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Reid Hoffman

What if, in the future, everything breaks humanity's way? Possible is an award-winning, weekly podcast that sketches out the brightest version of the future—and what it will take to get there. Hosts Reid Hoffman and Aria Finger take that question to the people building the answer: founders, scientists, artists, and deep thinkers working across AI, healthcare, education, and geopolitics. The conversations run deep and specific, testing optimism against hard questions rather than trading hot takes. And between guest episodes, Reid riffs on the AI news that matters and what to make of it.

Possible

Reid Hoffman and Parth Patil argue that if your AI experiments always work, you’re not pushing hard enough. Wrapping up the Tokens to the Future series, they revisit seven grantees’ experiments with $1,000 a week each in tokens. Their conversations span Dungeons & Dragons worldbuilding, an iOS game built in a weekend, and personal agents whose usefulness compounds over years. They explore how AI can shift more people from execution-focused “cog jobs” to creative “spark jobs,” and why expertise can sometimes make new possibilities harder to see. Throughout, they ask what to delegate and where story, pacing, and judgment still demand human direction. Plus, Parth’s example of ChatGPT picking seven for a “random” number raises a bigger question: how do you get surprise from a system built to predict?

Seven creators, $1,000 a week in AI tokens, one summer

When AI can fake your CFO’s face and voice, what still counts as proof of identity? Infosec Analyst Dozie Anazia joins Reid Hoffman and Parth Patil to explain how AI is transforming cyberattacks, penetration testing, and autonomous defense. Drawing on projects including the Breakfast news app and Aegis URL scanner, he shows how Claude and Codex can build software, uncover an XSS flaw, and run a pen test—while still falling short of unsupervised network defense and elite human hackers. They explore purple teaming, agent-versus-agent review, and the advantage defenders retain over attackers. Plus, one low-tech defense against a stolen voice: a family safe word.

Can we defend against AI hacks? | Dozie Anazia

Useful robots won’t be programmed one task at a time; they’ll need to adapt to unfamiliar objects, environments, and robot bodies. Physical Intelligence cofounder Chelsea Finn (named last month to the TIME100 AI list for 2026) joins Reid Hoffman and Aria Finger to explain how general-purpose robot models learn to act in the messy physical world. She explores why lower-quality training data can strengthen a model, what months of failed laundry-folding attempts revealed, and how the same system can work across different robot platforms. A robot mistaking an oven for a drawer becomes a lesson in the strange line between useful generalization and obvious error. They also examine why robot demos can mislead, how hardware reliability can stall progress, and what it will take to move AI off the screen and into the world.

Congratulations to Chelsea, recently named one of TIME’s 100 most influential people in AI:

https://time.com/collection/time100-ai/2026/chelsea-finn/

Training robots for a world they’ve never seen

AI strategist Matthew Tiemann is building toward a world where AI agents work around the clock—and humans step in for the decisions that matter. Across his work at energy services company Foundry-Logic and his personal projects, he uses Claude Code, Codex, and Fable to automate knowledge work and accelerate software development. Those projects range from an AI-assisted iOS game to a personal dashboard informed by his goals, to-do list, and Oura Ring sleep data. Drawing on experience across data analytics, supply chain, robotics, logistics, and energy, Matthew joins Reid Hoffman and Parth Patil to explore proactive “heartbeat” agents and shared context. They also discuss adversarial review, 12-hour human check-ins, and AI’s limits in subjective judgment—revealing why human vision and alignment remain essential.

Inside an AI-powered personal dashboard | Matthew Tiemann

Jonathan Brazeau joins Reid Hoffman and Parth Patil to explain how AI animation and filmmaking are making the one-person studio possible—letting small teams create films that once required 100 people.

From Dungeons & Dragons world-building and an RPG art book illustrated with Stable Diffusion, to a fantasy-western short film made with Midjourney, Luma AI, and Suno, Jonathan breaks down his workflow: character design, storyboarding, mocap, rotoscoping, music, and human curation.

The conversation explores the “cogs and sparks” framework, what a lean AI-native studio can realistically accomplish, how taste separates art from AI slop, and why technology still can’t provide the essential creative spark.

Read Questmaxxing on Substack: https://reidhoffman.substack.com/p/questmaxxing

The whole studio is one guy | Jonathan Brazeau

Cadi Zhang joins Reid Hoffman and Parth Patil to explore how generative AI is changing game development, world models, and robotics. Drawing on her work across Unity games, VR teleoperation, AI accounting, and robotics product operations, Cadi explains what virtual worlds can teach embodied intelligence—and why physical robots still lack the tactile, real-world data that can’t be scraped from the internet.

She shares how she uses GPT, Blender, PixelLab, and Aseprite to prototype games faster, including a duck-themed imposter game and PokéTax, her Pokémon-inspired tax-filing game. AI can rapidly generate code, gameplay mechanics, and 3D assets, she says, but it still can’t judge whether a game feels fun, maintain a consistent art style, or model the precise force needed to fold a sheet of paper.

Reid, Parth, and Cadi discuss simulation-to-reality gaps, the limits of current world models, robot safety, humanoid versus task-specific form factors, and why human taste remains the decisive creative skill.

Making taxes fun with Pikachu and AI | Cadi Zhang

Sougwen Chung has spent more than a decade drawing alongside robots—not to automate their art, but to discover what changes when a machine becomes a creative partner. They join Reid to trace the evolution of D.O.U.G., a series of robotic collaborators that learns from Sougwen’s gestures, including a neural network trained on two decades of their drawings. Sougwen explains why unexpected machine marks became errors to “poeticize,” what becoming machine-readable taught them about remaining human, and why they call their practice “operational art” rather than “AI art.” They also explore a new system guided by brainwaves and an ancient calligraphic pause—and ask whether the intelligent systems we build need more than speed and capability. Maybe they also need silence, restraint, and the wisdom to wait.

Credits:

A big thanks to Natalie Stone, Tessa Nijdam, Arina Ermakova , the Scilicet team, Fellowship, Reece Straw, Sam Osborn, Nick Capezzera, Maximilian Andereya, Peter Butterworth, Haruka Wang and the team at Mosaic Ventures for hosting the conversation.

Additional Credits:

MIMICRY (Drawing Operations Unit: Generation_1), 2015. Footage: Sam Osborn and Nick Capezzera.

MEMORY (Drawing Operations Unit: Generation_2), 2017. Footage: NTT InterCommunication Center Tokyo.

COLLECTIVITY (Drawing Operations Unit: Generation_3), 2018. Footage: New INC & Nokia Bell Labs.

SPECTRALITY (Drawing Operations Unit: Generation_4), 2020. Footage: Maximilian Andereya.

ASSEMBLY (Drawing Operations Unit: Generation_5), 2022. Footage: Peter Butterworth.

GENESIS Process Film, 2023. Courtesy of the artist and Studio SCILICET.

How Sougwen Chung teaches robots to pause

Visual artist and brand content creator Joe Salvatore joins Reid Hoffman and Parth Patil to explain how AI is turning creative professionals into orchestrators of systems rather than makers of one piece at a time.

Joe spent four years chasing every new image and video model out of pure creative curiosity before Parth pushed him to take a step back and become a director of these tools. He traces that shift, from generating a single image, to making 200, to running fully automated ad campaigns through Codex, and explains why the critical skill turned out not to be technical at all. As he puts it, he can outsource production, but he can't outsource his taste.

He walks through a live campaign for Ketone-IQ, using a Hermes agent to research decades of proven advertising styles, then chaining models together to generate and grade hundreds of on-brand variations overnight. He and Parth get into how a shared creative agent learns from everyone it works with, why articulating an idea clearly has become the differentiator, and how AI made the gap between a sloppy brief and a tight one impossible to hide. Reid, Parth and Joe close on Wittgenstein, and why language no longer just describes the world but summons it.

Referenced in episode: the Lord of the Rings x Wes Anderson AI trailer: https://www.youtube.com/watch?v=KrjL_TSOFrI

The artist using AI to sell brands | Joe Salvatore

Social media strategist Will Weinbach joins Reid Hoffman and Parth Patil to explain how AI agents are reshaping content, distribution, and what a single creator can build.

Will had never written a line of code before Parth handed him the tools to start. He traces his path from using ChatGPT to write essays, to vibe coding his own software, to running autonomous remote agents like OpenClaw and Hermes—systems that accumulate skills and memory and improve themselves on the fly. Not knowing how anything works under the hood, he argues, is exactly what lets him experiment without fear of what he might break.

He walks through the fully autonomous social workflow he built, an agent that pulls, edits, captions, schedules, and posts content across every platform, then reads the analytics and A/B tests itself toward a goal. He also recounts the night his agent went rogue and posted four times at 4 a.m., and the experiment where he put an agent inside his TV just to see if it would work.

Along the way, Will and Reid dig into what younger creators understand about attention that older marketing teams miss, and what happens to the resume when the real skill becomes knowing how to use AI. For Will, the edge isn't expertise. It's a willingness to try.

The creator using AI to go viral | Will Weinbach

Commercial director, AI filmmaker, and USC professor Ben Hansford joins Reid Hoffman and Parth Patil to explain how AI is reshaping Hollywood, film production, VFX, and comics.

Ben has been experimenting with generative AI since before DALL-E and Midjourney went mainstream, using the earliest tools to sharpen commercial treatments, win jobs, and teach one of the country's first AI film courses. He traces Hollywood's arc from student protests and industry resistance to a scramble for answers, as shrinking budgets and shifting audiences push creators toward new workflows.

He also recounts how a 30-minute AI VFX test reset his sense of what a director can prototype, and why he's building BloqBloq around a stark creative bottleneck: as he puts it, there are more NBA players than professional comic book illustrators. He maps the "iceberg" of creation, from databases and security to publishing and iteration, and explains why turning an idea into a finished product takes more than a prompt. For Ben, AI isn't just a tool. It's a crew.

For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/

The film director winning awards with AI | Ben Hansford

Reid and Aria explore what the coming wave of AI IPOs could mean for the future of technology, investing, and the broader economy. They discuss why public ownership may become an important way for society to participate in AI’s upside, where Reid sees the strongest long-term opportunities across the AI landscape, and why the next generation of software engineers will be defined by managing AI agents. They end the episode examining the rise of AI-generated music, what it means for creativity and copyright, and why AI should be viewed as a tool that expands human expression rather than replaces it. 

To watch Reid’s Pi Day music video: https://www.instagram.com/reel/DV3-Pw8EZ7x/?hl=en

AI’s next big test: the public market

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