The whiplash-inducing pace of change

The whiplash-inducing pace of change

This week it would have been easy to write my ForwardThinking commentary about DeepSeek, the new model from China that had everyone buzzing about its impact and speculating what it means. Find your favorite writer or thought leader and there was surely an opinion. But what struck me about the DeepSeek news wasn’t just the weightiness of it, although it clearly was a bombshell, it was its part in the breakneck news cycle we are experiencing.

Consider that in the last week we’ve heard about OpenAI releasing a beta of Operator, a new AI agent that reportedly can search the web and undertake tasks on your behalf. Then came Stargate, a project to build massive data centers for OpenAI with an astonishing $500 billion potential price tag, a number that’s so big, it’s hard to wrap our heads around. Then DeepSeek came along to blow our minds yet again. 

DeepSeek in particular raised a plethora of questions about everything we’ve been told about generative AI. If this model, which seems quite capable, can be produced for millions of dollars, why do U.S. tech companies require hundreds of billions to build out their data centers in the chase for ever more compute power and presumably increasingly sophisticated and capable models? What does it mean for the cost of AI in the future, and are we witnessing the beginning of the commoditization of generative AI?

That’s just one week of news folks. It’s not just you. It is hard to process what it all means. I do this for a living, and it’s hard for me. I’m sure it’s even more challenging for anyone trying to set tech policy inside a large company with what is essentially a moving target. 

It feels like we’re constantly falling behind, and we need to keep accelerating to keep up. Yet we know that large organizations don’t tend to move fast, and in spite of the high-speed pace of the announcements coming from vendors, study after study is showing that very few companies are actually implementing successful generative AI projects at scale. In fact, most are still very much in the experimenting phase.

A recent report from Deloitte, The State of Generative AI in the Enterprise concluded that it’s going to take time to scale this technology and executives need to be patient. “The GenAI journey is long, and C-suite leaders need to be realistic about time horizons for project success and organizational transformation,” the report found.

It’s easy to feel like you’re lagging when one big news event after another comes washing over us like so many waves upon the shore, but the fact is we are very early with this technology. The vendors may be pushing the velocity, but enterprises are going to do what enterprises do – move slowly and deliberately and figure out what works and what doesn’t.

So slow down and take a deep breath because we’re just getting started, and we are going to have to work hard to separate the hype from the reality.

- Ron

Photo by Mathew Schwartz on Unsplash