Uploaded June 2026 | Updated September 2026, 2 weeks ago
Harper Carroll is a computer scientist who built machine learning systems at Meta but she also describes herself as "born an actress from Manhattan." She’s combined those disparate parts of her background into a unique role as an AI educator, where she uses her magic superpower of making sense of AI to reach half a million people on social media and beyond. She has a knack for explaining how models actually work, covering concepts like optimization and token distributions and the math behind them in terms that land for people who've never opened a Python notebook.
Harper sat down with Tim to talk about how she makes technical complexity incredibly relatable, but they also thought through some of the more comprehensive challenges the industry is facing. Those ranged from the technical, as Harper explained why fine-tuning a small open source model beats prompting even the best closed-source model when you're trying to capture voice, to cultural considerations like the need to shift the narrative from fearing AI to explaining how AI can expand ambition both for individuals and for organizations, why we should treat AI as a medium like photography or writing, and why open source AI is a much bigger story than open source models. And in keeping with both Harper’s and Tim’s focus on learning, they discussed the skills everyone in the workforce will need to have to use AI effectively. That’s a social problem to the extent that we’ll need to ensure that everybody learns enough about AI so we don't end up with AI haves and have-nots. But it’s also a recognition that AI education is becoming a critical part of the path to success for all kinds of jobs.
"The people who are really going to struggle," Harper told Tim, "are the people who are not willing to accept that AI is coming and are not willing to learn it."
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Harper Carroll is a computer scientist who built machine learning systems at Meta but she also describes herself as "born an actress from Manhattan." She’s combined those disparate parts of her background into a unique role as an AI educator, where she uses her magic superpower of making sense of AI to reach half a million people on social media and beyond. She has a knack for explaining how models actually work, covering concepts like optimization and token distributions and the math behind them in terms that land for people who've never opened a Python notebook.
Harper sat down with Tim to talk about how she makes technical complexity incredibly relatable, but they also thought through some of the more comprehensive challenges the industry is facing. Those ranged from the technical, as Harper explained why fine-tuning a small open source model beats prompting even the best closed-source model when you're trying to capture voice, to cultural considerations like the need to shift the narrative from fearing AI to explaining how AI can expand ambition both for individuals and for organizations, why we should treat AI as a medium like photography or writing, and why open source AI is a much bigger story than open source models. And in keeping with both Harper’s and Tim’s focus on learning, they discussed the skills everyone in the workforce will need to have to use AI effectively. That’s a social problem to the extent that we’ll need to ensure that everybody learns enough about AI so we don't end up with AI haves and have-nots. But it’s also a recognition that AI education is becoming a critical part of the path to success for all kinds of jobs.
"The people who are really going to struggle," Harper told Tim, "are the people who are not willing to accept that AI is coming and are not willing to learn it."
Follow O'Reilly on:
LinkedIn: linkedin.com/company/oreilly
Facebook: facebook.com/OReilly
Instagram: instagram.com/oreillymedia
BlueSky: https://bsky.app/profile/oreilly.bsky.social
TikTok: tiktok.com/@oreillymedia
