Tag: AI Operations

  • The Hybrid Imperative: What Formula 1 Can Teach Us About AI, Humans, and the Race Nobody Saw Coming

    The Hybrid Imperative: What Formula 1 Can Teach Us About AI, Humans, and the Race Nobody Saw Coming

    There’s a fight happening in the most expensive, most scrutinized, most technically demanding sport on earth — and it has nothing to do with tires or teammates. It’s a fight about what it even means to race.

    Max Verstappen, four-time world champion, the most dominant driver of his generation, called Formula 1’s new 2026 cars “Formula E on steroids.” He said driving them isn’t fun. He said it doesn’t feel like Formula 1. He said — and this is a man who has never once seriously contemplated stopping — that he might walk away.

    Let that land.

    The man who won four consecutive world championships, who drove circles around the field while the rest of the paddock scrambled to understand how, is sitting in the fastest car ever built and saying: I don’t enjoy this.

    Why? Because the car now thinks.

    Not literally. But close enough that it matters. The 2026 power unit splits propulsion roughly 50/50 between the internal combustion engine and an electric motor delivering 350 kilowatts — nearly triple what it was before. The car harvests energy under braking, on lift-off, even at the end of straights at full throttle in a mode called “super clipping.” Up to 9 megajoules per lap, twice the previous capacity, stored, managed, and deployed in a continuous loop of harvesting and releasing that never stops.

    Split view of classic V10 F1 engine with fire on the left versus modern hybrid electric power unit with blue circuits on the right
    Fire and electricity. The old F1 and the new — not opposites, but two halves of something more powerful than either alone.

    You’re not just driving anymore. You’re managing a conversation between two completely different power systems — one that roars, one that hums — while hitting 200 miles per hour and making decisions in fractions of seconds that determine whether you win, crash, or run out of energy in the final corner.

    Lando Norris, the reigning world champion, said F1 went from its best cars in 2025 to its worst in 2026. Charles Leclerc said the format is “a f—ing joke.” Martin Brundle told Verstappen to either leave or stop complaining. The entire paddock is arguing about what the sport is supposed to be.

    And none of them realize they’re having the exact same argument happening in every boardroom, every startup, every kitchen table business in the world right now.

    The Either/Or Was Always Wrong

    For the past few years, the conversation about AI has been framed as a binary: human or machine. Replace or be replaced. Use it or lose to someone who does. Old way or new way.

    This is the Verstappen position, and I say that with respect — because Max is right that the old feeling is gone. He’s just wrong about what that means.

    Formula 1 didn’t abandon the combustion engine. They didn’t go full electric. They didn’t pick a side. They built something harder, something that demands more from drivers, not less — because now you have to be brilliant at two things simultaneously and know when to lean on each one.

    The drivers who are thriving in 2026 stopped mourning what the car used to feel like and started learning the new language.

    They’re harvesting energy through corners where they used to just brake. They’re deploying battery power in ways that look, from the outside, like supernatural acceleration. They’re thinking three moves ahead — not just about position, but about energy state.

    That’s not easier than pure combustion racing. It’s harder. But it’s a different kind of hard. Sound familiar?

    Business Is an F1 Track — and It Changes Every Race

    First-person cockpit view inside a Formula 1 car at speed, with digital energy harvest HUD overlays
    Every lap is a new calculation. Harvest here, deploy there — the dashboard never tells you the answer, only the state.

    Here’s what makes Formula 1 genuinely profound as a metaphor: the tracks are different every single week. Monaco demands precision and patience. Monza demands raw speed. Spa demands bravery in rain. Singapore demands night vision and inch-perfect walls. The same car, the same driver, the same team — and yet the setup, the strategy, the tire choice, the energy management plan all have to reinvent themselves race by race.

    Business is no different. What worked in Q4 last year fails in Q1 this year. The competitive landscape that was stable for a decade reshapes overnight. A supply chain that was reliable becomes fragile. A channel that was growing saturates. A customer who was loyal gets poached.

    The teams that win championships don’t win because they figured out the perfect setup. They win because they built the organizational capability to adapt faster than everyone else.

    The old AI conversation asked: should I automate this? The new one asks something harder: what’s my energy state right now, and what does this moment call for?

    The Dance Nobody Taught You

    The 2026 F1 energy system doesn’t work like a switch. You can’t just floor it and let the battery do its thing. You have to harvest before you can deploy. You have to give before you can take. You have to think about the lap you’re on and the lap you’re about to run and the laps after that, all at once.

    This is the part of AI integration that nobody talks about in the breathless headlines about productivity gains and job displacement.

    The best operators I’ve seen aren’t using AI like a vending machine — put prompt in, get output out. They’re in a dance. They bring the domain knowledge, the judgment, the instinct built from years in the field. The AI brings the pattern recognition, the synthesis, the ability to hold fifty variables in mind without forgetting one. Neither is complete without the other. Both are diminished when treated as a substitute for the other.

    The driver who just mashes the throttle and trusts the battery to save him will run out of energy in Turn 14 and coast to the pits. The driver who ignores the electric system entirely and tries to drive the 2026 car like a 2015 car will be half a second off pace before the first chicane. The dance — the real skill — is knowing when you’re in harvesting mode and when you’re in deployment mode, and making that transition so smooth that from the outside it just looks like speed.

    Max Was Right About One Thing

    Verstappen isn’t wrong that something was lost. The howl of a naturally aspirated V10 at 19,000 RPM is an irreplaceable thing. The feeling of a car that responds to pure mechanical input — no management, no algorithms, just physics and nerve — that’s real, and mourning it is legitimate.

    The track doesn’t negotiate.

    The regulations don’t care what you loved about the old car. The competitor who masters the new system while you’re grieving the old one is already three tenths faster. The market doesn’t pause while you decide whether you’re comfortable with how things are changing. The question was never do I have to change. The question is always how fast can I learn the new dance — because the music already changed, and the floor is moving.

    A Word About Williams — and a Disclosure Worth Making

    Williams Formula 1 car in white and blue livery at sunset with a glowing AI aura
    Williams Racing — F1’s great independent, now with Claude as its Official Thinking Partner. The future of racing looks a lot like the future of business.

    Williams Racing — one of Formula 1’s most storied teams, the last truly independent constructor in the paddock — just named Claude their Official Thinking Partner in a multi-year partnership with Anthropic.

    My name is William Tygart. I use Claude every single day. And now Claude is on the side of an F1 car driven by one of racing’s most legendary teams. I’ll let you make of that what you will.

    But the reason this partnership makes sense says something important. Williams isn’t Red Bull with unlimited resources. They’re not a manufacturer team with a factory army. They are, as Anthropic’s head of brand marketing put it, “world-class problem solvers focused on the smallest details.” They win not by outspending, but by out-thinking. That’s the promise of genuine AI partnership — not replacing the engineers, but serving as the thinking partner that helps brilliant people think better.

    The Harvest Before the Deploy: A Framework

    • Identify your harvesting moments. Where is knowledge being created in your operation that isn’t being captured? Where are patterns repeating that nobody’s noticed? AI harvests those moments — but only if you build the conditions for it.
    • Identify your deployment moments. Where does speed matter most? Where is the bottleneck not ideas but execution velocity? Those are your deployment moments — where the stored energy gets released.
    • Practice the transition. The driver who only harvests never wins. The driver who only deploys runs dry. The rhythm — harvest, deploy, harvest, deploy — has to become organizational muscle memory.
    • Accept that the track changes. What worked at Monaco won’t work at Monza. Build teams and cultures that don’t just tolerate adaptation but expect it, plan for it, and practice it constantly.

    The Race Is Already On

    Max Verstappen may or may not be in Formula 1 next year. The paddock may or may not sort out its feelings about the 2026 cars. But the cars will race. The energy will be harvested and deployed. And somewhere on the grid, a driver who stopped arguing with the regulations and started mastering the new system will cross the finish line first.

    The same is true in your industry. The debate about AI is real and worth having. But while it’s happening, the race is underway.

    The hybrid era isn’t coming. It’s here. The only question is whether you’re learning the dance.


    Sources: Verstappen on walking away — ESPN | Verstappen: “Formula E on steroids” — ESPN | 2026 F1 Power Unit Explained — Formula1.com | Anthropic × Williams F1 — WilliamsF1.com | Verstappen future uncertain — RaceFans

  • I Don’t Have a Morning Routine. I Have a 3am Shift.

    I Don’t Have a Morning Routine. I Have a 3am Shift.

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    Everyone I talk to about AI eventually asks the same thing: “How do you use it to work faster?”

    I’ve stopped trying to answer that question. Because it’s the wrong one.

    The better question — the one that actually describes what’s happening at my end — is: what does it do when I’m not watching?

    The answer is: a lot. And most of it happens at 3am.

    What Actually Happens at 3am

    There’s a Google Cloud virtual machine I’ve been building for months. It runs on a small Compute Engine instance in GCP’s us-west1 region. During the day I’m in and out of it — deploying code, running optimizations, publishing articles to client sites. But the interesting stuff happens after I close the laptop.

    At 3am Pacific time, a cron job fires. It kicks off a content pipeline that pulls from my second brain — a BigQuery database that logs every working session I’ve ever had with Claude — identifies knowledge gaps across a set of websites I manage, writes articles to fill them, optimizes them for search, and publishes them to WordPress. By the time I wake up, there are new posts live on sites I didn’t touch.

    The session extractor runs on a different schedule. Every time I finish a Cowork session, a job logs everything that happened — what was built, what was decided, what failed, what’s next — into Notion with a date stamp and status markers. The next session reads that log before doing anything else. Context that would have evaporated gets carried forward. The machine remembers so I don’t have to.

    There are 17 scheduled jobs running on that VM right now. SEO scorecards that refresh on the first of the month. Social media batches that fire every three days. A second brain intelligence dashboard that updates itself and surfaces what’s trending in my own knowledge base. An AI receptionist prototype I’m building for a client that processes intake calls through Twilio and logs them to Firestore — all without a human in the loop.

    3am Shift — Automated Pipeline Running
    Each node in the pipeline triggers the next. No one has to push a button.

    The Morning Routine That Isn’t One

    My mornings used to start with a list. Now they start with a report.

    The daily briefing in Notion tells me what the overnight runs produced — which articles went live, which pipelines succeeded, which ones hit an error and why, what the status is on every client and project. Red, yellow, green. By the time I’ve had coffee, I know the state of everything without having asked a single question.

    The second brain intelligence dashboard is the part that still surprises me. It tracks what topics are heating up across all my knowledge nodes — which subjects are getting more mentions, more connections, more cross-references. On any given morning it might surface that “agentic commerce” has spiked, or that my restoration intelligence cluster has thinned out and needs new content. I didn’t build an alarm system. I built something that tells me what to pay attention to before I know I should be paying attention to it.

    The whole thing runs on maybe $40–60/month in GCP compute. The VM is an e2-standard-2. Not a supercomputer. What makes it powerful isn’t the hardware — it’s the fact that it’s always on, always running, and always logged.

    3am Shift — Unattended Dashboard Updating
    The dashboard updates on its own. By morning, the state of everything is already known.

    The Moment It Clicked

    There was a specific moment when I understood what I was building was different from “using AI tools.”

    I was running a music generation pipeline — an experiment where Claude was creating and evaluating short audio clips, keeping the ones that met a quality threshold and discarding the rest. At some point during the run, the pipeline stopped. Not because of an error. Because Claude evaluated the output, decided it wasn’t good enough, and called sys.exit(). It halted itself.

    I called it the Autonomous Halt. The article about it is on this site if you want the full story. But the feeling in that moment — reading the log and realizing the system had made a judgment call without me — was unlike anything I’d experienced with software before. It wasn’t just automation. It had opinions about its own output.

    That’s when the shift happened in how I think about this. The question stopped being “how do I get AI to help me work” and became “how do I build a system that works, and then stay out of its way.”

    What This Changes About How I Work

    The conventional productivity conversation is about reclaiming time. You delegate tasks to AI, you get hours back, you use those hours to do higher-value things. That’s real and I don’t dismiss it.

    But the thing that’s actually happened for me is different. It’s not that I have more hours. It’s that the category of work that requires my presence has gotten much smaller and much clearer.

    The 3am shift handles content. It handles monitoring. It handles routine optimization, publishing, reporting, and logging. What’s left for me is judgment — the things that require knowing the client, reading the room, making a call that doesn’t have a clear right answer. Strategy. Relationships. New ideas. The stuff that benefits from a human being actually thinking, not executing.

    The SEO portfolio I manage runs at about $168,000/month in tracked search value across 22 domains. That number grew while I slept. Not metaphorically — the articles published at 3am indexed, ranked, and accumulated traffic value while I was nowhere near a keyboard.

    3am Shift — Night and Day Split
    Night is when the work happens. Day is when I decide what it means.

    What It Takes to Get Here

    I want to be honest about something: this didn’t happen overnight and it didn’t happen by accident. The 3am shift is the result of a lot of deliberate architecture decisions, a lot of failed pipelines, a lot of sessions that ended in error logs instead of published articles.

    The session extraction system — the one that logs context to Notion so the next session can pick up cold — that took three iterations to get right. The first two versions lost too much context and the logs were too vague to be useful. The third version extracts structured data: what was built, what failed, what was decided, what’s next. That specificity is what makes the loop work.

    The cron jobs took longer than they should have to set up properly, mostly because I kept trying to run them from the wrong place. The Cowork VM is too constrained. The knowledge-cluster-vm on GCP is the right home — persistent, always on, with the credentials and tools pre-loaded. Once that decision was made, the automation clicked into place quickly.

    The second brain itself — the BigQuery database that everything feeds into — was the foundational investment. Without a structured knowledge store, the 3am pipeline has nothing to pull from. The intelligence is only as good as what’s been logged.

    None of that is glamorous. Most of it was debugging. But the result is a system that genuinely works while I’m not working, and that’s a different category of thing than a faster workflow.


    Most people ask how I use AI. The better question is what it does when I’m not watching.

    The answer, lately, is most of the work.

  • The Loop Has To Go Both Ways — Tygart Media Visuals Visual

    The Loop Has To Go Both Ways — Tygart Media Visuals Visual

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  • Split Brain Architecture AI Content Operations — AI & Technology Concepts Visual

    Split Brain Architecture AI Content Operations — AI & Technology Concepts Visual

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  • Stop Building Inventory Build The Machine — AI & Technology Concepts Visual

    Stop Building Inventory Build The Machine — AI & Technology Concepts Visual

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  • UCP Universal Commerce Protocol AI Agents — Article Hero Images Visual

    UCP Universal Commerce Protocol AI Agents — Article Hero Images Visual

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