Author: Scott Loftesness

Categories
AI Business

The Wage of Knowing

In 1973 the Los Angeles Public Library installed a telephone line that worked while the building was dark. Dial H-O-O-T-O-W-L on a rotary phone, nine at night until one in the morning, and a librarian would answer. Somebody wanted to know the boiling point of mercury, or who wrote a poem they half remembered, or how many wives Henry VIII actually had, and a person on the other end of a cord found out. This went on for years. Nobody thought of it as data collection. It was just a service, a courtesy, a woman at a desk with a card catalog in her head.

I worked, in another life, in the payments industry, back when a merchant who wanted to charge your card had to call in and ask permission. There were rooms for this. Banks of phones, a bulletin of stolen numbers updated by hand, a floor limit past which a supervisor had to be found. The people answering the phones were, more often than you would guess, college students. Twenty years old, minimum wage, deciding in real time whether a stranger’s card was good. Nobody trained them for six months first. They learned the bulletin, they learned to listen for something wrong in a voice, and they said yes or no.

I have been driven, recently, by a car with nobody driving it. I noticed the wheel turning on its own and I braced for the wrongness of it. Thirty seconds later I was not bracing. I was looking out the window. The data says I was right to relax: across two hundred and twenty million miles, the cars involved in this experiment cause a small fraction of the serious crashes a human would have caused over the same roads. I did not need the data. I needed thirty seconds.

None of these people knew what they were doing. That is the thing about the librarian and the college student and, for that matter, about me learning to trust a wheel that moves by itself. The librarian was not building a search engine. The clerk was not training a fraud model. He was making rent. Their competence was not evidence, to them. It was just Tuesday. It became evidence later, to someone else, in a room they never saw — the accident logs, the chargeback data, the accumulated record of a million correct guesses that turned out to be exactly the material a system needed to learn the job and take it.

This is the part that is easy to get wrong. It is not that the human failed and the machine succeeded. It is that the human succeeding, over and over, in full view, was the demonstration that the job could be learned. You do not automate a task nobody can do. You automate the one being done well enough, often enough, for long enough that the pattern becomes visible. Doing the job right was never neutral. It was the case being built.

Which brings me to a woman I will call the lawyer, because there are thousands of her and none of them are exactly her. She has a laptop open at her kitchen table. She logs into a dashboard belonging to a company that pairs credentialed people with the AI labs that need them — a doctor here, a banker there, a corporate attorney with fifteen years of contract law behind her. She reads a model’s draft of a merger agreement and marks where it reasons like a first-year associate instead of a partner. She rewrites a clause. She explains, in the margin, why the model’s version would get laughed out of a negotiation. She is paid well for this. More, some weeks, than she billed certain clients.

She knows exactly what she is doing. That is the difference between her and the other three. The librarian did not know she was leaving a trail. The clerk did not know his good judgment would become someone else’s weights. I did not know, thirty seconds into that ride, that I was participating in anything at all. The lawyer knows. She is being paid, by the hour, at a rate that respects her expertise, to make her expertise legible enough that it no longer requires her. The company she works for has a name for this. They call it the reinforcement learning economy, which is a tidy way of saying: teach it everything, and then it will not need to call you back.

She does the work anyway. The rate is good. The work is interesting, in the way that teaching is interesting — you learn what you know by trying to say it clearly enough for someone else to use. Nobody is lying to her. The dashboard does not pretend to be anything other than what it is. She logs off at the end of the session the way anyone logs off after a long day of being excellent at something, tired in the specific way that comes from careful work, and she does not, from what I understand, spend the evening thinking about what she has just fed into the machine.

I keep coming back to the rotary dial. Somebody dialing H-O-O-T-O-W-L at midnight in 1973 could not have imagined the lawyer at her kitchen table. But the shape is the same, if you look at it long enough. A person answers a question well. The answering becomes a record. The record becomes a system. The system answers next time. Nobody in the room ever decided this was the plan. It just turned out, every time, to be the plan.

Categories
AI Photography

The Price of the Cold

Two men are standing close to a brick wall trying not to talk, because talking wastes what little warmth is left in a body that has been outside too long. One of them has a camera — Jerry Schatzberg, a fashion photographer. His hands are jammed half into his coat pockets between shots. The other man has his collar up around his ears and a scarf wound twice, black and white, and he is not moving much, because moving costs heat, and heat is the one thing neither of them has enough of. Schatzberg raises the camera. His fingers, by this point, are not entirely his own. When he presses the shutter there is a tremor in it he did not order and cannot undo.

The picture comes out smeared at the edges. Bob Dylan’s face, in the frame, is dissolving slightly into the gray behind him, like a man photographed through a windshield in the rain. It is, by any studio standard, a bad photograph. Schatzberg knows it’s a bad photograph. He has made a career out of not taking bad photographs.

And it became the cover of Blonde on Blonde, which is the best rock album ever recorded, and in nearly sixty years nobody has managed to improve on it by reshooting it clean. The blur isn’t a decision. It’s a symptom — of two men standing in the cold too long, of a photographer choosing, afterward, to keep the evidence of his own discomfort instead of erasing it.

There’s a difference between an accident and serendipity that I don’t think gets said out loud enough, and it matters more than it used to. An accident is the cold — involuntary, uninvited, spent before you know if it was worth spending. Schatzberg didn’t choose to shiver. His hands moved because his body was doing what bodies do at a certain temperature, and the shutter caught what his hands actually did, not what he meant to do. Serendipity is what happens next: a verdict, rendered after the fact, that the wreckage of an intention was better than the intention itself. The accident is what makes the verdict possible. Without the cold, there’s nothing to render a verdict on.

I’ve been sitting with a large language model most days for the better part of a year now, watching it write, asking it to try again, watching it try again in a way that is never quite the same and never quite different enough to matter. Somewhere upstream of me there is a number called temperature, and I will never see it. Somebody else did, once, in a meeting, and decided that the word for controlled, pre-approved, refundable randomness should be temperature — the same word for the thing that made Schatzberg’s hands shake, the same word for the actual physical stakes of standing outside too long in January without enough coat — and then set it, and moved on, and nobody in that meeting laughed, because nobody in the room had ever been cold in a way that mattered to the work.

Picture the room instead. It is climate-controlled to sixty-eight degrees, humidity held flat, year-round, by a building management system nobody thinks about until it fails. Somewhere in it, the hardware is generating your next five versions of a photograph like the one on Blonde on Blonde. Nobody in that room is going to lose feeling in their fingers today. Nobody’s collar is up. I don’t know his name — nobody outside the building does — but somebody like him tuned the sampling distribution and went home at six. That’s the guy in the good suit. He built the weather. He never once stood in it.

The small model inherits conclusions. It never inherits the cold. Whatever accidents shaped the teacher model’s own training — whatever costly friction produced the insight in the first place — the student model gets none of that weather. It gets the photograph, cropped and sharpened, with the blur removed because somebody along the way decided the blur was noise instead of signal — the way Schatzberg, a lesser photographer, might have reshot Dylan clean and thrown the bad one away. It is heir to a serendipity it never earned, because it was never present for the accident that made the serendipity possible. It is, in the most literal sense the industry means by the word, cheap.

I keep coming back to the fact that nobody at the API layer is shivering. That’s not a complaint, exactly. It’s just an observation about where the cost went. Somewhere in the training data, some human being was cold, or scared, or holding a fish that was starting to smell, or standing on a stepladder with ten minutes before the traffic came back, and that person paid a real price for a result they couldn’t yet know was good. The model downstream of all that gets the result without the price.

Two rooms, then. In one of them it is January in New York and a man’s fingers have stopped entirely obeying him. In the other it is sixty-eight degrees, always, on a Tuesday and on a Sunday and at three in the morning, and the machines are making you nine more versions of that same blur. Sixty-eight degrees. A number, upstream, that you will never see.

Categories
AI

The Taste Beneath the Summary

The real work of staying informed has never been volume. It has been the quiet, repeated acts of judgment: does this matter, to whom, why now, what is the signal beneath the noise.

A recent piece from Bridgewater’s AIA Labs and Thinking Machines Lab, “Learning to Replicate Expert Judgment in Financial Tasks,” describes training models to do the triage investors actually do—filtering news, research, central bank documents, internal notes, for relevance. Frontier models struggled with judgments that looked simple and weren’t. The fix wasn’t a bigger model. It was Qwen, fine-tuned on labeled examples from practitioners, and it beat the frontier leaders while costing a fraction to run.

The bottleneck was never model size. It was taste. And taste, it turns out, can be taught to something small and cheap, if you’re precise enough about what you’re teaching it—a market’s worth of Mercors is already proving the same thing at scale.

The researchers were clear that expert judgment doesn’t reduce to rules or prompts. It took high-quality, domain-specific labels from people doing the actual work. The most powerful systems will be built in partnership with practitioners who can say, and keep saying, what “good” looks like in their own context.

Which raises the question I haven’t answered yet: what would I actually put in the labels, if someone asked me to teach my own taste to a cheap model.

Categories
Living

Why the Light Leaves Mornings First

I woke up at my usual time this morning and noticed the room was a little dimmer than it had been just a week or two earlier. The light felt more reluctant to arrive. It wasn’t dramatic — just enough to make me check the clock and wonder whether the days were already turning.

They were. And the change had begun earlier than I expected.

Here on the San Francisco Peninsula, the earliest sunrise of the year came around June 12. The summer solstice — the official longest day — arrived on June 21. Yet the latest sunset didn’t occur until June 28. That gap means the mornings started shortening while the evenings were still lengthening. Nature didn’t wait for the solstice to begin reclaiming the light on the side of the day I care about most.

As a lifelong morning person, I’ll admit this feels slightly unfair. I treasure those early hours when the light arrives gently and the day still feels open and possible. Learning that those hours began to shrink first, while the evening light held on a little longer, feels like a quiet trick played on people who love the dawn.

The total amount of daylight still reached its peak on the solstice itself, when the Sun stands highest in our sky. But sunrise and sunset don’t move in perfect step with each other. Two subtle astronomical effects are responsible: the gradual shift in the Sun’s declination and the Equation of Time — the small irregularity in the Sun’s apparent motion caused by Earth’s tilted, elliptical orbit. Together they create this gentle asymmetry. The mornings give ground first.

By today, July 10, both ends of the day are shortening. Sunrise here is around 5:54 a.m. and sunset around 8:32 p.m., bringing us down to roughly fourteen hours and thirty-eight minutes of daylight and losing about a minute each day. The process, though, began weeks ago — quietly, on the morning side.

I’ve been reflecting on how often change arrives this way: unevenly, and rarely all at once. One part of a season or a life begins to shift while another part still feels steady. It’s easy to grumble when the change touches something we love. But there’s also an invitation in noticing it — to pay closer attention to these transitions, to savor what remains abundant, and to stay curious about the small asymmetries that shape our days.

This morning the light is arriving more quietly than it did just a few weeks ago. Later I’ll step outside earlier than usual, just to meet it while it’s still generous. The day is already growing shorter on my preferred side, yet it remains long enough for a walk, for gratitude, and for whatever the remaining hours might bring.

Nature doesn’t negotiate. It simply keeps showing up — sometimes a little earlier, sometimes a little later than we’d prefer. And perhaps that, too, is part of what we’re meant to notice.

Categories
AI

The Quiet Setup: MacSparky’s Robot Assistant and the Unfair Advantage Still Available

A single X post caught my attention this week. It described something quietly happening among a small group of solo professionals. They aren’t working longer hours or grinding harder. Instead, they’ve built a particular kind of setup around AI that carries much of the load.

While most of us still treat powerful models as clever search bars—typing questions and copying answers—these folks have given the AI a rich folder of context, a briefing file that orients it to their world, connections to their tools, and routines that let it produce real work on its own. The result can look like the output of a small team. From the outside it reads as talent or luck. Up close, it’s mostly architecture.0

The post (from @zephyr_hg) emphasized that this advantage remains available because most people haven’t yet made the shift from one-off prompting to building persistent systems. It landed with me because it echoes so closely the practical territory David Sparks (MacSparky) has been mapping for months in his Robot Assistant Field Guide.

MacSparky’s Approach: From Chatbot to Persistent Colleague

David’s work centers on building a true personal assistant using Obsidian (for a local, plain-text knowledge base) and Claude (in its file-aware “Cowork” or project capabilities). The system isn’t a chatbot that forgets everything between conversations. It’s designed to remember your projects, preferences, and people; triage email in your voice; handle morning briefings; track tasks; process documents; and support weekly reviews—freeing you from what David calls the “donkey work.”

The key ingredients will sound familiar to anyone who read that X post:

  • A dedicated context layer (your Obsidian vault or structured folder) holding the details of how you work.
  • Briefing/instruction files that tell the model who you are and what good looks like.
  • Integrations that connect it to email, calendar, files, and other tools.
  • Skills and routines that turn one-time intentions into repeatable, low-friction action.

David has been refreshingly transparent about the journey. He experimented earlier with more fully autonomous agents and even shut one down after learning what felt reliable and aligned. The Robot Assistant Field Guide distills those lessons into videos, workshops, templates, and a starter kit that lets people build without needing to code.

Why This Matters Now

Both perspectives point to the same shift in stance: moving from “How do I prompt better today?” to “What kind of system do I want running alongside me every day?”

For me, at this stage of life, that question carries weight. I’m not chasing maximum output for its own sake. I want arrangements that protect attention and energy for what actually matters—deep reflection, family history work, thoughtful investing, writing that might be useful to others, and simply being present. A well-designed AI setup doesn’t just save minutes; it changes the texture of the day by reducing context-switching and repeated explanations.

It feels like finding a productive seam in the current moment of AI evolution—one of those hidden transitions where leverage quietly compounds if you’re willing to build the architecture.

The Door Remains Open

The encouraging message in both the X post and David’s teaching is that this isn’t locked behind rare talent or expensive infrastructure. The models are accessible. The patterns are becoming clearer. What’s required is the decision to treat AI less like a toy and more like a colleague you’re willing to orient and trust with real work.

I don’t have my own “robot assistant” fully built yet. I’ve been experimenting with custom agents, structured daily scans, and ideas like “The Observatory” for signal synthesis. Reading these sources side-by-side sharpened my sense of the next layer: giving the system a proper home, clear instructions, and meaningful recurring work.

If you’re a solo professional, creator, or lifelong learner feeling the press of too many small tasks, this is worth exploring. Start small. Build a modest context folder. Write a briefing file that captures how you think. Experiment with one routine. Iterate from there.

The setup that outworks the grind isn’t magic. It’s deliberate, learnable, and still wide open.

What setups are you experimenting with these days? I’d love to hear in the comments or on X.

Categories
Books History

The Absence of Cavalry

There is a moment on the second day at Gettysburg, in the trees below a hill nobody had bothered to name until that afternoon, when a colonel from Maine runs out of bullets. He has maybe eighty men left who can still stand. The ammunition wagons are somewhere behind him, or ahead of him, or nowhere at all — nobody has told him, because nobody knows. What he knows is the sound coming up the slope, which is the sound of more men than he has, coming again.

He gives the only order left. Fix bayonets. Then he does something that has no tactical name because it isn’t tactics, not really — he swings the end of his line forward like a door closing, and his men run downhill into an enemy that outnumbers them, screaming, empty rifles held out in front of them like something between a weapon and a prayer.

It works. It should not have worked. That’s the part that stays with you.

I read Michael Shaara’s The Killer Angels years into a working life that had already taught me the plain, unglamorous truth at the center of that charge — that almost every real decision gets made with less than you need to know. Not none. Less. You wait for the number that would make it easy, and the number doesn’t come, and the hill is still there, and eventually you have to move anyway. Chamberlain at Little Round Top is the version of that truth that Hollywood wanted, the one with a bayonet charge and a hill that becomes a metaphor. It’s a fine scene. It is not the one that has stayed lodged in me the longest.

The one that stays is a man who isn’t even at Gettysburg for most of the book he’s in.

Jeb Stuart was supposed to be Lee’s eyes. That’s the literal job — cavalry rides ahead, cavalry finds out where the enemy is and isn’t, cavalry comes back and tells the general the thing the general cannot see for himself. Stuart instead took his troopers on a long, glittering loop around the entire Union army, capturing wagons, making headlines, having — by every account, including Shaara’s — something close to the time of his life. And Lee, the whole time, was moving a hundred thousand men through Pennsylvania blind. Not blind because he was careless. Blind because the one man whose entire function was to prevent that blindness had gone looking for glory instead of information.

By the time Stuart rides back in, dusty and pleased with himself, the battle has already half-decided itself without him. Lee’s fury at him is quiet in the novel, almost tender, which is somehow worse than if it had been loud. There’s no scene of reckoning. There doesn’t need to be. The absence already did its work. Three days of an army feeling its way forward with its hands out, and somewhere behind all of it, a man who could have told them what was there, riding home late with wagons full of things nobody needed.

What stayed with me wasn’t Stuart’s vanity. It was the shape of the thing — the specific, structural loneliness of deciding while the person whose job was to reduce your uncertainty is off somewhere doing something that felt more important to him at the time. You don’t get to know, later, whether the information would have changed anything. You only get to know you didn’t have it, and you went ahead anyway, and now it’s history, or it’s Tuesday, or it’s both.

There’s a way of reading war novels where the tactics are the point, where watching intelligent people arrange forces on a map has an almost mathematical satisfaction. The Killer Angels gives you that if you want it; Shaara knew where every regiment stood on every ridge. But the book’s better trick is quieter. It keeps putting men in rooms — tents, really, canvas and lantern light — where the map has a hole in it, and asking them to act as though it doesn’t.

Longstreet knows the ground favors defense and says so, and is overruled by a man he loves, and does his duty anyway with the full weight of his own doubt still sitting in his chest. Chamberlain doesn’t know if the flank will hold and commits to it as though he does. Lee doesn’t know where the Union army actually is, for three of the most consequential days in American history, and moves his men forward on faith and old maps and the assumption that his cavalry would come back in time to tell him otherwise.

None of them get the number. They all have to decide before the fog lifts, because the fog was never going to lift on schedule, and the enemy does not wait for your confidence to catch up to your responsibility. That’s not really a Civil War lesson. It just happens to be dressed as one, in wool and brass buttons, on a July afternoon in Pennsylvania in 1863. It’s the same lesson wearing whatever clothes your own decade puts on it.

I don’t remember exactly where I was when I read it. This book doesn’t come back to me that way — it comes back as an idea I keep finding uses for, decades later, in situations that have nothing to do with ridgelines or rifles. Which might be the highest compliment a novel about a three-day battle can earn: that it stopped being about the battle, for me, somewhere around the second reading, and became a way of thinking about every room I’ve since sat in where the map had a hole in it and the clock kept running anyway.

Stuart did come back, eventually. Late, but he came back. I think about the men who never got their Stuart at all — who made the call, and never did find out what the missing piece would have told them, and had to live the rest of their lives inside a decision made permanently, gorgeously incomplete.

Categories
Aircraft Memories

Captain Gil Rud

Recently I wrote about a visit to the USS Constellation that I was very fortunate to make years ago. In that post I mentioned the captain of the ship without citing who he was.

This morning I realized I should do a bit of research and correct that – so I asked Gemini:

Who was the captain of the uss constellation who had previously led the blue angels?

Here’s how I got reacquainted with Captain Gil Rud:

That captain was Captain Gil Rud (USN, Ret.).

He served as the commanding officer and flight leader of the Blue Angels from November 1986 to November 1988, notably leading the team’s transition from the A-4 Skyhawk to the F/A-18 Hornet. Later in his career, he served as the commanding officer of the aircraft carrier USS Constellation (CV-64) from May 1993 to December 1994.

I continued:

When was he assigned to the Constellation? I remember him talking about taking it out of the Philadelphia Navy Yard?

And learned:

Your memory is spot on. Captain Gil Rud took command of the USS Constellation in May 1993, which coincided exactly with the end of her massive Service Life Extension Program (SLEP) overhaul at the Philadelphia Naval Shipyard.

From July 1990 until early 1993, the carrier was essentially torn down and rebuilt in Philadelphia to add an extra 15 years to her operational life. Captain Rud was assigned to the ship just as this grueling three-year yard period was wrapping up.  

He was the captain who literally “took her out” of the Philadelphia Navy Yard, leading the crew through the post-overhaul sea trials and commanding the carrier during its transit from the East Coast, around South America (as she was too large to fit through the Panama Canal), and back to her homeport in San Diego.

I continued this back and forth with Gemini learning about his career after the USS Constellation including his retirement from the Navy and work in private industry. Turns out he also authored a memoir: From the Prairie to the Pacific: A Blue Angel’s Journey.

Afterwards I felt better having done this research and getting reacquainted with Gil Rud.

Categories
Business Creativity Innovation Inspiration

The Idea That Won’t Let Go

“There are three elements to every great idea: 1. It solves for ‘why.’ Long before you figure out what a product will do, you need to understand why people will want it. The ‘why’ drives the ‘what.’ 2. It solves a problem that a lot of people have in their daily lives. 3. It follows you around. Even after you research and learn about it and try it out and realize how hard it’ll be to get it right, you can’t stop thinking about it.”
— Tony Fadell, Build

The third element is the only one that isn’t optional. You can fake the why — retrofit it, hire a consultant to write it on a slide in a font called Montserrat. You can borrow the what; most products are just other products in a better jacket. You cannot fake the third thing, because the whole test of it is that it happens without your permission.

Call it a visitation. It shows up uninvited at the stoplight, in the shower, at 2:40 a.m. when the ceiling becomes a screen for it. You know the unit economics don’t work. You know the regulatory path is nine years long. You know — you know — that fourteen better-funded people already tried this and left with nothing but a Delaware C-corp and a grudge. None of it helps. The idea has already filed its paperwork. It lives here now.

You cannot bullet-point a visitation. There is no OKR for thinking about landing gear hydraulics while your wife is telling you about her sister’s wedding. Everyone who’s built something that mattered will tell you this, late enough at a dinner and honest enough on the wine: not conviction, not passion — that laminated word — but the visitor, still standing in the doorway of every other thought you’re supposed to be having.

It doesn’t check your calendar. It doesn’t care that you’ve moved on to point two, the sensible one, the one venture capitalists nod along to with their flat whites going warm. It was never about the market. The market is the alibi you build afterward, so you don’t have to say the true thing: I didn’t choose it. It moved in while I was asleep, and my life rearranged itself around the shape of it, the way a house rearranges itself around a family it didn’t ask for.

Maybe the third element isn’t a filter for the idea. Maybe it’s a filter for you — a way of finding out, by accident, at 2:40 in the morning, staring at a ceiling that has decided to keep the light on.

Categories
Cars Design Honda

The Shape of Fear

There’s a red-and-silver Honda CRX that shows up in a parking lot near a park I walk regularly. Not always — it’s not a daily thing — but often enough that I’ve started to look for it. When it’s there, I stop. I stare longer than a car deserves. I’ve owned other Hondas. I never owned this model, and by the time I could have, I was a family man, and a two-door coupe with a back seat that barely qualifies as a suggestion wasn’t a thing you brought home. That’s still true. I still love it anyway.

For a long time I thought the pull was nostalgia — an artifact from a specific decade doing what artifacts from specific decades do, standing in for the whole era around them. But nostalgia doesn’t usually make you stop walking.

Something more specific was going on, and I only located it recently, looking at a rendering of Tesla’s Cybercab: the same silhouette. Not the same car, not the same era, not the same anything except the one thing that matters most in a side profile — the roofline. Low nose, a peak over the front seats, one continuous downward sweep to a short, cropped tail. No break at the B-pillar to speak of. Glass that continues the line of the roof instead of interrupting it.

Two cars, forty years apart, arriving at the identical answer to a formal problem. That’s the kind of coincidence that isn’t really a coincidence — it’s a shape that keeps getting rediscovered whenever the constraints line back up.

The Cybercab gets there because there’s no driver’s compartment to package around, no B-pillar structure fighting for space, nothing back there to make room for. The roofline can just fall away because there’s nothing left to interrupt it.

The CRX got there from the opposite direction — not by subtraction of function but by subtraction of everything else. Weight. Drag. Ornament.

What I didn’t expect, going looking, was how much fear was baked into that shape.

The CRX wasn’t dreamed up in some skunkworks with time to spare. It came out of what Honda’s own people described, at the time, as something close to an image crisis — the third-generation Civic was about to launch into a market with sharper competitors than the last one, and the man responsible for small-car development at Honda R&D was worried the company’s whole small-car identity was aging out from under it. The response was billed internally as a kind of renaissance, and the CRX was its opening statement — not a side project, but the leading edge of an “all-out attack.”

The person who actually shaped it, Hiroshi Kizawa, had already put his career on the line once, on the original Civic — a car he believed Honda’s future as a real manufacturer depended on. He came back and did it again, smaller and stranger this time: a two-seat coupe, under 900 kilograms, wrapped in plastic body panels molded in-house, chosen partly because they could someday be recycled — Honda thinking, in 1981, about what happened to the car after its life was over, which is its own small strangeness worth sitting with.

The reception at home was lukewarm in a way I find almost endearing now. One Japanese trade magazine at the time called it a dehydrated Camaro with some boy-racer posturing, allowing that it might not be beautiful but was at least likeable. That’s a strange epitaph for a car I’d call one of the most purely resolved shapes of its decade. But maybe that’s how it goes with real design — the people closest to it, watching it get made under pressure, can’t yet see what it will look like from forty years out, parked in a lot, still stopping people who weren’t even born when it launched.

Less than six months after the CRX reached showrooms, Honda started work on what would eventually become the NSX. The unglamorous little economy coupe, born from institutional anxiety and injection-molded plastic, turned out to be the warm-up act for the most serious sports car the company would ever build. Fear, it turns out, is not a bad place to start, if the people afraid of it are good enough to turn it into something worth being afraid for.

Which makes me think of Ferrari’s own version of this moment, playing out right now. Their first electric car, the Luce, is exactly the kind of institutional fear the CRX was born from — a company that has to prove it still belongs to the future, using a technology it didn’t choose. And where Honda answered that fear with a shape, a single unbroken line that turned scarcity into style, Ferrari answered it with a four-door liftback, roomy and glassy and, by most early accounts, nobody’s idea of a Ferrari silhouette. I wish they’d gone the other way. I wish somebody at Maranello had looked at what a wedge does when you strip a car down to its constraints — no engine bay to hide, no B-pillar to interrupt, nothing left but the line from nose to tail — and had the nerve to make the Luce look like it was afraid of something, the way the CRX clearly was.

I think about that shape differently now — not as a wedge from the eighties, and not as a preview of some robotaxi’s rendering either, but as a shape that seems to arrive whenever a design team is left with almost nothing to hide behind. No engine bay to speak of. No back seat to protect. No driver at all, in one case. What’s left, both times, is the same honest line — nose to tail, unbroken — and I wonder what it says that the shape survives every reason for making it, outlasting the fear and the plastic and the market anxieties that produced it, showing up again decades later for reasons nobody involved the first time could have guessed.

Categories
AI Podcasts

A Remarkable Conversation…

Highly recommend this conversation between Harry Stebbings and Clay Bavor. Among many topics, I especially enjoyed the discussion about not investing in frontier models, the important values, the particular importance of craftsmanship, intensity, and family. And the special conversation about parenting and kids near the end. Just a delightful conversation to be able to enjoy!

Key Highlights:

• Founding Sierra: Bavor explains why he and Taylor chose to start Sierra, focusing on the transformative potential of language model-based agents (1:37 – 5:53).
• The AI Tech Stack: Sierra focuses on building enterprise-grade agent architectures and fine-tuning models on top of open-weights models rather than pre-training foundation models from scratch, prioritizing capital efficiency (5:53 – 7:15).
• Unbounded Demand for Intelligence: Bavor argues that there is massive, unmet demand for “frontier-level” intelligence in fields like coding, science, and legal work (7:15 – 11:41).
• Internal AI Operations: He details the use of Pinecone, an internal AI agent Sierra developed to navigate company data, streamline engineering, and assist in recruitment (18:36 – 22:00).
• Enterprise Strategy: Sierra employs a “forward-deployed” engineering model, embedding staff within client companies to ensure rapid, effective integration of AI, leading to quick deployment timelines (30:12 – 33:22).
• Board Governance: To keep pace with the speed of AI development, Sierra operates on a six-week board meeting cadence, utilizing comprehensive memos instead of traditional slide decks (39:07 – 41:13).
• Corporate Culture: Bavor emphasizes values like craftsmanship, intensity, and family. He also highlights the importance of working in-person to foster apprenticeship, mentorship, and a cohesive team culture (43:02 – 55:41).