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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 Google Google Gemini

Fun with Nano Banana 2

Google just released a new version of its image creation tool Nano Banana. It’s pretty amazing at creating all kinds of images.

On X a prompt was shared that I wanted to try out:

I need a flowchart for how to scramble eggs, make it as wacky and over the top and complicated as possible.

So I gave it a try:

Here are a couple of additional examples:

What a McKinsey partner does to prepare for a client’s board meeting presentation

The credit and debit card systems in the U.S.

David Allen’s Getting Things Done methodology

Pretty amazing! Conceiving and drawing one of these “flowcharts” would take me many hours!

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Google Google Bard Google Gemini Google NotebookLM

Dive Deeper with Google’s NotebookLM: A Researcher’s Dream Tool

Remember that mind-blowing Google I/O demo of an AI tool that unlocks hidden insights from your research documents? That’s NotebookLM, and it’s not just for tech giants anymore. (See this earlier blog post about what was originally Project Tailwind.)

As a longtime reader of author Steven Johnson (and avid follower of his “Adjacent Possible” Substack), I was thrilled to learn he’s now part of the team at Google Labs bringing this powerful technology to the masses.

Imagine uploading piles of research papers, articles, or even future forecasts (like I did with those year-end reports from Wall Street investment houses forecasting what’s expected in 2024!), and then having NotebookLM not only summarize them but also weave connections you might have missed. That’s exactly what I experienced.

NotebookLM’s “additional questions” feature is a game-changer, prompting me to explore angles I wouldn’t have considered on my own. It’s like having a tireless research assistant with an uncanny knack for spotting crucial details.

Of course, NotebookLM is still in its early stages. The current 20-document limit can feel restrictive, and its future as a paid product is unclear. But for researchers grappling with mountains of information, it’s a game-changer. It’s not just about saving time; it’s about sparking genuine intellectual leaps.

This tool isn’t just for academics, though. Imagine journalists using NotebookLM to connect seemingly disparate news articles, or students piecing together complex historical narratives. The possibilities are endless.

Sure, like any AI tool, it’s not perfect. Fact-checking is crucial, and occasional “hallucinations” can crop up. But NotebookLM’s source citations make verification easier, and its overall accuracy is impressive so far.

So, ditch the highlighter and embrace the future! NotebookLM isn’t just a fancy research tool; it’s a bridge to deeper understanding, more insightful analysis, and ultimately, groundbreaking discoveries. Unleash your research potential – your next breakthrough might just be a question away.

For more about this new tool, see this interview with Steven Johnson by Jason Calacanis on his This Week in Startups podcast.

Note: this post reflects some editing assistance I got from Google Bard.

Categories
AI AI: Large Language Models ChatGPT Google Google Bard

Looking forward to Project Tailwind…

This week at Google IO, one of the projects covered was a new experimental one called Project Tailwind – see how Steven Johnson covered it on his Substack after the event. He’s been working part-time with Google on this project which he describes Tailwind this way:

Tailwind allows you to define a set of documents as trusted sources which the AI then uses as a kind of ground truth, shaping all of the model’s interactions with you. In the use case shown on the I/O stage, the sources are class notes, but it could be other types of sources as well, such as your research materials for a book or blog post. The idea here is to craft a role for the LLM that is not an all-knowing oracle or your new virtual buddy, but something closer to an efficient research assistant, helping you explore the information that matters most to you. 

Google’s one line description is: “Tailwind is your AI-first notebook, grounded in the information you choose and trust.”

While working with the existing chatbots (ChatGPT, Google Bard, Microsoft Bing, etc.) is fun and useful, I’d be much happier having a research assistant which would primarily function on content I’ve created with an option to go beyond my content to the wider world. Johnson says he has “found that Tailwind works extremely well as an extension of my memory.”

Google’s initial implementation of Tailwind is based upon files in your Google Drive. For privacy reasons particularly, I’d especially welcome such a feature being trained and used locally on my own computer rather than having to upload my content to Google Drive and a cloud trainer.

I’ve requested access to Project Tailwind and look forward to experimenting with it when it’s made available. Meanwhile, here’s a short video that discusses Tailwind:

Categories
Google Photography

Snapseed on iOS

I don’t know if you use Snapseed or not but it’s become very much a part of my iPad/iPhone photography workflow.


I initially started using it because it has a Frames tool that lets me simply add a border to an image before uploading to Instagram/Facebook. But I’ve become increasingly addicted to a few of the other editing tools as well (tonal contrast, glamour glow, define (structure/sharpen), and faces. It also has a very nice healing brush as well as a dodge/burn tool that I use in monochromes.

This morning Google updated Snapseed to add a new Curves tool – which does what you think it should do – including allowing adjusting curves by red/green/blue channel. Very nice update/upgrade – this tool had become extremely useful for a mobile only workflow and it’s amazing that it’s all free from Google! If you haven’t played with it in a while, give it a try.

Categories
Google Web/Tech

Finding Me on Google+

Here’s my Google+ profile.

See also my initial thoughts about Google+.

Categories
Google Twitter Web/Tech

Thinking about Google+

A friend invited me about 10 days ago into Google+, Google’s new “social” service. As many others have commented, it’s very well done for a “field trial” as Google calls it. The UI is very nice – with a couple of exceptions like endless comment streams – and Google+’s handling of photographs is beautifully done. You can get to my Google+ posts by clicking on the G+ icon over at the top of the right sidebar on this page.

Of course, Google+ is still new – and it’s attractive partially just for that reason. It’s sort of like the new restaurant in town. Still, I’m finding that Facebook is getting less of my attention as a result of Google+. How about you?

As for Twitter, I typically keep Twitter running – as a separate app – off on the right side of my display and always in view along with my browser. It’s a parallel feed – and I appreciate it’s “information density” with short posts, no integration of comments, etc.

Facebook, on the other hand, I run in a separate browser tab – a tab that I have to decide to click and go to – just like my email (ugh!). In the “attention economy”, seems to me that’s important – at least in the desktop environment.

On a mobile device, it’s clearly a different story. Each app is “all consuming”! We’ll see how the Google+ iPhone app affects our mobile usage – once that app is released.

Going forward, it’ll be very interesting to see where Google+ goes. Might it replace my separate blog here? Or…?