Keywords

entrepreneurship, pricing transparency, Instacart scandal, grocery shopping, Stretch app, good quests, AI pricing, startup advice, founder journey, consumer behavior

Summary

In this episode, Dalton Anderson interviews Andy Elwood, founder of Stretch, discussing the importance of pricing transparency in grocery shopping and the implications of the recent Instacart scandal. They explore the concept of 'good quests' in entrepreneurship, the innovative solutions offered by Stretch, and provide valuable advice for aspiring founders. The conversation emphasizes the need for transparency in pricing and the role of technology in enhancing consumer choice.

Takeaways

Andy Elwood emphasizes the privilege of being a founder again.

The concept of 'good quests' is crucial for impactful entrepreneurship.

Instacart's pricing manipulation raises ethical concerns in grocery shopping.

Stretch aims to provide transparency in grocery pricing for consumers.

The app allows users to compare prices across different stores easily.

Future updates will enhance personalization and user experience in Stretch.

Entrepreneurs should focus on solving real pain points, not just nice-to-haves.

Identifying a long-term problem is essential for sustainable startups.

Writing down frustrations can help identify potential business ideas.

Building a startup requires passion and commitment to the problem being solved.

Sound bites

"We're doing the right work here"

"Focus on good hard quests"

"Feeding our families is not a choice"

Chapters

00:00 Introduction to Stretch and Pricing Transparency

03:22 The Concept of Good Quests in Entrepreneurship

09:16 Instacart Scandal and Pricing Manipulation

15:10 Stretch's Solution for Grocery Shopping

24:10 Future Vision and Personalization of Stretch

30:49 Advice for Aspiring Founders

Learn More

https://stretchformore.com/

https://www.linkedin.com/in/andyellwood/

Venture Step

https://www.daltonanderson.net/venture-step/

Keywords

entrepreneurship, AI tools, Base 44, Emergent, Lovable, Riplet, Firebase GenKit, MVP, development tools, technology trends

Summary

In this episode, Dalton Anderson discusses various tools for entrepreneurs, focusing on AI development tools like Base 44, Emergent, Lovable, and Riplet. He provides insights into their functionalities, strengths, and weaknesses, emphasizing the importance of choosing the right tool for quick MVP development. The conversation also touches on Firebase GenKit and its unique approach to app development. Dalton concludes with a comparison of the tools and shares his final thoughts on their effectiveness in the entrepreneurial landscape.

Takeaways

It's 2016, a great time to start your entrepreneurial journey.

AI tools are evolving rapidly, offering new capabilities.

Base 44 stands out for its comprehensive output and user interface.

Emergent lacks the functionality expected from a leading tool.

Lovable has made significant progress in its offerings.

Riplet has potential but faced issues during testing.

Firebase GenKit is more of an orchestrator than a full stack generator.

Choosing the right tool can significantly impact your development process.

Documentation and architecture are crucial in app development.

The entrepreneurial landscape is filled with tools that can help streamline processes.

Sound bites

"Base 44 crushes their competitor."

"Base 44 is the gold standard."

"GenKit has potential, needs work."

Chapters

00:00 Introduction to Entrepreneurship and Industry Trends

00:00 Challenges in Modern Entrepreneurship

00:13 The Best Time to Start Entrepreneurship

00:14 Navigating Industry Trends

00:31 Kickstarting the New Year: Tools for Entrepreneurs

01:00 Exploring AI in Entrepreneurship

01:05 Kickstarting Your Entrepreneurial Journey

03:48 Exploring Full Stack Generators

07:59 Base 44 vs. Emergent: A Comparative Analysis

21:22 Lovable and Riplet: Quick MVP Solutions

32:57 Firebase GenKit: A New Approach to Development

Keywords

Google Workspace Studio, automation, email management, productivity, AI tools, Microsoft Power Automate, entrepreneurship, technology trends, Google Gemini, project management

Summary

In this episode of the Venture Step Podcast, Dalton Anderson discusses the newly released Google Workspace Studio, exploring its features, automation capabilities, and how it compares to Microsoft Power Automate. He shares insights on email management, workflow automation, and the potential future developments of the platform. The conversation also touches on the importance of distinguishing between a product and a feature in the tech industry, concluding with reflections on personal value in relationships.

Takeaways

Google Workspace Studio is a new automation tool from Google.

It offers low-code solutions for email and task management.

The transition from Maker Suite to Workspace Studio reflects Google's rebranding efforts.

Workspace Studio is compared to Microsoft Power Automate, with pros and cons for each.

Automation can significantly improve meeting efficiency and email management.

The integration of AI tools like Gemini 3.0 enhances Workspace Studio's capabilities.

Users can create custom workflows to manage emails and tasks effectively.

The importance of organizing digital files for better accessibility is emphasized.

Understanding the difference between a product and a feature is crucial for tech companies.

Personal value in relationships should be considered, just like in business.

Sound bites

"Google, you gotta stop doing that."

"It just makes more efficient meetings."

"Thank you for listening to the show."

Chapters

00:00 Introduction to Google Workspace Studio

02:47 Exploring Features and Automations

06:08 Comparing Google Workspace Studio and Microsoft Products

08:53 Practical Applications and Workflows

12:00 Live Demo of Google Workspace Studio

12:09 Introduction to Google Workspace Studio

16:59 Transition from Maker Suite to Workspace Studio

22:24 Exploring Google Workspace Features

26:35 Demonstrating Email Automation Flows

33:18 Managing Email Attachments and Organization

39:51 Automating Promotional Emails and Updates

44:38 Future of Google Workspace and Agentic Workflows

47:03 Product vs. Feature: A Critical Perspective

Keywords

pricing, surveillance pricing, legislation, consumer rights, algorithmic pricing, Instacart, market manipulation, historical pricing, value of service pricing, consumer surplus

Summary

In this episode of the Venture Step podcast, Dalton Anderson explores the evolution of pricing from historical negotiation practices to modern surveillance pricing driven by AI algorithms. He discusses the implications of these changes on consumer rights, the recent Instacart scandal, and the legislative responses aimed at regulating algorithmic pricing. The conversation highlights the moral and ethical considerations surrounding pricing strategies and the potential future of pricing legislation.

Takeaways

The AI algorithm is now determining consumer pricing based on data.

Surveillance pricing can lead to manipulation of consumers.

Historical pricing was based on negotiation and transparency.

Legislation like the Interstate Commerce Act was created to protect consumers.

Instacart's pricing practices led to a significant FTC fine.

Value of service pricing was a historical method of pricing based on consumer need.

New bills are being introduced to combat algorithmic pricing.

The impact of pricing algorithms can lead to significant consumer costs.

Legislative responses are evolving in reaction to pricing scandals.

The future of pricing legislation is uncertain but necessary.

Titles

The Future of Pricing: Algorithms vs. Ethics

Surveillance Pricing: The New Consumer Challenge

Sound bites

"That's manipulation, those are quite different."

"We don't care if it's a good or a service."

"This is unchristian. We're not doing this."

Chapters

00:00 The Evolution of Pricing: From Negotiation to Algorithms

06:09 Surveillance Pricing: The New Age of Consumer Manipulation

12:55 Value of Service Pricing: A Historical Perspective

19:51 Legislative Responses to Algorithmic Pricing

28:16 The Future of Pricing: Trends and Implications

Support

https://www.congress.gov/bill/119th-congress/senate-bill/232

https://www.congress.gov/bill/119th-congress/house-bill/4640

https://www.ftc.gov/news-events/news/press-releases/2025/12/instacart-pay-60-million-consumer-refunds-settle-ftc-lawsuit-over-allegations-it-engaged-deceptive

https://www.justice.gov/opa/pr/justice-department-requires-realpage-end-sharing-competitively-sensitive-information-and

https://www.consumerreports.org/money/questionable-business-practices/instacart-ai-pricing-experiment-inflating-grocery-bills-a1142182490/

Keywords

Instacart, Consumer Reports, dynamic pricing, AI, surveillance pricing, Eversight, customer surplus, price discrimination, grocery staples, algorithm ethics

SummaryIn this episode of the Venture Step Podcast, Dalton Anderson dives into a recent Consumer Reports investigation regarding Instacart’s use of AI-enabled dynamic pricing. Dalton explores how the technology, powered by a company called Eversight, aims to reduce "customer surplus" by charging users different prices for the exact same goods based on their willingness to pay. The discussion highlights the ethical concerns of applying airline-style pricing logic to essential items like milk and eggs, the manipulation of discount perception, and the potential for discriminatory profiling against vulnerable populations.

Takeaways

While society generally accepts variable pricing for airline seats due to supply and demand, applying this logic to essential goods like bread and eggs raises significant ethical questions.

A Consumer Reports investigation involving 437 volunteers found that 74% of items selected had different prices for different people in the same geographic areas.

The study revealed massive markups, such as a 23% increase for Wheat Thins in Seattle and a 20% price swing for eggs in DC.

This pricing strategy is driven by "Eversight," which uses AI to determine the maximum price a consumer will pay, effectively eliminating the savings a customer might otherwise enjoy . Additionally, the illusion of deals is created when algorithms artificially inflate the list price for certain users to make a standard discount appear larger. Brand loyalty can also act as a disadvantage; if an algorithm knows you will always buy a specific brand, it may charge you more for it. Ultimately, there are fears this technology could evolve into discriminatory profiling, similar to failed predictive policing models, targeting users based on personal data and location.

Sound Bites"But what if that same logic applied to staple goods like milk, bread, eggs?"

"Your loyalty has a penalty now."

"It's predatory price manipulation of goods, of staple goods that people need on a day-to-day basis."

"Gross, the whole thing is gross."

Chapters00:00 Introduction: Dynamic Pricing in Groceries01:49 From Physical Stickers to Digital Black Boxes03:52 Understanding Eversight and Customer Surplus06:44 The Consumer Reports Investigation Results09:50 The Ethical Cost of AI Pricing11:03 Psychological Manipulation and the Illusion of Deals14:44 The Dangers of Surveillance Pricing and Discrimination18:39 Conclusion: The Need for Pricing Transparency

Support

https://www.consumerreports.org/money/questionable-business-practices/instacart-ai-pricing-experiment-inflating-grocery-bills-a1142182490/

Keywords

Synthetic Rubicon, Uncanny Valley, Agentic Web, AI Influencers, C2PA, Content Authenticity, Generative AI, SEO vs AIO, Deepfakes, Nano Banana, Digital Trust.

Summary

In this episode, Dalton Anderson explores the concept of the "Synthetic Rubicon"—the point of no return where AI-generated content becomes indistinguishable from reality. We have officially crossed the uncanny valley regarding static imagery. Dalton breaks down the implications of the $8.5 billion AI influencer market , the current limitations of AI video and audio , and the massive shift toward an "Agentic Web" built for bots rather than humans. Finally, the episode discusses the C2PA initiative—a "nutrition label" for digital content designed to restore trust in a post-reality internet.

Key Takeaways

• Crossing the Uncanny Valley: We have reached a point where it is nearly impossible to discern between synthetically created images and real ones, with issues like dead eyes and skin texture largely solved.

  
  

• The Rise of AI Influencers: By 2030, the AI influencer industry is projected to be worth $8.5 billion. Brands prefer them because they offer complete control, don't age, don't sleep, and carry no risk of human controversy.

  
  

• The Agentic Web: The internet is shifting from being human-centric to being optimized for AI agents. Future SEO will require providing deep context for AI models rather than simple answers for human users.

  
  

• Trust & C2PA: To combat deepfakes, the Coalition for Content Provenance and Authenticity (C2PA) is proposing cryptographic keys that prove an image came from a legitimate camera sensor—essentially a verification standard for reality.

  
  

• Video & Audio Lag: While static images are perfect, AI video still struggles with "object permanence" (forgetting details about a subject), and audio lacks the consistent, emotional nuance of human speech over long formats.

Sound Bites

• "We have officially crossed the uncanny valley. It is almost impossible now to discern between synthetically created images and those that are real."

  
  

• "[AI Influencers] don't come with... brand hits with controversy... they don't have to sleep, they're completely controlled... and they never age."

  
  

• "Increasingly the internet is going to be built for non-humans... It's going to be built for agentic search."

  
  

• "We can't trust the safeguards that are put together by these AI models... So you know what we'll do? Let's create a key for things that were authentically created."

  
  

  
  

  Chapters

  00:00 – Intro: The Synthetic Rubicon

  01:39 – Recap: Filter Bubbles & The Imperfect Echo

  09:30 – The "Perfect Fake": Why metadata isn't enough

  11:04 – The $8.5 Billion AI Influencer Industry

  14:12 – Visual Analysis: Nano Banana vs. Reality

  25:08 – The State of AI Video: Object Permanence

  28:17 – The Final Frontier: Humanizing AI Audio

  31:25 – The Agentic Web: SEO for Non-Humans

  37:39 – C2PA: A "Nutrition Label" for Authenticity

  42:36 – Conclusion: Engagement in a Post-Reality World

• 
  

  Related Episodes

  
  

  E23 The Filter Bubble Blues: The Personalized Reality

  E63 The Imperfect Echo: AI Voice Cloning and its Current LimitsE80 Zero Click: SEO for AI Search, Google Rankings & Agentic AI

  E82 GOOGLE'S NANO BANANA: THE VIRAL TREND THAT DETHRONED CHATGPT

  
  

  
  

  Support

Keywords

OpenAI, positioning, market expectations, talent, models, ecosystem

Summary

In this conversation, Dalton Anderson discusses the positioning of OpenAI in the market, emphasizing its reputation as a leader in talent and technology. He highlights the expectations that come with this positioning and the implications for performance and perception in the competitive landscape.

Takeaways

OpenAI is seen as the best in talent and technology.

Market expectations are high for leading companies.

Not meeting expectations can lead to negative perceptions.

Positioning affects how companies are viewed by the market.

The ecosystem surrounding a company influences its success.

Being the best creates pressure to maintain that status.

Expectations can drive innovation and performance.

Companies must continuously evolve to meet market demands.

Reputation is crucial in the tech industry.

Leadership in technology requires constant improvement.

Sound bites

"OpenAI was positioned as the best talent."

"The market expects you to be the best."

Chapters

00:00 The Positioning of OpenAI

00:13 Expectations and Market Perception

KeywordsThe $200 Billion Gap: Climate, Catastrophe, and the Broken Insurance Market

Climate risk, market failure, insurance regulation, reinsurance, Department of Insurance (DOI), catastrophic risk, fair plan, Community Development Reinsurance Institution (CDRI), resiliency, parametric insurance.

Summary

• There is a $200 billion gap between climate disaster losses and what is actually covered, signaling a market failure in the insurance system.

  
  

• The insurance market is broken because it relies on historical data for pricing, but climate change has made the future fundamentally unpredictable.

  
  

• The three key groups dictating how insurance goes are the insurance company, the consumer, and the Department of Insurance (DOI), with reinsurance sitting on top for catastrophic risks.

  
  

• When insurance companies are substantially underpriced due to changing trends, they must ask the slow-moving DOI for rate changes, which can lead to public hearings.

  
  

• When large rate increases are suppressed or costs (like reinsurance) cannot be priced in, companies like State Farm exit the marketplace, leaving a void (e.g., in California and Florida)

• 
  

  Chapters

  
  

  00:00 Introduction: The $200 Billion Gap and Market Failure 01:34 Host's Health Update and Podcast Promotion

  02:15 The Background: The Broken Market and the Three Authorities (Insurer, DOI, Consumer)

  03:12 The Role of Reinsurance for Catastrophic Risk

  05:19 Actuarially Sound Rates and Regulation on Profit Margins

  06:40 The Department of Insurance (DOI) and Approving Rates/Forms

  08:50 Why the System is Failing: Historical Data vs. Current Trends

  09:35 Underpricing, Rate Changes, and the Threat of Insolvency/Market Exit

  10:19 Case Study: State Farm Exiting California and Reinsurance Costs

  12:08 Florida's Market Failure and the Void Left by Admitted Carriers

  13:43 The Insured's Tug-of-War and Desire for Reasonable Prices

  14:49 The Supply Crisis: Lack of Data Drives Up Price

  15:47 The Insurer of Last Resort: Fair Plans and Citizen Risk 17:26 Problems with the Fair Plan: Underfunded and Politically Vulnerable

  18:13 The Core Problem: Trapped in a Vicious Cycle of Loss and Hikes

  20:18 Suppressing Premiums and Incentivizing High-Risk Development

  22:00 Discussion of the Community Development Reinsurance Institution (CDRI)

  23:08 CDRI's Goal: Transforming to a Proactive System by Incentivizing Resilience

  23:38 Example: Florida's Hurricane Home Hardening Grant Program

  25:52 CDRI Model: Public-Private Partnerships for Societal Impact

  28:17 CDFI Metric: $1 Public Funding Attracts $8 Private Investment

  29:22 Innovative Products: Quixent's Sunshine Guarantee (Parametric Warranty)

  32:22 Innovative Products: EV Star (Coverage for Charging Stations)

  34:10 EV Star's Role in Improving EV Adoption and Range Anxiety

  35:01 Final Thoughts and Wrap-up