The New Problems AI Is Creating (And How People Are Solving Them)

Last year at this time, AI was a very different place.

ChatGPT-5 had just launched to not much acclaim at all, people were really upset that GPT-4.0 was being deprecated, and there was so much growing conversation and consternation, frankly, about the potential of an AI bubble.

And on top of all that, there were some companies out there that were still trying to convince themselves that AI was overhyped and just not going to be a thing.

Now a year on from that, the conversation is very different.

Not only have the models advanced, not only have the use cases shifted to the agentic, living up to the promise that's been lurking for years, but the businesses that are harnessing AI have gotten so much more sophisticated in the questions they're asking.

In fact, over the last year, we've gone from, in many cases, not even asking the right questions to actively solving the new problems that emerge for new work patterns that come alongside specifically agentic AI.

Easy production causing an AI slop problem? Institute a new AI writing policy.

Over usage of top models costing too much? Come up with new ways to allocate tokens and intelligence to different parts of the organization.

Today we're gonna dig into not only the current challenges of AI, but how companies are actually solving them.

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