Machine Learning Tools Ramping Up, So Why Are Companies Lagging On Adoption?

In our previous Talman Tidbit, we spoke about an avalanche of new foundational models for machine learning and how the cost of training these models is dropping at a rate of nearly 70% per year.

With these events in mind, surely you’d think more companies would jump on the bandwagon and implement machine learning models. Yet, that’s exactly where the picture gets more complicated.

For example, there has also been a significant development with hardware: NVIDIA has produced a new chip called H100 for things like OpenAI for large language model training. H100 is about 33 times faster than A100, which is NVIDIA’s GPU that everyone lived on up to this point. H100 may be a bit more expensive than A100, but at the same time, it’s nowhere near 33 times more expensive.

As a result, companies can build and test their models much faster, creating a situation where the tools seem to be accelerating.

At the same time, in my conversations with people building technologies inside various organizations, there is still a lot of inertia:

They will say, “I asked ChatGPT to write me some code and it did it really fast!”
To which I’ll say, “Well, have you looked at Copilot (Microsoft’s AI programming tool)?”

“No, we haven’t started on that.”

So, despite many machine learning models entering the marketplace and steadily dropping the cost of computing, several people are still on the learning curve.

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Lex Fridman, an AI researcher and one of my favorite interviewers, was interviewing Sam Altman, the CEO of OpenAI. Lex commented that while he spends a fair amount of time writing code, he’s had Copilot for a while. From his personal experience, Fridman described two feelings:

1) The sense of excitement that he could be so much more productive and it’s more fun to write code with the help of Copilot.

2) The sense of dread that, as time passes, what does Copilot need him for?

The other comment I found very interesting by Fridman was that it takes quite a while to get better at using Copilot, which Altman agreed with. 

So, on the one hand, you have a dynamic in which machine learning tools seem to be getting better, smarter and faster literally by the week.

On the other hand, people using the tools are either too conservative or have too much inertia to use them extensively.

I must admit that I haven’t heard many stories of organizations who report switching to a machine learning tool and, in turn, watching their company’s performance and productivity change dramatically.

That said, there’s no doubt that the thirst for developing new AI models will only grow from here, creating an opportunity worth seizing for companies to invest in hiring new people and accelerating their learning curve. Especially since their competitors may say all the right things about the new frontier of machine learning but, in reality, continue to drag their feet on evolving their business.

If the new developments of machine learning models are causing greater confusion than clarity, you’re not alone. Many companies find themselves at a crossroads of how and when to commit to attracting the very best technical talent in the field.

So take the easiest first step possible: Talk To Talman First. With our extensive experience over several decades in helping companies identify high-quality talent that can evolve as rapidly as they do, a conversation today can help you arrive at a greater peace of mind and, ultimately, if it makes sense for us to go forward together, arrive at the finest candidates to choose from.