Every major AI lab has just bought itself a consulting arm. Ross Katz explains why that is not a flex, it is an admission that the model alone does not create value.
You have been told to "do something with AI" by year end, and now every systems integrator in the market wants the work. How do you tell a partner doing real engineering from one reselling a model licence with a nice deck on top?
Ross Katz is principal and data science lead at CorrDyn, where he works daily with enterprise data teams trying to get real value from AI deployments. He reads these lab-consultancy tie-ups as a practitioner who actually does the integration work, not as someone selling the deal.
Ross walks through why OpenAI, Anthropic and the big four consultancies are suddenly partnering, and what each side actually gets from the arrangement. You will come away with a clear framework for the layers of work that sit between signing an AI deal and getting anything useful out of it, plus a simple filter for evaluating any implementation partner pitching you.
This episode covers the OpenAI and Anthropic consulting and private equity deals, the six layers between a model and real business value, and where money actually accrues in the AI stack. It is built for data and analytics leaders under pressure to show AI results, not for anyone looking for lab hype or a quick fix.
Key Takeaways
- The labs, consultancies and PE firms should not be natural partners, but each is trading something specific: reach, integration knowledge, or portfolio intelligence. Ross breaks down exactly what each side walks away with.
- Getting a model into production means working through six layers beyond the model itself, and the one nobody wants to talk about is the hardest.
- Anthropic's own numbers suggest six dollars of services spend for every dollar of software, which tells you where the real work (and the real value) is sitting right now.
- Ross gives you three questions to ask any AI consultant before you sign, built around exactly how they claim to deliver value.
Chapter Markers
00:00 AI labs buying into consultancies and PE
01:25 Why labs need consultancies at all
09:19 Strategic alliance or forced marriage
11:12 The six layers between deal and deployment
17:11 Where the AI money actually lands
20:13 Are consultancies funding their own disruption
22:48 The 1970s mainframe rollout parallel
26:43 Spotting real engineering vs a reseller
29:09 What We're Watching: AI labs and IPO pricing
31:56 Recap and takeaways
Useful Links & Resources
- Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/
- Ross Katz on X: https://x.com/brosskatz
- Previous episode referenced on Snowflake and Databricks context layers
- CorrDyn: corrdyn.com
Connect With the Show
- CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/
- Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/
- Ross Katz on X: https://x.com/brosskatz
Which layer is eating your AI budget right now: the data substrate, the governance, or the process and change nobody budgeted for? Tell us where your own AI rollout is stuck, and whether your consultant could actually answer Ross's three questions.
If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com.
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