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Disruption

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So why not have all forms of computing?

Horace Dediu, on why each new computing interface tends to add to the others rather than replace them:

The touch UI did not immediately obviate the need for traditional personal computing interfaces. The smartphone brought computing to more people and more contexts but keyboard and pointer computing cannot be fully replaced by touch. We have a situation where there is co-existence between touch and non-touch computing. Indeed we also have sensor computing in the form of Apple Watch and AirPods. Perhaps there were will also be Apple spectacles to expand the compute real estate on the body.

Intent computing will probably reside primarily with our phones and wearables but Spatial Computing will strengthen their position alongside keyboard computing. Spatial was always a “high commitment” interface. To use it you strapped in, settled down and became acclimated. Then you became productive. A session was going to last at least 20 minutes, perhaps even a few hours.

Intent computing is a few seconds of use. Perhaps a few seconds strung out in multiple sessions but it was far more “glance” computing than “sit down” computing.

So why not have all forms of computing?

As the diagram on the coral of life shows, evolution results in a multitude of “form factors”. Some do become extinct (see the scroll wheel iPod or the stylus PDA.) But most survive and coexist.

I agree with Horace. Intent computing will build on top of the platforms we already have, not replace them. An AI pin isn’t going to displace the smartphone. Even when you can just ask your phone to do something, you’ll still pick it up to read, watch, and look things up.

asymco.com
Intent Computing vs. Spatial Computing

You don’t have to choose. In the grand debate on the future of computing, we’ve been led to believe that there was a choice coming, a new interface to replace the touch UI interface tha…

Without human direction, you have compute running in circles.

Satya Nadella, making the case that the model itself becomes a commodity — and that the value moves to the learning loop a company builds on top of it:

Every company is going to have to build what I think of as human capital and token capital. Human capital comprises the knowledge, judgment, relationships, ingenuity, and pattern recognition of its people, while token capital is the firm’s AI capability it builds and owns.

Importantly, human capital does not become less valuable as token capital grows. It only becomes more valuable! I believe human agency will be the driver of token capital growth. Humans will set ambitious goals, connect dots across domains, build relationships, and recognize patterns that matter most. Without human direction, you have compute running in circles.

This means the real opportunity is not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound. You can offload a task, or even a job, but you can never offload your learning. The future of the firm is the ability to compound that learning across people and AI.

This requires a new architectural approach where every business is able to build agentic systems that improve over time, while still retaining control over their IP. A company should be able to switch out a “generalist” model without losing the “company veteran” expertise built into their learning system. This is the key “test” of your control and sovereignty in the era ahead.

He’s right about the headline: without human direction, you’re leaving compute to wander. The creativity, the instinct, the judgment about what’s worth doing — call it taste — still has to come from people. No model supplies that for you.

But his bias shows in the vision he paints. Microsoft is vulnerable in exactly the future he describes, one where the model-makers absorb the very expertise he’s urging firms to protect. And the economics push them to do it: pulling that expertise into the model is the business those companies are in.

snscratchpad.com
A frontier without an ecosystem is not stable

I’ve been thinking a lot about the future of the firm in an AI-driven economy.

Measuring the wrong company

3 min read

Companies are taking a hard look at their AI spending and deciding the numbers don’t add up. Uber blew through its entire 2026 AI budget in four months — on a coding tool its engineers couldn’t stop using. Another company spent half a billion dollars before anyone thought to set a limit. Forrester now expects enterprises to postpone about a quarter of their planned AI investment into 2027 because the returns haven’t shown up.

I’ve heard this argument before. It’s the same one people made about the cloud in the early 2010s.

Back then the case against moving to AWS went like this: we already run our own data centers, we run them well, and we run them for less than Amazon would charge us. So why move? On the spreadsheet, the skeptics were often right. A company that had already sunk the capital into its racks and knew how to keep them humming could beat cloud pricing on raw unit cost for years.

They were answering the wrong question.

The cloud was never about running the same workloads for less money. It was about what you no longer had to think about. Moving to AWS turned infrastructure from a capital expense into an operating expense, from a thing you bought, racked, and depreciated into a thing you rented by the hour and stopped paying for the moment you stopped using it.

I lived this one. In my early days as CTO of Mailprotector, our real weakness wasn’t the software — it was everything underneath it: buying, racking, and babysitting the hardware our products ran on. Before AWS was anywhere close to ready to replace a data center, I wrote “AWS as a data center?” in a notebook and circled it. A year or two later we started migrating — and not to save money; the spreadsheet didn’t make that case yet. We did it to stop spending our attention on machines and put it where we could actually differentiate: the software. A couple of years after that, we turned the lights off on our last data center and never looked back. In hindsight it’s hard to separate that one decision from the company’s success — maybe even its survival.

Most companies never framed it that way. They measured the cloud against their own data centers, saw a higher unit cost, and stopped there — and because they already had data centers, the shift didn’t help them. It helped the company that didn’t exist yet. A startup in 2012 could spin up infrastructure that would have required millions in upfront capital a few years earlier, and pay for it out of revenue as it grew. Whole categories of companies got built that couldn’t have raised the money to build themselves the old way.

That generalizes well past the cloud. A general-purpose technology rarely just lowers the cost of what you already do; what it offers is a different cost structure, and different cost structures get used by different companies.

When an established company asks whether AI is worth what it’s spending, the buried question is whether AI makes the current operation cheaper. Often the honest answer is: not by enough to matter. Bolting a model onto a process that was designed around people rarely pays for itself. A lot of the spending getting scrutinized right now genuinely is waste. The scrutiny isn’t wrong.

But “our AI spending isn’t paying off” and “AI doesn’t pay off” are very different conclusions, and the distance between them is exactly where the data-center operators got caught. They weren’t wrong about the numbers. They were measuring the wrong company.

The company that mattered was being built on rented infrastructure, with a cost structure they could never reach by trimming their own. It’s being built again now, with AI in the foundation instead of bolted to the side. That’s the spend worth watching, and it isn’t yours.