Cooking up GTM

There are a handful of useful frameworks buried in the episode - deterministic-vs-AI decision logic, ROI-first thinking on model selection, and the metered-pricing shift - but roughly a third of the runtime is personal chit-chat, a live product demo of limited analytical depth, and a job advertisement. The insight-per-minute ratio is low.

“Can it be solved deterministically? And there's two ways that you can solve that. One of them is the tools that you're using...Maybe you can peel those out to then get AI to reason over their job title”

“try and find the best of both worlds and leverage AI in those use cases and then you should have quite a clear ROI on that”

Every core argument - AI costs were subsidized and are now rising, you need to measure ROI, use cheaper models where possible - is a recycled take circulating widely on LinkedIn and in B2B podcasts. The live avatar demo adds mild novelty but produces no original analysis.

“over the last six months, year, et cetera, when everyone's been getting very excited about AI, all of those costs have been subsidized by the model providers to try and get your attention and get you as a customer and lock you in”

“someone said going from AI pilled to AI poor, which is uh, very quickly, um, which is something I think a lot of go uh, to market teams are dealing with”

The hosts run a real go-to-market consultancy (CS2) with credible enterprise clients and genuine hands-on experience; they are practitioners, not pure thought leaders. However, there are no external guests, and the hosts themselves are mid-market consultants rather than operators who have scaled a function at a named company.

“our clients are companies like Superhuman, bloom energy scale, AI decagon, G2 salesloft, essentially a lot of the latest and greatest and best B2B tech companies out there”

“you'll be on the front lines working with them every single day, helping them deploy AI. Go to market ops, uh, CRM Infrastructure, analytics”

A few concrete data points appear - Anthropic Fable going metered around June 21-22, Clay's credit model, rough pricing figures from the HubSpot avatar - but the Uber budget story and the $80K API hack are both flagged as unverified, and most claims remain at the level of general observation without named studies or measured outcomes.

“you heard one of the big things people always talk about is that Uber blew through their AI budget already, like a month ago. For the whole year”

“I saw a post on LinkedIn, I don't even know if it's true. Someone's API key got hacked and then they got a bill for $80,000, something crazy like that. Apparently it's making them like an individual go bankrupt or something. Could be complete bullshit”

The conversation is friendly and natural but rarely probing; questions are open-ended softballs and the hosts almost never push each other to defend a claim or produce evidence. The HubSpot demo section substitutes novelty for craft, and the episode ends with an uninterrupted job advertisement.

“What do you think, Charlie? What's kind of like some things that you're seeing people kind of get tripped up with on, in terms of, um, kind of also, you know, managing their credit spend”

“Do we need to go much further? Any questions, Krissy?”