AI is going to break a lot of companies

Some years ago (never mind exactly) a mentor told me that the difference between a scientist and a businessman was that the scientist wanted to do a thousand things once and a businessman wanted to do one thing a thousand times. That logic still applies today, but AI may present a problem.

Before I talk about AI, let's think about businesses.

Most businesses do like to perform a single thing (such as selling a hamburger) many times. That's a bit of an exaggeration, as hamburger shops like to sell hamburgers with cheese (or without), with fries (or with onion rings), with soda, and sometimes chicken sandwiches instead of hamburgers. But I think you get the basic idea: businesses like to sell a limited set of products or services in a limited set of configurations and make a profit on each transaction.

That idea extends beyond hamburger shops. Auto dealers sell cars, publishers sell books or newspapers, movie studios sell movies, ... the list goes on.

All of these businesses like consistency and stability. While the news changes from day to day and movies have different plots and special effects, to basic idea of selling hamburgers remains the same from day to day, and the basic idea of selling movie tickets remains the same.

Technology has always been a motivator of change. The IBM PC changed the use of computers in businesses. Prior to the IBM PC, most businesses used computers either as large centralized processors of mostly accounting data, with a few exception cases for dedicated word processors or experiments with a TRS-80 or Apple II computer. After the IBM PC, desktop computers were adopted throughout the business world.

But the rate of technological was manageable. IBM (and later Microsoft) limited the changes to hardware and software, and kept old things working with new things. When IBM introduced the PC AT, it required a new version of PC DOS yet it ran almost all programs from the original IBM PC. New versions of Microsoft Windows run almost all programs from the previous versions. The first versions of Windows ran 16-bit DOS programs, too.

Occasionally, a company introduced a product or service that was radically new. Apple brought out the Macintosh computer (or the LISA, an earlier product with similar features) that did not run software from the Apple II. Microsoft released Windows NT which had a completely different architecture and notably did not run many older programs.

These major shifts occurred infrequently, and we always had the opportunity to stay with the older systems for some time. That transition period allowed for gradual upgrades to systems. Most importantly, it allowed businesses to plan upgrades and to operate in a period of known technology. That period provided stability.

Businesses like stability because they can predict the short-term future, and make plans for investments, expansions, new products, and advertising. They can also adjust their supply chains and automate internal processes.

So how does AI affect stability of technology?

In short, AI causes changes in almost every aspect of business, and AI itself is changing rapidly.

When PCs became available, they first replaced typewriters and dedicated word processing systems. The spreadsheet made it easy to analyze data. Later uses included databases and e-mail. Their uses were limited and specific. Their adoption was measured and gradual.

AI, in contrast, can be used in almost any part of the business, from drafting e-mails to analyzing current business conditions to suggesting changes for supply chain management. That means that many areas of the company can change to use AI. That doesn't provide a measured and gradual change.

AI is inconsistent. A request today gets a certain response, a request tomorrow gets a different response (sometimes slightly different, sometimes significantly different). That doesn't provide stability.

The AI engines change frequently. The original IBM PC was first delivered in 1982. The PC XT (almost identical except it included a hard disk) in 1982. The PC AT (with the 80286 processor and a larger hard disk) in 1984. Compaq delivered the 80386-based Desktop Pro in 1987. All of those did basically the same thing.

Today the AI providers (OpenIA, Microsoft, Google, xAI) update their offerings frequently. Those updates are not trivial, and the changes are significant. In some ways, it is like the PC market before IBM announced its PC, with multiple vendors and multiple standards and limited compatibility and consistency.

The frequent changes to AI engines is at odds with the desire of companies for a stable, consistent set of technology on which they can run their business. I don't see it slowing, which means that companies will have to deal with changes (and problems) in their AI purchases. (I also don't see a company that can dominate the market, as IBM dominated the PC market in 1981.)

The changes caused by AI and the ongoing changes in AI products will strain businesses. They will have to adapt to an environment of constant change.

Most won't like it.

Some won't survive it.

The end of the PC empire

Micron Technology, a large manufacturer of memory DIMMs for PCs, recently announced that it was exiting that business and is redirecting its efforts to memory components for AI server farms.

I think the impact of this announcement is not fully understood.

This change by Micron Technology indicates a larger shift in the industry: away from PCs and towards AI. Away from consumer PCs (desktops and laptops), and also office PCs. The PC, the king of the tech world for decades, has lost its crown.

The IBM PC, announced in 1981, legitimized the then-sputtering tech market for PCs. Before the IBM PC (and for some time after its introduction), PC makers such as Apple, Commodore, and Radio Shack all had to cobble their products together from components available from other systems. Instead of designing the display, the disk, the memory, etc., manufacturers had to survey the market for available components and then design a system with those components. Even the original IBM PC used a keyboard from IBM's System/23 desktop computer system.

But PCs were popular, and manufacturers couldn't ignore the market. They started designing components for PCs. When Microsoft introduced Windows and set hardware standards, the transition was complete: The PC was the center of attention. Standards were set (and followed). Supply chains were built to provide components that met those standards, with robust delivery schedules. One could easily buy components and build PCs.

Some thirty years later, component manufacturers are now looking at the market for AI servers, and they cannot ignore it. Which means that they will pay less attention to the PC market -- or ignore it completely, like Micron is doing.

(I'm rather skeptical of the AI boom, and doubtful that it is sustainable, but that is another question. Micron is placing its bets. I'm assuming that other companies will follow.)

What does this change mean for the PC market? At a minimum, manufacturers of PCs will find it harder to obtain components. Some components will become more expensive. Others will become impossible to find. PC manufacturers may have to submit custom orders for components, or find other sources. It is easy to predict that the price of PCs will rise.

But we may also see fewer PC models, and longer times between announcements of new models. We may see "limited run" announcements in which a new model is available for a limited period of time, or a single production run.

Apple will be somewhat immune to this effect, as they design most components for their PCs. Sourcing components (that is, getting someone else to build Apple-designed components in large quantities) should be possible because Apple's scale is such that one does not ignore it.

But other manufacturers (Dell, LG, HP, etc.) who have relied on the PC supply chain may find their business at risk.

For consumers, I think we will see fewer offerings: Fewer PC models, and fewer configuration options. (Which perhaps may not be such a bad thing. I am often overwhelmed by the number of possibilities when I look for a new PC.)

The missing conversation about AI

For Artificial Intelligence (AI), -- or at least the latest fad that we call "AI" -- I've seen lots of announcements, lots of articles, lots of discussions, and lots of advertisements. All of them -- and I do mean "all" -- fall into the category of "hype". I have yet to see a serious discussion or article on AI.

Here's why:

In business -- and in almost every organization -- there are four dimensions for serious discussions. Those dimensions are: money, time, risk, and politics. (Politics internal to the organization, or possible with external suppliers or customers; not the national-level politics.)

Businesses don't care if an application is written in Java or C# or Rust. They *do* care that the application is delivered on time, that the development cost was reasonably close to the estimated cost, and that the application runs as expected with no ill effects. Conversations about C++ and Rust are not about the languages but about the risks of applications written in those languages. Converting from C++ to Rust is about the cost of conversion, the time it takes, opportunities lost during the conversion, and reduction of risk due to memory leaks, invalid access, and other exploits. The serious discussion ignores the issues of syntax and IDE support (unless one can tie them to money, time, or risk).

With AI, I have not seen a serious discussion about money, for either the cost to implement AI or the reduction in expenditures, other than speculation. I have not seen anyone list the time it took to implement AI with any degree of success. I have yet to see any articles or discussions about the risks of AI and how AI can provide incorrect information that seems, at first glance, quite reasonable.

These are the conversations about AI that we need to have. Without them, AI is merely a shiny new thing that has no clearly understood benefits and no place in our strategies or tactics. Without them, we do not understand the true costs to implement AI and how to decide when and where to implement it. Without them, we do not understand the risks and how to mitigate them.

The first rule of investment is: If you don't understand an investment instrument, then don't invest in it.

The first rule of business management is: If you don't understand a technology (how it can help you, what it costs, and its risks), then don't implement it. (Other than small, controlled research projects to learn about it.)

It seems to me that we don't understand AI, at least not well enough to use it for serious tasks.