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?CHAPTERS00:00 Muse, Dot, and the shift beyond model benchmarks04:52 Venture Step update and where to watch the show06:51 My first experiences with Muse and Dot10:22 Muse's personalized feed, ideas, and goals13:49 Dot as chief of staff; Muse as companion18:10 Why one chat gets crowded across projects20:27 Price, access, and who each assistant serves21:27 The phone as the distribution point24:04 Meta's acquire, adapt, and build playbook30:10 Why ease of use can win the next AI habit32:13 The assistant as gatekeeper for choices and information34:40 Discovery, agency, and the risk of being managed36:35 Closing thoughtsSOURCESMeta, 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.CHAPTERS00:00 Introduction, NVIDIA's founding, and Jensen Huang's vision02:10 Product development and market missteps04:56 Innovative use of emulators in chip design07:36 Market feedback and product iteration10:18 Staying ahead with rapid release cycles13:39 Building long-term partnerships and distribution16:56 Talent acquisition and company culture20:23 The future of NVIDIA and AI innovation23:11 Key takeaways for entrepreneurs and tech leadersSOURCESTae Kim, *The Nvidia Way*:https://wwnorton.co.uk/books/9781324086710-the-nvidia-wayNVIDIA corporate timeline, including the 1997 RIVA 128 launch:https://www.nvidia.com/content/timeline/time_97.htmlNVIDIA'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.CHAPTERS00:00 DevDay and the live experiment01:36 Calling Dot and assigning the tasks09:51 Why AI agents have avatars16:36 Specialized Dots and collaboration18:09 Space and Pages21:30 Usage allowances and developer tools30:17 ChatGPT as a business distribution channel34:41 Checking Dot's results41:11 Allowance and audience-count clarifications42:22 The intro reveal43:11 From idea to action and the organizational gapSOURCESOpenAI 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/dotsPages:https://learn.chatgpt.com/docs/space/pagesPlugin 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 listeningE15: 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.

AI is getting more capable. What happens to the people using it?I revisit earlier Venture Step conversations about AI tutors, digital companions, and intimate deepfakes, connecting those concerns to reported harms today. I discuss loneliness, excessive chatbot validation, personal data, child safety, and the TAKE IT DOWN Act.My experiences studying for the CPCU, migrating podcast files, and tackling dbrand's Robot City puzzle show why I find AI useful. They also raise a question: who bears the consequences when a useful tool becomes a harmful influence?EARLIER EPISODESE35: AI tutorshttps://youtu.be/6BwWKkZ7aeAE61: Gemini Canvashttps://youtu.be/qoGIyz0azwwE67: TAKE IT DOWN Acthttps://youtu.be/JMf253z5VEYE28: Meta AI Studio and AI companionshttps://youtu.be/_zlpebV6cHYE33: Productivity and AI relationshipshttps://youtu.be/nAW62_6pXaUE84: Ani, Meta Vibes, and digital addictionhttps://venturestep.net/episodes/the-digital-addiction-crisis-x-ai-s-ani-meta-vibes-and-the-pursuit-of-ai-slopFurther listening, E106: Robert LoCascio on children and AIhttps://venturestep.net/episodes/kid-company-uare-ai-why-you-are-the-best-ai-with-robert-locascioREFERENCESJohn Oliver: AI Chatbots (2026)https://www.youtube.com/watch?v=Ykvf3MunGf8Francesca Mani and Elliston Berry's Senate testimonyhttps://www.commerce.senate.gov/meetings/field-hearing-take-it-down-ending-big-techs-complicity-in-revenge-porn/FTC: TAKE IT DOWN enforcement and platform removal requirementshttps://www.ftc.gov/news-events/news/press-releases/2026/05/ftc-begins-enforcing-take-it-down-actOpenAI: Data use and training controls vary by product and settingshttps://openai.com/policies/how-your-data-is-used-to-improve-model-performance/dbrand Robot City puzzlehttps://dbrand.com/shop/limited-edition/robot-cityCPCU designationhttps://web.theinstitutes.org/designations/cpcuSUPPORTIn the US, call or text 988 for crisis support.https://988lifeline.org/

Short summaryMost AI demos stop when something appears on screen. This one starts there.Dalton Anderson uses OpenAI Codex to turn fictional customer feedback into a working dashboard, then critiques the first output, steers the redesign, and explains the system around reliable agent work: Plan as the map, Goal as the contract, skills as reusable procedures, and evaluations plus stop conditions for agentic loops.A practical episode for founders, operators, and builders who want better results from Codex without giving up judgment.### Mobile-first show notesMost AI demos stop when something appears on screen. This one starts there.Dalton gives Codex a fictional customer-feedback dataset and asks for a decision-ready dashboard. The first output works, but it is not good enough. That becomes the real lesson.This episode shows how to move from a one-shot prompt to a workflow you can inspect, steer, and verify.You will learn:- Why Plan is the map and Goal is the contract- The four parts of a strong goal: outcome, context, constraints, and done-when evidence- When a repeatable workflow should become a skill- How clear names keep a growing skill library usable- Why every agentic loop needs an evaluation and a hard stop- Where human judgment still mattersThe demonstration uses fictional data. No outreach is sent.## Official OpenAI resources- [Codex use cases](https://developers.openai.com/codex/use-cases)- [Build skills](https://learn.chatgpt.com/docs/build-skills)- [Build plugins](https://learn.chatgpt.com/docs/build-plugins)- [Subagents](https://learn.chatgpt.com/docs/agent-configuration/subagents)- [AGENTS.md](https://learn.chatgpt.com/docs/agent-configuration/agents-md)- [Follow a goal](https://learn.chatgpt.com/use-cases/follow-goals)- [Scheduled tasks](https://learn.chatgpt.com/docs/automations)- [Git worktrees](https://learn.chatgpt.com/docs/environments/git-worktrees)- [OpenAI Codex repository](https://github.com/openai/codex)- [OpenAI Plugins repository](https://github.com/openai/plugins)Note: the older [openai/skills repository](https://github.com/openai/skills) is deprecated and now directs readers to OpenAI Plugins.## Skill repositories worth exploring- [Anthropic Skills](https://github.com/anthropics/skills): official Claude skill examples and templates- [Superpowers](https://github.com/obra/superpowers): a cross-agent software development workflow and skill collection- [Microsoft Skills](https://github.com/microsoft/skills): skills and custom agents for Microsoft developer workflows- [Microsoft Learn Agent Skills](https://github.com/MicrosoftDocs/Agent-Skills): Microsoft and Azure skills grounded in Learn documentation- [Gemini CLI](https://github.com/google-gemini/gemini-cli): Google's open-source coding agent with Agent Skills support- [Gemini CLI Agent Skills guide](https://geminicli.com/docs/cli/using-agent-skills/)- [Agent Skills specification](https://github.com/agentskills/agentskills): the open format behind portable skills- [Vercel Skills](https://github.com/vercel-labs/skills): a cross-agent CLI for discovering, installing, and sharing skillsInstall selectively. Read a skill before trusting it, understand the tools and permissions it can use, and test it on bounded work first.## Chapters00:00 Why this episode exists01:45 Turning fictional feedback into a dashboard02:29 Commands, context, compact, goals, and Plan04:16 What the first plan is doing07:05 Steering the build with butter yellow09:03 Reviewing the first dashboard10:37 Plan is the map, Goal drives the work15:54 An honest review of the redesign17:23 Building a Goal and the four-part prompt formula21:08 Commands, skills, and reusable workflows24:57 Naming skills so they stay usable26:34 Broad threads and focused projects28:27 A simple context-and-constraints analogy30:52 Keeping agentic loops safe32:21 Closing

Exploring the Power and Limitations of AI Imagery and Voice ModelsIn this episode, Dalton Anderson dives into a hands-on exploration of various AI models, including image, voice, and interactive storytelling tools. He shares real-world demos, highlighting their strengths and areas for improvement, especially from a marketing and content creation perspective. Whether you're curious about how AI can enhance your creative projects or want a behind-the-scenes look at the current state of AI models, this episode offers valuable insights.KEY TAKEAWAYS:Microsoft’s AI models in beta offer the ability to generate images, transcribe speech, and produce voice outputs, providing a versatile toolkit for creators.The Google Nano Banana Pro image model excels at creating realistic, lived-in scenes with detailed aging and environment, but can sometimes produce inconsistent details.Prompt specificity is crucial; AI-generated images can misinterpret vague instructions, leading to hallucinations or illogical outputs, such as unrealistic headgear without appropriate suits.Voice models demonstrate strong emotional range, with certain voices like Harper’s delivering particularly convincing storytelling, while others struggle to evoke specific emotions like anger or sadness.Interactive story features currently limit depth due to character caps and simplistic responses, making them less effective for complex storytelling or nuanced character interactions.Microsoft AI Platform: microsoft.aiVenture Step website: https://www.daltonanderson.net/venture-step/Call to Action: Subscribe to Venture Step for more behind-the-scenes founder stories and AI insights.CHAPTER TIMESTAMPS:00:00 - Introduction and overview of AI models showcased00:28 - Microsoft’s AI beta platform and available models00:56 - Demonstration of image generation and critique from a marketing perspective01:25 - Comparison between Google Nano Banana Pro and Microsoft’s image model02:28 - Visual demo of Microsoft’s UI and image outputs03:01 - Creating a futuristic diner scene and assessing visual emphasis03:33 - Analyzing inconsistencies in AI-generated scenes04:33 - AI depiction of Gemini with futuristic elements and critiques05:51 - Visual analysis of the emphasis on key objects like shakes and burgers07:04 - Halucination in AI images—cat paw coming out of a table08:02 - Detail critique: gears and aged objects in AI art09:30 - Evaluation of realistic details like butterflies and aged books10:08 - Google’s attempt at capturing a dusty carpenter’s workshop11:04 - Comparing Google’s and Microsoft’s realism in craftsmen scenes12:23 - Limitations of AI in facial expressions and lighting realism13:00 - Exploring voice-to-text capabilities and story creation14:14 - Generating a short story with emotional beats15:47 - Testing voice model emotion: Harper, Ethan, Olivia16:33 - Switching perspectives to the Ant Queen in storytelling21:12 - Creating emotional stories from different viewpoints24:29 - The superiority of Harper’s voice for storytelling25:29 - Challenges with interactive stories and character limits28:34 - Wrapping up with a futuristic AI story titled 'Neon Gears of Hunger'CONNECT & LINKS:Microsoft AI Platform: microsoft.aiVenture Step website: https://www.daltonanderson.net/venture-step/Call to Action: Subscribe to Venture Step for more behind-the-scenes founder stories and AI insights.Music licensed through Soundstripe.Code: OWITVHYLRLGCXG5V, SZ1PQVE2IOUP0XDN

This episode explores the evolving relationship between Microsoft and OpenAI, the strategic shifts in AI model development, and the competitive landscape shaping the future of artificial intelligence. Dalton Anderson provides an in-depth analysis of key events, investments, and technological advancements.keywordsAI, OpenAI, Microsoft, AI models, tech competition, AI investment, cloud computing, AI strategy, GPT, Anthropickey  topicsMicrosoft and OpenAI partnership historyAI model development and competitionStrategic investments and infrastructure in AIImpact of leadership changes and corporate politicssound bites"Microsoft is hiring top AI talent.""The future of AI is highly competitive.""The AI landscape is rapidly evolving."chapters00:00 The Evolution of Microsoft and OpenAI's Relationship10:10 The Crisis: Sam Altman's Ouster and Its Implications19:54 The Shift: From Partnership to Competition30:06 The Future Landscape of AI: Competition and InnovationresourcesOpenAI Official Website - https://openai.comMicrosoft AI Models - https://microsoft.com/aiAnthropic - https://anthropic.comGoogle DeepMind - https://deepmind.comSam Altman Twitter - https://twitter.com/samaltmanElon Musk XAI - https://x.aiGoogle AI - https://ai.googleAWS AI - https://aws.amazon.com/aiOracle Cloud - https://oracle.com/cloudSoftBank Vision Fund - https://softbank.comlearn morehttps://www.daltonanderson.net/venture-step/

Discover how Google's Gemini Spark is revolutionizing AI agent workflows and transforming entrepreneurship. Dalton Anderson shares insights on the future of AI in business, automation, and productivity enhancement.keywordsAI, entrepreneurship, Gemini Spark, Dalton Anderson, Google, automation, productivity, AI workflows, venture step, AI toolskey  topicsGoogle Gemini Spark and its capabilitiesAI agent workflows and automationGrounding AI within personal and business contextManaging podcast guest pipelines with AIFuture of AI in entrepreneurship and productivitysound bites"Gemini Spark is the evolution of AI agent workflows.""Grounding AI within your account makes it more effective.""Automating outreach saves hours of manual work."chapters00:00 Introduction to Gemini Spark and its significance02:20 Background and capabilities of Gemini Spark04:08 Creating skills and customizing AI for tasks06:12 Grounding AI in personal and business context07:51 Continuous operation and scheduling with Gemini Spark09:18 Demo: Managing tasks and scheduling in Google11:50 Using AI for email management and outreach17:23 Managing podcast guest pipeline with AI22:55 Researching and qualifying podcast guests28:51 Automating outbound outreach and lead generation33:46 Final thoughts on AI's future in entrepreneurshipresourcesGoogle Gemini Spark - https://gemini.google.comlearn morehttps://www.daltonanderson.net/venture-step/