The obvious problem with naming your best model Opus is that you eventually have to build a better one.

A magnum opus is supposed to be the work someone is remembered for. Anthropic’s original Claude hierarchy—Haiku, Sonnet, Opus—was memorable and reasonably intuitive. A haiku is short, a sonnet is more substantial, and an opus is a major work (in terms of impact, but that’s often correlated with length). The metaphor was not mathematically precise, but it did not need to be. Many could see the three names and understand the intended order.

It was also versioned. Claude 3 Opus could become Claude 3.5 Opus, then Claude 4 Opus, without disturbing the hierarchy. The model family and the generation were separate concepts.

But Opus left no obvious room above it. Anthropic eventually introduced Mythos as a new class above Opus, with Fable as a safeguarded model built from the same underlying system. The etymology is defensible: fable and mythos both refer to stories, and Anthropic explains the relationship between them. As a product hierarchy, though, Haiku → Sonnet → Opus → Fable is no longer something a person can infer. The metaphor has moved from the length of the writing, to the importance of the work, to the nature of the story itself.

Anthropic outgrew its own naming scheme.

Good software quietly disappears: it stops feeling like a tool and starts feeling like an extension of how you think. Most software never gets there in large part because it wasn’t built for you specifically. It was built for a market segment. But that rigid constraint is starting to dissolve, and the downstream effects are going to completely rewrite how we think about product defensibility.

I recently finished listening to the Acquired podcast. Not just the latest episode, but all of them. When I started, I had decided to listen in reverse chronological order, winding my way back through the last 10+ years of episodes.

Walking backward through tech history meant listening to predictions from progressively further back with the absolute benefit of hindsight.

The show’s 2016 year-in-review and 2017 predictions episodes are particularly fascinating time capsules. Looking back now, a decade later, the tech worldview of 2016 wasn’t naive. Many of today’s major themes were already visible. Where the industry stumbled wasn’t on the what, but on the when, the how, and the sequencing.

Here is how some of the predictions, frameworks, and narratives of that era have aged.

You can be deeply familiar with AI and still be skeptical of “agentic AI.” I was.

For a while, I dismissed most “agentic” startups as VC-funded cron jobs. An LLM integration wrapped in a thin scheduler, rebranded as autonomy.

But there’s something real underneath the hype, and understanding it requires zooming out. LLMs haven’t evolved in one continuous line. They’ve moved in distinct acts, each driven by hitting a ceiling and finding a way around it.

We’re now entering their fifth act. The first four largely occurred in parallel, so forgive the linear framing that follows.

Dario Amodei, Anthropic’s CEO, recently suggested that their Claude AI models might be conscious.

This may seem jarring: large language models generate text by predicting the next likely token in a sequence, a process that appears purely statistical and mechanical.

But both the claim and the dismissal miss the deeper question.

The End of the Crawlable Web : For most of the modern internet, there was an implicit contract between websites and search engines. Websites made their content accessible. Search engines indexed it. And in return, search engines sent users back to the original site.

It wasn’t always perfect — news aggregators and social media links were persistent points of contention — but the incentives aligned well enough that the web became broadly searchable. You could discover obscure blogs, old research papers, forum discussions from fifteen years ago, or technical documentation buried deep in a site.

As software development shifts from requiring specialized skills—built on multiple layers of technical understanding—to describing intent in plain English (or your language of choice), the act of producing software appears to become accessible to a much wider audience. The people best positioned to excel may not be well versed in software at all, but rather those who are good at expressing ideas clearly, thinking iteratively, and breaking problems down.

At first glance, that sounds like perhaps the most egalitarian shift our industry has ever seen.

But there’s a quieter change brewing while we’ve been distracted by the expanding capabilities of AI tools. As AI reshapes how software is written, it reintroduces something we spent decades deliberately removing: cost.

And once cost becomes the bottleneck, software stops being democratic very quickly.

If you paint a picture of Sonic the Hedgehog in your living room, you are technically creating an unauthorized derivative work—but in practice, no one cares. Private, noncommercial creation has always lived in a space where copyright law exists on paper but is rarely enforced.

Gift it to a friend? Still functionally tolerated—a technical act of distribution that copyright law mostly ignores at human scale. Take a photo and post it on Instagram? Now you’ve crossed into public distribution of a derivative work without permission. Under the letter of the law, that’s infringement, although a fair-use defense might apply and Sega almost certainly won’t care. It’s fan engagement, free marketing, and good PR.

Sell that painting, though, and the tolerance disappears. You’re no longer a fan, you’re a competitor.