Are Frontier Models Becoming A Commodity?

We need to treat Enterprise AI as a "normal technology" that doesn't pay for itself just because it's cool. And that means problem solving, not experimenting.

Today I posted a podcast on a controversial topic: Is AI Becoming A Commodity? Or Is it Just A Normal Enterprise Technology like other?

Here’s the basic idea. Now that we have Frontier models from OpenAI, Anthropic, Google, and Microsoft (MAI), and we also have free open source (GLM, Deepseek, Kimi, Mistral, IBM’s Granite…) as well, can we stop “buying AI” simply because it’s cool and start looking at this as just one more amazing tool for building solutions?

I think we’re at that point.

We are just finishing a report (Enterprise AI Playbook) and our research (200+ companies) found that only about 4% are building real enterprise apps so far and most are primarily assuming that individuals will figure out what to do with it.

In other words, companies are buying AI as an employee “benefit,” in a sense, and hoping and assuming that good things will happen. And as I’ve seen with our own use of Galileo (which uses Claude but works with most models), if you don’t focus on a specific domain and problem area, it’s easy to waste a lot of time playing with these tools.

Just look at how economists are losing their inflated expectations!

What Normally Happens With Enterprise Tech

Now in a “normal technology” purchase this would never happen. Someone finds a tool, builds a business case, works with IT to build security and data support, and then buys the system with a clear goal and ROI in mind. This does happen when companies buy Paradox or Eightfold or Radancy or Sana, which are AI applications, not AI platforms, but it doesn’t necessarily happen when you just buy Claude and turn people loose to play around.

And along the lines of “normal….” Even if you are amazed at Gen AI’s ability to write code, create images, analyze spreadsheets, or answer a question - that innate “fun and interesting” ability does not always produce business value when you’re paying a high cost for consumption.

I don’t want to rain on any parades, but most of our “playing around” with AI has been subsidized by $1.5 Trillion of forward-looking investors. These pragmatic industrialists bought the engineers, data centers, NVIDIA processors, and power plants we use. And soon enough they won’t let their companies give the AI away any more, so more and more of our “playing” and “experimenting” will cost money.

Last week the WSJ published two articles on AI “price wars,” which kind of make me smile. Not only are the Frontier vendors worried about competing with each other, they’re now dropping prices to compete with each other. Isn’t this what happens in commodity markets? Where switching costs are low?

Welcome to a “normal technology market,” where the price and cost is commensurate with the value and problems it solves.

(By the way, even the velocity of “model improvement” is slowing, as this capability evolution chart shows.”

This slowdown is actually good, because we now see companies taking the time to focus on problem solving, not just “buying tech and hoping the fairy dust creates value.” In other words, in the corporate space, we all have to dig in and really focus on solutions, not “implementing AI.”

How Does This Shift Play Out?

It’s pretty clear from our HR 2030 model, which is a reference blueprint for dozens of high-value AI solutions in human resources, that most of the high-ROI use cases require more strategic investment than we thought.

If you want to transform and speed hiring, for example, there are a series of agents and superagents you can buy - but it will require partnering with IT and re-designing how your talent acquisition works. (Hot vendors here include Paradox, Maki, Radancy, Smartrecruiters, and others.) And you’ll change a lot of roles in talent acquisition.

If you want to transform your employee service centers, you can build solutions on MS Copilot, Workday Sana Core, ServiceNow, or smaller vendors like Leena.ai and others. But again this is a “project” that requires policy consolidation, governance, data management, and cross-functional teamwork. And you’ll end up reorganizing L&D.

If you want to build a high performance onboarding program, as both Rolls Royce and Lockheed Martin are doing, you have to build consensus on program elements, develop a lot of global and role specific use cases, and build a governance model to bring tactical and strategic content together in a way it can stay up to date. Again it’s a very powerful use case, but the LLM itself is only a tiny fraction of the solution.

And on and on.

We have identified about 130 Agents in our HR 2030 blueprint, and some you can buy and others you can build. So if you want to make AI pay off, you’ll be spending time prioritizing where to start, working with IT, and preparing your team for new roles, skills, and interesting workflows.

Enterprise AI Is A Reengineering Process, not “Magic” from an LLM

In other words, just because Claude Code can whip out artifacts and HTML websites or code snippets in second, that doesn’t mean it’s magic, or superintelligent, or worth millions of dollars. It depends on the problem you’re trying to solve and how well you design the end-to-end solution.

Guess what, this sounds more and more like “traditional technology projects,” doesn’t it.

We’re huge fans of AI in our company - we now have Galileo modeling entire companies and producing new models for reorganization, pay structure, and massive amounts of skills, pay, and organizational analytics that used to take months from a consulting firm.

But all that “problem solving” took us almost four years of work to build. The system didn’t magically learn how to do all this without our painstaking effort to train it, add lots of workflows, and leverage the new features of the LLM.

That’s what you, as an HR or IT person will be doing in the coming years. Finding high value problems and “applying AI” to build, buy, or customize these solutions. The “magic” inside the LLM is becoming less and less interesting and important by the minute.

All this is good, because the $1.5 Trillion invested is going to want a return! So we, as buyers and implementers, have to stop futzing around. Join us on this journey by signing up for Galileo, take our HR 2030 training, or get certified in our new Global HR Excellence Certification.

Here’s the podcast if you want to hear more.