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AI Anthropic Business Google

The Weight of the Bill

Jordi Visser has been making the case for months — in his weekly YouTube commentary and on his Substack — that we are living through an exponential transition that most people are measuring with the wrong instruments. I think he’s right. I found two data points this week that suggest why.

I was somewhere in the middle of an Invest Like the Best episode when Dylan Patel said it — almost as an aside, the kind of thing you drop to establish context before moving on to the point you actually came to make. His firm, SemiAnalysis, analyzes the semiconductor and AI industries for a living. And their usage of Claude, he noted, has been growing. The costs have been growing too.

Exponentially.

He moved on. I didn’t.

I think Patel’s API bill might be one of the more honest documents in the current AI moment — more honest than the analyst reports his firm produces, more honest than the earnings calls where every public company performs its AI fluency for shareholders.

Surveys bend. When you ask someone whether they’re using AI in their work, you’re asking them to self-report on a technology that has become a proxy for relevance, for not being left behind. The incentive to say yes is enormous. And even when the yes is genuine, it tells you nothing about depth — whether AI has become load-bearing in how someone actually works, or whether it’s an impressive thing they do occasionally.

Nobody pays exponentially growing API costs for show. Money is the honest witness.

What makes Patel’s situation quietly strange is the recursion in it. SemiAnalysis exists to help sophisticated investors and technologists understand this industry — and they cannot predict their own consumption curve. They are inside the exponential the same way everyone else is. They just happen to be watching their bill.

Then this morning, a different number arrived. Google announced it will invest up to $40 billion in Anthropic — $10 billion committed now, another $30 billion contingent on performance milestones. This follows a separate $5 billion from Amazon, part of a broader arrangement under which Anthropic is expected to spend up to $100 billion on compute over time.

The temptation with numbers like these is to treat them as spectacle. Forty billion dollars is so large it becomes almost aesthetic — a statement about ambition, about the kind of bets that define eras. You feel the weight of the zeros and move on.

But I keep coming back to Patel’s API bill.

Because Google’s $40 billion and SemiAnalysis’s compounding monthly costs are saying the same thing, expressed at scales so different they almost don’t seem related. One is a research firm noticing that their tool usage has quietly escaped prediction. The other is one of the most sophisticated capital allocators on earth making a bet that strains comprehension. But both are pointing at the same reality: that this technology, wherever it takes hold, does not plateau. It compounds.

We have been waiting, I think, for the moment when AI adoption becomes legibly real — some threshold event that separates the signal from the noise, the press release from the actual change. The surveys were supposed to mark that moment. The enterprise announcements. The benchmark numbers.

Patel’s aside suggests we’ve been waiting for the wrong thing. You don’t arrive at the exponential. You just eventually notice you’re already in it — in an aside on a podcast, before moving on to the point you actually came to make.

Categories
AI

The Geometry of Speed

We are surprised when witnessing something move faster than our intuition expects. We are inherently wired to understand slow, compounding growth. We expect the long, grinding years of the plateau—the quiet periods where nothing seems to happen before a sudden breakthrough.

I was looking at a chart Patrick Collison shared this morning, and it challenged that very intuition. It’s a simple, stark visualization: AI model intelligence relative to the formation date of the lab that built it.

If you trace the lines for Google and OpenAI on the right side of the graph, you see the history we’ve all lived through. Thousands of days—more than a decade of quiet, methodical, often unglamorous research—before their trend lines finally bend and shoot upward. It is a geometry of patience. It’s the visual representation of laying bricks, one by one, year by year, until you have a foundation sturdy enough to support the weight of a revolution.

And then, on the far left of the chart, there is a red line. MSL. The team behind Meta’s new Muse Spark model, released today.

The red line doesn’t curve. It doesn’t slope. It simply strikes straight up, like a lightning bolt in reverse.

In roughly 200 days since formation, this new effort achieved a level of capability that took the early pioneers thousands of days to reach. Collison noted how much he loves seeing things done quickly, and it’s hard not to share that specific, visceral thrill of seeing the boundaries pushed so aggressively.

I find myself thinking about the architecture of speed and what it means for the rest of us.

We spend so much of our lives absorbing the lesson that “good things take time.” We are taught that the crucible of meaningful work requires a long, slow simmer. And mostly, that remains true. The compound interest of human experience is real, and wisdom is rarely rushed.

Yet, every once in a while, a new paradigm emerges that doesn’t just accelerate the timeline—it collapses it entirely.

The pioneers cut the agonizingly slow path through the jungle, taking the brunt of the time, the friction, and the missteps. The ones who follow—like xAI, Anthropic, and now MSL—don’t have to clear the brush from scratch. They can look at the map, pave the road, and simply drive.

What does it mean for our own mental models when the timeline from “formation” to “frontier” shrinks from five thousand days to a few hundred?

It is a jarring reminder that the past pace of performance is not a law of physics.

I think about my own assumptions—how often I assume a project, a habit, or a societal shift will take a while, simply because similar things took a while in the past. We anchor our expectations to old geometry.

Meta’s release of Muse Spark is a technical feat, certainly. But the chart itself holds a broader, more human lesson. It’s a visual prompt to constantly re-evaluate our assumptions about how long the impossible is supposed to take.

The future doesn’t always arrive on a comfortable, predictable schedule. Sometimes, it just shows up unannounced, demanding we adjust our stride to keep up.

Categories
Music

Every Blog Needs a Theme Song!

Google has added a new music generation model called Lyria 3 to its Gemini 3 models.

I was playing around with it last night – having it generate happy birthday greetings for a friend whose birthday is coming up in a few days, another song for a longtime business partnership I was part of, and more. It’s kind of crazy! And a lot of fun.

When you use Lyria 3 as a tool in Gemini 3 you get back an image and an MP3 file that’s 30 seconds long (longer coming soon according to Google). Turns out the 30 second length is just about perfect for the “quick hit” from a snippet of music.

Google provides several genres you can choose from to start with – or you can just go with whatever you want to say in the prompt – here’s a rough template for doing that:

[Topic] + [Genre] + [Mood] + [Instruments] + [Vocals]

This morning I went for my morning walk and had a thought – how about generating a theme song for my blog. So when I got back home I opened up Gemini, selected the Music tool and entered:

Take a look at my blog and compose my theme song! blog: https://sjl.us

You can see with that prompt that I really didn’t provide it much direction – just a pointer to my blog so that it could try to generate something appropriate.

It took a few seconds for Lyria to read my blog and then use what it found to generate my blog’s theme song – and I like it!

You can play the theme song for yourself here:

Categories
AI

The Second Fire: From Finding to Forming

There is a specific kind of vertigo that comes with a paradigm shift. It’s the feeling of standing on the edge of a map that has just been unrolled to reveal twice as much territory as you thought existed. Lately, as I navigate the vast, generative landscape of AI, that old vertigo has returned. It’s a hauntingly familiar resonance, a structural echo of the late nineties and early 2000s when we first encountered the Google search bar.

Back then, the world was a series of closed doors. Information was siloed in physical libraries, expensive encyclopedias, or the unreliable oral histories of our social circles. Then came that clean, white interface with a single blinking cursor. Suddenly, the friction of “not knowing” began to evaporate. We weren’t just browsing the web; we were suddenly endowed with a collective memory. It felt like a superpower—the ability to summon any fact from the digital ether in milliseconds.

“Google is not just a search engine; it is a way of life. It is the way we find out who we are, where we are going, and what we are doing.”

Today, the sensation is different in texture but identical in weight. If Google gave us the power to find, AI is giving us the power to form.

The “Aha!” moment of 2026 isn’t about locating a PDF or a Wikipedia entry; it’s the realization that the distance between a thought and its realization has shrunk to almost nothing. When I prompt a model to synthesize a complex theory or visualize a dream, I feel that same electric jolt I felt twenty years ago when I realized I’d never have to wonder about a trivia fact ever again.

But there is a philosophical weight to this new “awesome.” With Google, the challenge was discernment—filtering the flood of information to find the truth. With AI, the challenge is intent. When the “how” becomes effortless, the “why” becomes the only thing that matters. We are moving from the era of the Librarian to the era of the Architect.

We are once again holding a new kind of fire. It’s warm, it’s brilliant, and just like the first time we saw that search bar, we know that the world we lived in yesterday is gone, replaced by a version where our reach finally matches our imagination.

Categories
AI Books Google NotebookLM San Francisco/California Writing

The 280 Project

Way back in 2016 when I was contemplating my retirement, I found myself pondering what projects might keep me engaged once my long-standing career in payments consulting came to an end. One compelling idea that emerged during this reflective period was the prospect of writing another book. This time, I envisioned the topic focusing on the intriguing story behind Interstate 280, often referred to as “the world’s most beautiful freeway.”

Our family’s migration from the Midwest to California took place in the early 1960s, a time when the interstate highway system in the San Francisco Bay Area was still a work in progress. At that point, I-280 had not yet been completed. As I approached the age of obtaining my driver’s license and gained the freedom that came with access to a car, I remember setting off on explorative drives down the peninsula. During those excursions, I gradually became aware of the ongoing construction and development involved in building this iconic road.

Eventually, after years of planning and labor, I-280 was completed in the early 1970s. At that time, I was working for IBM and was engaged in a project that took me down to an IBM lab facility located on Sand Hill Road—a place that has since vanished. Driving along I-280 during those initial years was an absolute delight, with the smooth asphalt feeling fresh and new under my tires. The experience of traversing a well-constructed highway surrounded by natural beauty was euphoric.

Sidenote: that IBM lab on Sand Hill Road was where Gene Amdahl was working on what turned out to be his last project working for IBM. That project was abruptly terminated one day and Amdahl left to found what became Amdahl Computer, developer of the first of the serious IBM mainframe “clone” threats.

In stark contrast to other freeways that meander through urban landscapes or feature monotonous views, 280’s route is distinguished by its breathtaking scenery. The rolling hills, lush vegetation, and stunning vistas create a picturesque drive that sparkles in comparison to its sibling highway, US 101, which navigates through the more densely populated areas closer to San Francisco Bay.

As I brainstormed the possibility of transforming my interest in I-280 into a full-fledged book project, I realized there must be an abundance of fascinating stories to uncover regarding the history of this highway—particularly pertaining to how the route was established and agreed upon. To delve deeper into this narrative, I invested considerable time gathering a wealth of documents. A few hours of dedicated Google searches yielded a treasure trove of information, which I organized into a folder for easy access. However, I soon found myself lacking a clear methodology for effectively utilizing these documents to craft an engaging narrative.

Recently, I have begun experimenting with Google’s NotebookLM, which appears to be tailored precisely to meet my needs. This innovative tool allows me to input numerous documents and then facilitates various inquiries about the collected material. I can explore whether there are any captivating and compelling stories waiting to be told. As I embark on this new journey of exploration, I am filled with a sense of excitement and renewed vigor for my little project. While it remains uncertain whether a full-fledged book will emerge from this endeavor, I am intrigued by the possibilities and look forward to seeing how this story unfolds. Perhaps this exploration will not only breathe life into my ideas but also provide a narrative worth sharing with others. We shall see!