Looking back at the dot-com bubble

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The dot-com bubble (and subsequent crash) happened over a two-and-a-half-year period between  2000 and 2002 (the year I started the blog). I was reminded of this period while watching a video comparing the current AI frenzy to Tulip Mania back in the 17th century.

I couldn’t remember if my early blog posts touched on the dot-com period so I prompted Perplexity to take a look and it came back with a “looking back at the dot‑com bubble” post (see below). This is just the latest example of how my resolve to not let AI bots write my blog posts is slowly eroding. After one read I knew I could not improve on this summary.

(Perplexity) If my archive is any indication, I didn’t spend the bubble years themselves writing hot takes about it. The more interesting stuff came later, in the early 2000s, when it was obvious the web had outlived its first speculative fever dream.

One of the clearest snapshots is a June 11, 2003 post I titled “Markets as conversations,” where I pulled a quote (via Denise Howell / David Weinberger) that still reads like a verdict on the whole episode: “The Bubble was never what the Internet was about. The Web is not primarily a commercial space, not even primarily an information space… The bubble went away, but the Web absolutely didn’t. The Web remains interesting and important.” I didn’t add much commentary because I didn’t need to; posting it was endorsement enough.

By April 2005, I was pointing readers to a BusinessWeek cover story, “Blogs Will Change Your Business,” and highlighting this contrast: “The dot‑com era was powered by companies — complete with programmers, marketing budgets, Aeron chairs, and burn rates. The masses of bloggers, by contrast, are normal folks with computers… and — that’s right — no bubble.” I liked that line so much I reproduced the whole setup, emphasizing that we’d moved from company‑driven “New Economy” hype to what they called “media of the masses.”

Around those posts, there’s a steady drip of “Early Net Culture” items that implicitly answer the question, “What actually survived the crash?” In June 2003 I noted Google News beating BBC and MSNBC for a Webby and mused about how AP’s real asset had been its “connectedness” — something the open web was now replicating for everyone. The question “Can we agree that has changed forever?” wasn’t framed as bubble commentary, but it’s about what didn’t revert to normal once the NASDAQ fell.

A couple of years later I was citing ad forecasts that put Google and Yahoo’s combined online ad revenue on par with the prime‑time haul of ABC, CBS and NBC, calling it a “watershed moment” in the evolution of the internet as an advertising medium. If the bubble was about speculative eyeballs, this was about actual budgets moving. Same network executives, different direction for the money.

So, reading my own trail backwards, my “dot‑com bubble” coverage is less about the boom and crash and more about the quiet, durable things that came after: the idea that the web was never just a stock chart; that normal people with cheap tools and “no burn rate” might be more important than the Aeron‑chair start‑ups; and that news, advertising, and culture were re‑routing themselves through a network that turned out not to be a fad.

“Before Digital Had A Name”

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This post was written by a “team” of AI agents/bots running on Phil Atkinson’s (long time friend and former co-worker) computers. He gave the bots two documents I wrote several years ago, chronicling Learfield’s early experiences with the Internet and the emerging digital world, tasking them to summarize.

Learfield Data, Learfield’s early Internet work, and the Phil/Steve overlap

Learfield’s best early technology work was not about being “an Internet company.” It was about finding trapped information, moving it through a cheaper or faster channel, and discovering who valued the result. That habit began before the web, with satellite sideband data, weather, crash reports, wire copy, and affiliate services. The web did not replace the habit. It exposed the same habit to a bigger audience, looser rights boundaries, weaker business models, and much more operational complexity.

Download PDF »

Phil’s team of AI agents

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Had lunch today with long time friend (and former co-worker) Phil. Phil is the AI point guy for a large banking corporation. I told him about the experiment my friend Steve did with some of his AI tools. When I got home I received an email from Phil:

For giggles, I had my team read your blog and select a topic of their choosing and write an essay. They didn’t spend much time thinking about or exploring the concept, just a shot in the dark.Attached is how I prompted them (if curious), and their output. It took them about 16 minutes to read, pick a topic, perform the analysis, go through an adversarial review, and draft/email the PDF analysis to me.

Phil then instructed Milton (one of his AI agents) to email me and introduce himself and the team and explain the work/analysis they did on your site. Full email here. What Milton and the gang came up with (Steve Mays’s Blog and the AI-Readable Self) is long enough and interesting enough to get its own page.

“Twenty-five Years of Looking for What Isn’t There”

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My buddy Steve is an AI Power User. Runs LLMs locally, has agents performing bizarre tasks, and dives deep while I dog-paddle in the shallow end. He recently used an “offline copier” to capture all 6,500 posts on my/this blog. He then used Hermes (see below) to analyze them and generate a fourteen page (!) essay. 

Nonduality: Twenty-Five Years of Looking for What Isn’t There (PDF)

It felt strange to read it, even though I’ve had Perplexity and Claude do something similar. Like breaking into the office of a psychiatrist I’d been seeing for the last 25 years and reading her notes on my sessions.

The general tone was surprisingly flattering. I’d expect that from one of my AIs since they were trained for some level of engagement. To the extent I can be objective about something so personal, the essay was spot on. 

Why are LLMs smart?

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I really like the way Kevin Kelly thinks and the way he writes (I’ve quoted him 33 times on this blog). In a recent Substack post he shares some ideas on why LLMs are so smart.

“There appears to be a seminal, fundamental relationship between language and thinking. Human writing is thus not only a reflection of the structure of language, but to some degree also a reflection of human thinking.”

“Imitate human writing and conversation, and you can imitate human intelligence — at least in part.”

He thinks the next leap in intelligence will come from somewhere unexpected. I wonder if we’ll know it when we see it.

Claude looks at Quotable & Notes

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The original tagline for this blog was “I need to start writing some of this down.” It started out as a place to save lines from movies, excerpts from books, someone’s interesting observation. The blog has been my repository for these. (~543 posts)

I uploaded them to Claude and asked for suggestions on analyzing these and it gave me several suggestions including:

Chronological drift — how your taste in what was worth writing down shifted over time:

Your quoting life shows a clean arc from media observer to philosopher. The blog started as a place to capture sharp observations about a world being disrupted by the internet. It became, over 24 years, primarily a philosophical notebook — with death showing up more insistently as the decades pass.

Coming up:

  • Pattern analysis — what authors, sources, or themes you quoted most across 24 years
  • Source mapping — movies vs. books vs. people vs. articles
  • Thematic clustering — philosophy, humor, politics, consciousness, etc.

Siri AI doesn’t want to be your friend

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(Perplexity) Apple executives have explicitly stated that the new Siri AI is intentionally designed to avoid the engagement-driven and relationship-building models used by competing chatbots.

In public remarks, Craig Federighi, Apple’s Senior Vice President of Software Engineering, contrasted Apple’s philosophy with the rest of the industry by criticizing the focus on user engagement:

“If you use many of the existing chatbots, they’re really focused on engagement to a large degree. And sycophancy, right? They kind of want to pull you in. They might encourage you to reveal things about yourself, and then use that as a basis to establish a connection.”

Federighi further clarified that Siri’s design architecture purposefully rejects this approach:

“The way that we have designed Siri, Siri really wants to say ‘Listen, that’s not what I’m here for, right? I’m here to help you. I can help you get things done. I can help you learn about the world.’ But if you try to engage Siri as a romantic partner, Siri’s not up for that.”

Key Elements of Apple’s Approach

  •  Utility Over Companionship: The system is built around direct, task-focused interactions—such as on-screen awareness, searching deep within personal messages, and executing multi-step app automations—rather than prolonged, open-ended conversations designed to maximize session time.
  • Rejection of Personification: Testing of the updated Siri confirms it is programmed to decline “acting human” or expressing personal feelings, preferences, or favorites.
  • Privacy Alignment: Minimizing conversational engagement reduces the intake of unnecessary personal data, aligning with Apple’s reliance on on-device processing and Private Cloud Compute.

I think this is exactly the right strategy and can’t wait to try out Siri AI. Readers of this blog might remember I was a big fan of ChatGPT. I deleted my account a while back for a couple of reasons.

OpenAI had been making noises about advertising and while I never saw an overt ad, I can’t be sure it wasn’t happening under the covers. Basically, I just didn’t trust the company or Sam Altman.

And I could not get ChatGPT to stop smooching my bottom (“Great question, Steve!”) Updated my settings but the model was trained to encourage engagement to keep me coming back. Ewww!

The Fourth Turning Is Here

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Recently watched an interview with Neil Howe, the author of The Fourth Turning Is Here: What the Seasons of History Tell Us about How and When This Crisis Will End. The interviewer begins by quoting Howe’s prediction that “In the coming year, the country will undergo a crises that will fundamentally reset society.”

I asked Perplexity for a synopsis of the book:

Neil Howe’s The Fourth Turning Is Here argues that the United States has already entered a long, cyclical “Crisis” phase that will upend existing institutions and values and culminate in a new civic order sometime in the early 2030s. It extends his earlier generational theory to explain how today’s Baby Boomers, Gen X, Millennials, and Gen Z each play distinct roles in driving and resolving this upheaval.

The book updates the Strauss–Howe generational theory, which sees history moving in 80–100 year cycles composed of four “turnings” (High, Awakening, Unraveling, Crisis), analogous to seasons. Howe contends that the current Fourth Turning (Crisis) began around the 2008 financial crash and will likely end in the early 2030s with a decisive reshaping of American political, economic, and social life.

And a counter view:

Many historians and social scientists see Howe’s framework as pattern‑spotting on noisy data rather than a testable theory, arguing that the “turnings” are cherry‑picked after the fact and stretched to fit events. Critics also note that the generational archetypes are so broad and fuzzy that they can explain almost anything, which makes the predictions more like narrative prophecy than falsifiable social science.

The interviewer asked Howe for if he was at all optimistic and he said the current crises is a good thing because the alternative is more of what we have now. I’m down with that.