Venture Step

What happens when the assistant that helps run your day also becomes the way you discover what to read, buy, and believe? In this solo episode of Venture Step Podcast, I compare my early experiences with Meta's Muse and OpenAI's Dot. Both can take action and bring useful context into daily work, but they feel different to me. Muse feels more like a companion that wants to keep a conversation going. Dot feels more like a chief of staff coordinating work across projects. These are early impressions, shaped by how much context I have already shared with each product. I walk through Muse's setup, personalized feed, goals, and suggestions, including where the proactive prompts felt helpful and where they became distracting. I also explain why keeping twenty projects in one conversation can make even a capable assistant hard to manage. Price and access matter here too: the assistant people can try easily may have an advantage over one that is powerful but expensive or unfamiliar. That leads to the larger question. The phone became the default way to reach people, services, and information. AI assistants are competing to become the next front door. I use Meta's history with Instagram, Stories, Reels, and Threads to explain why distribution can matter as much as product quality. Then I ask what happens when one assistant starts choosing the news, recommendations, services, and next steps that reach you first. I'm optimistic about how much work these tools can remove. I'm also trying to keep my own judgment and room for discovery as they become more useful. If an assistant handles more of your life, who decides what it shows you? CHAPTERS 00:00 Muse, Dot, and the shift beyond model benchmarks 04:52 Venture Step update and where to watch the show 06:51 My first experiences with Muse and Dot 10:22 Muse's personalized feed, ideas, and goals 13:49 Dot as chief of staff; Muse as companion 18:10 Why one chat gets crowded across projects 20:27 Price, access, and who each assistant serves 21:27 The phone as the distribution point 24:04 Meta's acquire, adapt, and build playbook 30:10 Why ease of use can win the next AI habit 32:13 The assistant as gatekeeper for choices and information 34:40 Discovery, agency, and the risk of being managed 36:35 Closing thoughts SOURCES Meta, Introducing Muse: https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/ OpenAI, Introducing dots: https://openai.com/index/introducing-dots/

What do you do when the technology you believe in prevents customers from using your product? In this solo episode of Venture Step Podcast, I review Tae Kim's [*The Nvidia Way: Jensen Huang and the Making of a Tech Giant*](https://wwnorton.co.uk/books/9781324086710-the-nvidia-way). NVIDIA's position today can make its rise look inevitable. The book takes us back to the years when Jensen Huang, Chris Malachowsky, and Curtis Priem still had to decide whether to leave secure jobs and when early product bets nearly exhausted the company. The NV1 and NV2 were technically ambitious, but compatibility problems made adoption difficult. I walk through the decision to invest in simulation, the pressure to redesign with little runway, and the RIVA 128 launch. For anyone building a product, the question is familiar: are you solving a problem the market can use now, or asking customers to follow you somewhere they are not ready to go? Surviving once was not enough. I explore how a faster release rhythm, backward compatibility, and close work with PC manufacturers helped NVIDIA stay in the conversation. More releases meant more chances to learn. I connect that lesson to slower insurance product cycles and the danger of waiting years to get real market feedback. The story also runs through relationships and people: "rough justice" in a long-term partnership with TSMC, the Xbox deal, years of persistent recruiting, Jensen's whiteboard reviews, and his "top five" updates. Then I turn to researcher feedback, CUDA, and the investment that made GPUs useful beyond graphics and central to AI work. Listen for the painfully slow simulator, the employees who taped icons to their desks to save time, and the practical choice to cut scope while protecting a release date. My takeaway from the book: technical excellence and business judgment have to develop together. The visible victory lap rarely shows the cost of getting there. CHAPTERS 00:00 Introduction, NVIDIA's founding, and Jensen Huang's vision 02:10 Product development and market missteps 04:56 Innovative use of emulators in chip design 07:36 Market feedback and product iteration 10:18 Staying ahead with rapid release cycles 13:39 Building long-term partnerships and distribution 16:56 Talent acquisition and company culture 20:23 The future of NVIDIA and AI innovation 23:11 Key takeaways for entrepreneurs and tech leaders SOURCES Tae Kim, *The Nvidia Way*: https://wwnorton.co.uk/books/9781324086710-the-nvidia-way NVIDIA corporate timeline, including the 1997 RIVA 128 launch: https://www.nvidia.com/content/timeline/time_97.html NVIDIA's CUDA overview: https://developer.nvidia.com/hpc

What changes when you stop asking AI for answers and start handing it work? Dalton breaks down OpenAI's DevDay announcements through a live experiment: call Dot, assign a plain-English briefing, request a sourced fact check, and ask it to design a five-second Venture Step intro. Then keep recording while the work happens and return to inspect the results. The bigger idea is a personal chief of AI staff: one place to communicate with agents, delegate tasks, and review their output. Dalton explores why avatars could make that feel more approachable, how Space and Pages could support collaboration, and why ChatGPT's plugin ecosystem matters for founders trying to reach customers. Enthusiasm meets friction: computer crashes, changing usage allowances, and the difficulty of keeping multiple projects organized in one chat. The intro demo shows the promise of handing off a creative task, while the follow-up conversation sharpens what was actually announced. Two distinctions matter: OpenAI's audience claim is "our collective 1.2B weekly users," not an independently verified ChatGPT active-user count. Using a ChatGPT allowance in participating tools covers eligible AI work; it doesn't automatically pay for an entire third-party product. Listen for "I'm recording the episode plus I'm getting work done," the live intro reveal, and the question of whether reviewing a deliverable becomes more important than steering every prompt. CHAPTERS 00:00 DevDay and the live experiment 01:36 Calling Dot and assigning the tasks 09:51 Why AI agents have avatars 16:36 Specialized Dots and collaboration 18:09 Space and Pages 21:30 Usage allowances and developer tools 30:17 ChatGPT as a business distribution channel 34:41 Checking Dot's results 41:11 Allowance and audience-count clarifications 42:22 The intro reveal 43:11 From idea to action and the organizational gap SOURCES OpenAI DevDay recap, including model/API releases, Space, plugins, Sign in with ChatGPT, and Pro 500: https://openai.com/index/devday-2026-recap/ Dots: https://learn.chatgpt.com/docs/dots Pages: https://learn.chatgpt.com/docs/space/pages Plugin review and discovery: https://developers.openai.com/plugins/deploy/app-review

A real town square is noisy, awkward, and alive. What happens when a social feed recreates the bustle, but the people and reactions may be synthetic? Dalton revisits his “Synthetic Rubicon” idea and asks what AI slop does to trust. He traces the term’s rise, then explains three traits that let synthetic filler spread: superficial competence, asymmetric effort, and mass producibility. Shrimp Jesus and Jean Phil show how odd content can attract attention. Michael Smith’s bot-streaming fraud and the fictional influencer Emily Hart show how attention can become money. The episode also takes the objection seriously: people have always made mediocre content, and AI can help people learn and create. The real question is who bears the cost of checking a claim, and whether human time and in-person connection become more valuable as feeds fill up. Moments to listen for: • “What if that town center was social media?” • The “whack-a-mole” gap between generating a convincing video and checking it. • “Why am I even here?” when a feed stops showing you your friends. Sources and examples: • Term history: https://www.nytimes.com/2024/06/11/style/ai-search-slop.html • Simon Willison on slop: https://simonwillison.net/2024/May/8/slop/ • Three traits of slop: https://arxiv.org/abs/2601.06060 • Shrimp Jesus research: https://cyber.fsi.stanford.edu/news/ai-spam-accounts-build-followers • Jean Phil clip: https://www.instagram.com/reel/DdZ1-maCf_V/ • Jean Phil reporting and the token’s reported market cap: https://www.dexerto.com/tiktok/who-is-jean-phil-ai-character-behind-multimillion-dollar-meme-coin-sparks-fake-persona-trend-3412528/ • Michael Smith guilty plea: https://www.justice.gov/usao-sdny/pr/north-carolina-man-pleads-guilty-music-streaming-fraud-aided-artificial-intelligence-0 • Emily Hart investigation: https://www.wired.com/story/ai-generated-maga-girls/ • Automated web traffic (not a count of social posts): https://www.imperva.com/resources/resource-library/reports/2025-bad-bot-report/ • LinkedIn’s “AI slop” feedback option: https://www.linkedin.com/help/linkedin/answer/a15410007 • YouTube CEO’s AI-slop priorities: https://blog.youtube/inside-youtube/the-future-of-youtube-2026/ Previously on Venture Step: Episode 93, “The Synthetic Rubicon”: https://venturestep.net/episodes/the-synthetic-rubicon-crossing-the-uncanny-valley

You have to make decisions before the future is certain. The question is how much you can afford to get wrong. In this solo episode of Venture Step Podcast, I review Peter L. Bernstein’s Against the Gods: The Remarkable Story of Risk, exploring humanity’s evolving understanding of risk and what it means for entrepreneurs, business owners, and anyone making decisions with incomplete information. One hypothetical bakery connects the story. Before the first customer arrives, you’ve committed money to the lease, ovens, and ingredients. From there, we work through the questions that follow: which evidence should you trust, what could a loss actually cost you, and how do you keep one mistake from ending the business? The discussion moves from Roman games of chance to Pascal and Fermat’s interrupted wager, where dividing a $100 pot requires thinking about the games still to be played. John Graunt’s mortality records lead into the value of collecting data, the danger of biased samples, and why a predictable average across many businesses doesn’t tell one owner what their actual loss will be. Even a bakery’s product testing can mislead: friends praising your jalapeño croissants are different evidence from customers coming back to pay for them. Then we turn to utility and the St. Petersburg paradox. An attractive potential payout doesn’t settle whether a risk makes sense for the person taking it. A two-ship example explores how diversification can reduce the chance of losing everything while leaving smaller losses possible. The discussion of the 2008 financial crisis brings us back to a critical assumption: exposures that look separate may still fail together. Those questions become practical again when AI starts making decisions for a business. An instruction to maximize croissant production needs limits around equipment, spending, and employee wellbeing. Delegating the work doesn’t make its consequences disappear. I also explore framing effects and why “95% success” can land differently from “5% failure.” My own approach is to leave room to experiment and make mistakes while avoiding catastrophic losses. The episode closes by asking what we might give up if the pursuit of predictability leaves no room for people to defy the odds. What’s a risk you’re willing to take, and what downside would make it unacceptable? Share your perspective and follow Venture Step for entrepreneurship, industry trends, and the occasional book review. Further listening E15: Bet on Yourself, a review of Annie Duke’s Thinking in Bets, on decision quality, luck, and uncertain outcomes. E120: The Rise of the AI Coworker, on managing AI agents with clear goals, boundaries, and human review.