Equity

Inside Ode with Anthropic, the private equity-backed startup betting AI services are the future of enterprise

Mittwoch, 15. Juli 2026 · 31 min

MP3

Shownotes

Can a handful of engineers really do the work of an army of consultants? That’s the bet behind Ode with Anthropic — the joint venture dedicated to embedding forward-deployed engineers in enterprise firms, backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs and others.

On this episode of TechCrunch's Equity (techcrunch.com/podcasts/equity) podcast, Rebecca Bellan sits down with Ode’s leaders Chris Taylor (linkedin.com/in/taylorcd) and Eddie Siegel, (linkedin.com/in/eddiesiegel) who founded Fractional AI, the applied AI services startup that Ode acquired earlier this year (blackstone.com/news/press/the-ai-native-enterp…) to serve as the new venture’s core. The three discuss why so many enterprise AI pilots never make it to production and why they think AI-native services are about to become one of the biggest categories in tech.

Listen to the full episode to hear more about:

• The case for AI-native services, and why Ode believes small teams of applied AI engineers can unlock hundreds of millions of dollars in business value.

• Why the company is hiring experienced generalists and former founders instead of AI researchers, and how it's turning them into elite FDEs.

• Whether an AI services company can scale like software, yet maintain a boutique feel, and why Taylor believes Ode could one day become a trillion-dollar business.

Subscribe to Equity on YouTube (youtube.com/@TechCrunch), Apple Podcasts (itunes.apple.com/us/podcast/id1215439780), Overcast (overcast.fm/itunes1215439780/equity), Spotify (open.spotify.com/show/5IEYLip3eDppcOmy5DmphC?s…) and all the casts. You also can follow Equity on X (twitter.com/EquityPod) and Threads (threads.net/@equitypod), at @EquityPod.

Chapters:

Intro

Fractional AI becomes "Ode with Anthropic"

Why non-AI companies are the real AI winners

Working with Blackstone, Anthropic, and beyond

Inside a real project: fixing LogicGate's bottleneck

How long does it take from hypothesis to production?

Measuring ROI: revenue, efficiency, and evals

Model choice vs. workflow redesign, and why it's Claude-first

Hiring generalists over specialized AI talent

Can this scale without turning into another consulting firm?

Outro

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