As per a post on LinkedIn, I am NOT against real AI. I AM against the hype, false claims, and fake tech today’s enterprise vendors are trying to pass off as real AI!

You may recall, that like Jon W. Hansen and Pierre Mitchell, I was an early fan of AI, and what it could do for enterprise tech. As a PhD in Computer Science with a degree in applied math and specialties in multidimensional data structures and computational geometry (MSc and PhD) [think big data before that was a thing], analytics, optimization, and “classic” AI, as computing power advanced, and data stores exploded, I saw the real potential for next generation tech.

I did a very deep dive in a 22 part series on what should soon have been possible in our ProcureTech space on Spend Matters in ’18/’19, that started before the first LLM was released (despite the X-Files Warning). The research and implementation paths we were on was good, and the potential was great. It just required a lot of blood, sweat, elbow grease, and patience.

But then some very charming tech bros claimed that this new LLM tech was emergent and magical and would do everything and replace all of the old and busted (which was really tried and true) tech (that actually worked), some super deep pockets were blinded by the hype, we abandoned the path of progress (and sanity), and the rest, as they say, is history, which, sadly, is still ongoing (while tech failure rates have reached all time highs).

Until the space is ready to admit that

• Gen-AI/LLMs are not the be-all and end-all, and, in fact, have very limited reliable uses (especially in automation/agentic tech) [namely only tasks that can be reduced to semantic processing and large corpus search & summarization]
• real progress still requires real blood, sweat, elbow grease, and tears
• you can’t replace people as this tech is NOT intelligent (although you can make them 10x productive if you start focusing on Augmented Intelligence)

and abandon its zealotous devotion to Gen-AI as the divine tech (which would bankrupt some tech bros and investors, which is why they are now doubling down on the marketing hype at the point where the hype cycle would usually burst), we’re not going to make progress.

As Pierre has pointed out, Gen-AI is useful as a piece of the puzzle when it is properly combined with other, traditional, reliable, AI tech, so long the foundation of the AI tech is built on a deterministic engine and only incorporates probabilistic models with known confidence and guardrails. (Remember that unless the use case boils down to semantic processing and large document corpus search and summarization, Gen-AI is NOT the right tech.)

When the day comes that we abandon the madness, I’ll be happy to jump back on the souped-up classic AI hype train because, with the exponential increases in computing power and data over the past two-and-half decades, we could finally build amazing tech. We just need to remember that the best AI tech has never been generic, it has always been purpose-built to a specific task and if we want to automate processes, we will have to orchestrate multiple point-based process-centric agents, which may or may not use AI, to accomplish that.

But until then, we need to keep railing against the hype and the fake tech.