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.

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 EPISODES

E35: AI tutors

https://youtu.be/6BwWKkZ7aeA

E61: Gemini Canvas

https://youtu.be/qoGIyz0azww

E67: TAKE IT DOWN Act

https://youtu.be/JMf253z5VEY

E28: Meta AI Studio and AI companions

https://youtu.be/_zlpebV6cHY

E33: Productivity and AI relationships

https://youtu.be/nAW62_6pXaU

E84: Ani, Meta Vibes, and digital addiction

https://venturestep.net/episodes/the-digital-addiction-crisis-x-ai-s-ani-meta-vibes-and-the-pursuit-of-ai-slop

Further listening, E106: Robert LoCascio on children and AI

https://venturestep.net/episodes/kid-company-uare-ai-why-you-are-the-best-ai-with-robert-locascio

REFERENCES

John Oliver: AI Chatbots (2026)

https://www.youtube.com/watch?v=Ykvf3MunGf8

Francesca Mani and Elliston Berry's Senate testimony

https://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 requirements

https://www.ftc.gov/news-events/news/press-releases/2026/05/ftc-begins-enforcing-take-it-down-act

OpenAI: Data use and training controls vary by product and settings

https://openai.com/policies/how-your-data-is-used-to-improve-model-performance/

dbrand Robot City puzzle

https://dbrand.com/shop/limited-edition/robot-city

CPCU designation

https://web.theinstitutes.org/designations/cpcu

SUPPORT

In the US, call or text 988 for crisis support.

https://988lifeline.org/

Keywords

confrontation, conversation, professional relationships, connection, resistance, difficult people, empathy, communication

Summary

In this episode, Dalton Anderson tackles the challenge of dealing with "difficult" people, arguing that confrontation is best resolved by transforming it into a conversation. He explains that people are rarely difficult without a reason, as being difficult costs energy and is uncomfortable. The key, he suggests, is to build a genuine personal connection, which allows you to pull someone aside and understand the true reason for their resistance—be it fear, misinformation, or hidden constraints.

Takeaways

• 
  

  People are rarely difficult for no reason; being confrontational requires energy and effort.

• The best way to solve confrontation is to have a conversation.

• To have a successful conversation, you must first build a genuine personal connection with the other person.

• Genuinely connecting with colleagues allows you to understand their perspective and remove ego from the situation.

• It's your job to uncover why someone is being resistant, whether it's fear of change, lack of information, or past experiences.

• If you are incorrect, admit it immediately and respectfully; this builds trust.

• Before confronting someone, you better be sure you are correct, similar to how HR already knows the answer before asking a question.

• There is almost always something blocking the "easy path" (the path of least resistance); find out what it is.

  
  

  Sound bites

• 
  

  "We label them as difficult, but people are really difficult for no reason."

• "Instead of being confrontational, be conversational."

• "HR doesn't ask you questions unless they already know the answer, by the way."

• "People typically take the path of least resistance."

• "Why is there this flag in the ground and they're just standing on it?"

  "Pull them aside and say this is the new way, get on board or find a job. That's confrontational."

  
  

  Chapters

  
  

• 00:00 Introduction: The Problem with "Difficult" People

• 01:29 Episode 90 Announcement & Podcast Updates

• 03:55 The Core Thesis: Be Conversational, Not Confrontational

• 04:15 The Power of a Genuine Personal Connection

• 06:25 The Importance of Admitting When You're Wrong

• 08:24 People Aren't Difficult for No Reason

• 09:20 Uncovering the Root Cause of Resistance

• 11:15 Building Informal, Effective Professional Relationships

• 13:20 Why Are They Blocking the Easy Path?

• 15:47 Example 1: The Unresponsive Stakeholder

• 17:53 Example 2: The Resistant Salesperson (Fear of Change)

• 20:15 Example 3: The Nitpicking Client

• 21:30 Final Summary & Wrap-up

  
  

Keywords

AI, entrepreneurship, real estate, job displacement, innovation, commercial real estate, data accessibility, industry disruption, technology adoption, future of work

Summary

In this episode of the Midget Step Podcast, Dalton Anderson and Luke Tatman discuss the transformative impact of AI on various industries, particularly real estate and insurance. They explore the risks to jobs posed by AI adoption, the potential for innovation, and the changing landscape of commercial real estate. The conversation highlights the importance of an entrepreneurial mindset in adapting to these changes and the evolving role of data accessibility in decision-making processes. As AI continues to disrupt traditional business models, the hosts emphasize the need for individuals and companies to embrace new technologies and strategies to remain competitive.

Takeaways

AI has the potential to level the playing field in various industries.

Jobs at risk due to AI adoption are not limited to entry-level positions.

AI can serve as a catalyst for innovation, enabling faster and cheaper product development.

The traditional value proposition in real estate is being challenged by AI's data accessibility.

Commercial real estate is undergoing significant transformation due to AI.

The middle market in various industries may shrink as larger firms adopt AI.

Relationships will always play a crucial role in business, despite AI advancements.

AI tools are making data more accessible to smaller firms.

An entrepreneurial mindset is essential for individuals in at-risk jobs.

The current era presents a unique opportunity for entrepreneurs to leverage AI.

Sound bites

"Jobs at risk due to AI adoption."

"AI is a catalyst for innovation."

"The value proposition stays the same."

Chapters

00:00 Introduction to AI and Industry Disruption

01:59 Jobs at Risk: The Impact of AI

07:09 AI as a Catalyst for Innovation

09:55 Siloed Knowledge in Real Estate

13:46 The Changing Landscape of Commercial Real Estate

19:02 AI's Role in Data Aggregation

23:06 The Future of Deal Origination

30:03 The Entrepreneurial Spirit in the Age of AI

Reach out to Luke via Linkedin

https://www.linkedin.com/in/luke-jamison-tatman-b272b0154/

Keywords

AI data centers, energy consumption, water usage, environmental impact, local communities, health concerns, technology, politics, sustainability, future trends

Summary

This podcast episode delves into the rapid growth of AI data centers, their significant energy consumption, and the environmental and social implications they bring to local communities. The discussion highlights the staggering statistics surrounding energy and water usage, the strain on local resources, and the health concerns arising from pollution. It also touches on the political ramifications and the need for better policies to address these issues, setting the stage for a future episode that will explore the potential benefits of these data centers and advancements in technology.

Takeaways

AI data centers currently consume 1.5% of global energy.

Predicted energy consumption for AI data centers will reach 950 terawatt hours by 2030.

One year of AI data center energy usage could power California for 30 years.

Data centers in Virginia account for 25% of the state's energy consumption.

A large data center can use up to 5 million gallons of water daily.

66% of data centers are located in water-scarce regions.

Local communities are experiencing increased energy costs due to data centers.

Pollution from gas turbines is causing health issues in nearby residents.

Data centers are prioritizing profits while socializing costs to local populations.

Future discussions will focus on the benefits and technological advancements in data centers.

Titles

The Future of AI Data Centers

Energy Strain: The Cost of Progress

Sound bites

"AI data centers use 1.5% of global energy."

"Pollution from gas turbines affects local health."

"66% of data centers are in water-scarce areas."

Chapters

00:00 The Rise of AI Data Centers

04:57 Energy Consumption and Its Implications

10:13 Water Usage and Environmental Impact

18:00 Local Community Effects and Health Concerns

29:50 Political and Social Ramifications

45:09 Looking Ahead: The Future of Data Centers

SUPPORT

https://www.youtube.com/watch?v=DGjj7wDYaiI

https://www.youtube.com/watch?v=3VJT2JeDCyw

https://www.youtube.com/watch?v=mSWgDOzfKRI