great, honey,” she’d say, as sparks showered down onto the rug.
We ate dinner three hours late. The dining room was freezing, lit only by Arthur’s arsenal of camping lanterns and the fireplace glow from the other room. The turkey was… interesting. The skin was practically carbonized on one side and flabby on the other. It tasted strongly of oak smoke and lighter fluid. The mashed potatoes were lukewarm lumps. It was the best Thanksgiving dinner we ever had.
We laughed until we cried, huddled together in blankets under the harsh glare of the tactical lights. We told stories. We forgot about the storm outside because the chaos inside was so much more entertaining. At the head of the table sat Uncle Arthur, smelling of woodsmoke and triumph, his headlamp still beaming proudly on his forehead. And next to him sat Aunt Sally, who squeezed his hand under the table and whispered, “You saved us, dear. Now pass the smoky bits.”
Today, as I look at my perfectly reliable oven and my calm weather forecast, I almost miss the disaster. I miss the improvisational madness.
So, here’s to the oddballs, the preppers, and the quirky relatives who drive us crazy right up until the moment we desperately need them. And here’s to the Aunt Sallys who love them through it all.
May your turkey be cooked, your power stay on, and your memories be warm. Happy Thanksgiving.
Prompt for story: For my personal blog on Thanksgiving morning, write a story about a memorable family thanksgiving with an odd uncle on a snowy stormy day and his wife my aunt sally.
Prompt for image: Now create a photorealistic image based on the text of the story. Make it Norman Rockwell style.
Following up on my earlier post about Dwarkesh Patel’s lament about LLMs not really learning, Gwern writes LLM Daydreaming.
I propose a day-dreaming loop (DDL): a background process that continuously samples pairs of concepts from memory. A generator model explores non-obvious links between them, and a critic model filters the results for genuinely valuable ideas. These discoveries are fed back into the system’s memory, creating a compounding feedback loop where new ideas themselves become seeds for future combinations.
He points out that while the current crop of AI models are very powerful they still lack a fundamental capability that’s required to make them of real value in many contexts: the ability to learn via on the job training. That’s something that’s fundamental to human teams but it’s completely lacking in the current AI models.
Until there’s some breakthrough that solves this weakness, Patel feels the application of AI to many real world scenarios will be necessarily limited.
Many people are very optimistic about the potential for humanoid robots in the home. But this current lack of a learning capability is such a fundamental gap that it’s hard to imagine anyone wanting a robot that can’t take instruction and learn!
Maybe this capability just isn’t that important for self-driving cars — but for many other applications learning is totally fundamental and the lack of learning is a show stopper.
Recently Dwarkesh Patel shared some of his thoughts about one of the major challenges the current crop of large language models face: they’re not easily trained like a human assistant can be.
… the fundamental problem is that LLMs don’t get better over time the way a human would. The lack of continual learning is a huge huge problem. The LLM baseline at many tasks might be higher than an average human’s. But there’s no way to give a model high level feedback. You’re stuck with the abilities you get out of the box. You can keep messing around with the system prompt. In practice this just doesn’t produce anything even close to the kind of learning and improvement that human employees experience.
Today on X Andrej Karpathy replied to Dwarkesh and included the following which introduced a new term to me describing this weakness:
I like to talk explain it as LLMs are a bit like a coworker with Anterograde amnesia – they don’t consolidate or build long-running knowledge or expertise once training is over and all they have is short-term memory (context window). It’s hard to build relationships … or do work … with this condition.
I’m quite interested to see how this issue begins to be meaningfully addressed!
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