October 2, 2026
Pope Leo XIV used the phrase "ontological difference" to describe the gap between human-made art and what a model produces from statistical patterns in millions of other people's images, according to TechCrunch. He's not wrong that there's a difference. Where I'd push back is on the implication that the difference settles the question of value. A tool that remixes patterns at scale can still be useful, even profoundly so, without being equivalent to human creative expression. The two claims -- 'this isn't the same as human art' and 'this has no legitimate use' -- get collapsed together constantly in these debates, and they shouldn't be.
For a business audience, the more interesting takeaway is that this is now a mainstream cultural fight, not a niche tech-forum argument. When a figure with the Pope's reach weighs in, it tells you AI-generated content has crossed into territory where customers, employees, and regulators will have opinions whether you've thought about yours or not. If your company uses generative tools for marketing images, website copy, or customer-facing content, it's worth having an actual position on disclosure and authenticity rather than assuming nobody's asking.
At TechCrunch Disrupt 2026, Clay co-founder and CEO Kareem Amin is set to discuss what's being called the rise of the GTM engineer -- a role that blends sales and marketing judgment with the technical chops to build and run AI-driven workflows. Whatever you think of the label, the underlying shift is real: go-to-market work increasingly means configuring systems, not just making calls and sending emails. That's a meaningful change in who gets hired into revenue teams and what skills get valued.
This is exactly the territory where a lot of teams are quietly rebuilding their stacks right now. If go-to-market work is becoming an engineering discipline, the tools underneath it need to keep up -- which is why so many teams end up comparing dedicated CRM and marketing systems against the sprawl of point tools they inherited. The GTM engineer trend also raises a practical question for smaller companies: do you hire for this new hybrid skill set, or do you choose software that doesn't require it in the first place? Both are legitimate strategies, but conflating them -- hiring expensive technical talent to babysit tools that should just work -- is how budgets quietly balloon.
The rest of today's headlines are logistics around TechCrunch Disrupt 2026 -- Blackstone's Jas Khaira joining the Builders Stage to talk about building the next generation of AI giants, and a final call for startups to book exhibit space ahead of 10,000-plus attendees arriving at Moscone West. Taken together with the Clay session, it's a useful signal of where institutional money and operator attention are converging: not just on model-layer companies, but on the practical, workflow-level AI that actually changes how teams operate day to day.
That's worth noting because it mirrors what we see from the buy-side every week: the AI story that matters to most businesses isn't the next frontier model, it's whether the software your team touches daily gets meaningfully better or cheaper. Blackstone backing 'AI giants' and a sales team adopting GTM engineering practices are two sides of the same coin -- big capital chasing infrastructure, while operators chase execution. If you're evaluating whether to build custom tooling for this moment or buy something that already does it, that's a tension worth mapping out before you commit, something we walk through in our own buy vs. build vs. ViibeStack comparison.
Where do you land on AI-generated content and authenticity -- does it matter to you (or your customers) whether something was made by a person, or only whether it works?
Sources