Ideas and opinions that emerge from our team as we tackle transformational business problems with AI.
Some seemingly underwhelming benchmarks of OpenAI’s ChatGPT 4.5 sent the blogosphere into a tizzy recently. It looks like the model hallucinates a lot (albeit a lot less than previous versions). Hallucinations are a real concern for businesses trying to squeeze value out of AI. Some even claim that AI isn’t ready for the enterprise because of its unreliability. While legitimate, these concerns tend to get overblown to the point of obscuring the practical reality.
This is part two of a two-part series. The first article was about how, like it or not, AI is taking over the boring, repetitive parts of our jobs and how that fact is reshaping the very paradigm of work. This article is focused on what you can do to thrive during this shift.
Everybody is talking about agents. Here’s what you need to know to have a reasonable handle on what this technology really is and what it means for the present and future of business.
It's time to discuss something uncomfortable that is rightfully causing a lot of anxiety: job displacement. I’m a pragmatic optimist when it comes to AI. I’m excited about the opportunities, wary of the hype, and clear-eyed (I think) about some of the downsides. But one thing is sure: AI is coming for all our dumb jobs. And that may not be a bad thing.
Most of the utility from today’s generative AI-powered tools comes from a software architecture called RAG that few non-technical folks really understand. This is an explainer, broken into five tidy sections.
The endless hype around AI has some companies thinking that they need to start working on their AI moonshot. We believe the opposite path will lead to true transformational value.
Two things are true simultaneously: AI is both massively overhyped and a transformative technology. This creates a dilemma for business leaders: whether to engage with AI now or wait until the technology matures.
AGI is probably the most talked-about, least-understood topic in AI. The TL;DR here is that I think it’s bullshit and a huge waste of collective time. There is however, another topic we should be discussing.
The failure of many AI initiatives has people wondering if a return on investment is even possible. We know it is, but only if you approach and develop your projects with clarity and thoughtfulness.
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