Rajiv Pant thinks of AI as an Iron Man suit for the mind. Something you put on. That you fuse with. That takes you to greater heights — but could also make you incredibly dizzy and be very dangerous if you, the human, don't stay in control of it.
Rajiv sees successful collaboration with AI as a “synthesis.” And to that end, he’s building a series of skills and methodologies for synthesis engineering, coding, writing and project management.
In this episode, Rajiv explains why
synthesis engineering is a kind of middle ground between vibe coding and agentic engineering. It’s a method for human-AI collaboration that helps builders go faster while not falling into the trap of letting AI do the things we humans ought to own. i.e. The architecture. The judgment. The thinking and learning.
Rajiv is an engineering and product leader with deep experience in media. He’s held senior roles at the Wall Street Journal, Hearst, and the New York Times (where he and I first met). Today he's the president of
Flatiron Software.
Rajiv has open-sourced all of his Synthesis methodologies and he and I also discuss why open source is so important as we increasingly turn to AI to sharpen our thinking.
Can we really trust a system we don’t understand? Would Tony Stark have trusted his suit if he didn’t know how it was built?
Chapters:
- (00:00) - Iron Man suit for the mind
- (02:11) - What goes wrong when you vibe code into production
- (04:20) - What synthesis coding looks like hands on keyboard
- (05:40) - What AI code slop looks like
- (08:30) - The unexpected joy of managing a team of agents
- (11:00) - Using AI as a thinking partner without outsourcing your thinking
- (15:30) - How a non-programmer built a better version of his own software
- (18:15) - Is your use of AI making you dumber?
- (23:26) - Trusting AI when it’s a black box
- (27:11) - If Tony Stark owned your suit, would you trust it?
- (28:26) - What AI does to the economics of open source
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Future Around & Find Out helps builders think clearly about AI and emerging technologies, grapple with the implications, and decide what to build next.
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