Podcast Summary

Podcast: Right About Now - Legendary Business Advice
Host: Ryan Alford, The Radcast Network
Episode: Agentic AI Is Here: How ATOMS Turns Ideas into Revenue with Ethan Ouyang
Date: February 13, 2026

Episode Overview

In this episode, Ryan Alford connects with Ethan Ouyang, head of U.S. operations for ATOMS (by Deep Wisdom), to discuss the groundbreaking rise of "agentic AI." They explore how ATOMS enables not just smarter code, but full-cycle, end-to-end AI-driven businesses. Ethan unpacks what sets agentic AI apart, the real-world capabilities (and limits) of AI agents, and why small businesses and solo founders are uniquely positioned to leverage these new tools to transform ideas into profitable products—without the traditional overhead.

Key Topics & Insights

1. What Makes ATOMS Different? (00:00–02:43)

• Traditional AI vs. Agentic AI:

  • Most current AI tools act as passive systems, only operating when prompted and focusing on isolated tasks (e.g., code, copy).
  • ATOMS as an Autonomous Team: It can research, design, execute, optimize, and even handle SEO—all coordinated by multiple agents that work together in real-world environments.
  • End-to-End Delivery: From a single prompt, ATOMS can take a rough idea to a revenue-ready product, even iterating based on feedback.

  “Instead of helping people write code faster, we help them make decisions, execute and monetize more … in a single prompt, ATOMS can research, market, design a product, then build a system, launch it, and they can even optimize revenue for you.”
  — Ethan Ouyang (00:00)

2. Demystifying Agentic AI (02:43–04:01)

• ATOMS Is Not Just ‘Siri on Steroids’:

  • Emphasizes fully autonomous decision-making, not just “prompt–execute–prompt again.”
  • Built on top of years of foundational multi-agent AI research, actively shared with the global research community.

  “Business is not just code or just implementations, it's decisions. ATOMS run the full decision loop autonomously: research, planning, execution and iteration.”
  — Ethan Ouyang (02:43)

3. Example Use Case: Turning an Idea into a DTC Brand (06:14–08:44)

• How ATOMS Works Practically:

  • Designer uploads sketches/idea → AI researches the market, suggests opportunities, and recommends directions.
  • Human signs off or requests more iterations.
  • AI builds MVPs (multiple options in ‘race mode’), executes test phases, and keeps the human in the decision loop while handling the bulk of execution autonomously.
  • SEO agents and optimization features included.

  “You don't have to control everything. You just need to make key decisions.”
  — Ethan Ouyang (08:44)

  "So they become the manager, but ... just controlling what gets done. We used to live in a world where the how really mattered ... now it's more what do you want?"
  — Ryan Alford (08:44)

4. Lowering the Barrier to Entry for Entrepreneurs (09:12–09:51)

• Resource Accessibility Shift:

  • Entrepreneurs can now access research, strategy, and execution tools previously reserved for big companies with large budgets and teams.
  • Judgment and personal vision become more valuable than in-depth technical know-how.

  “Now the execution is near instant. The judgment, the taste, become more important. That really changes who gets to build a company ... You have your own judgment ... you can go ahead and try and test and then you probably find something that's better.”
  — Ethan Ouyang (09:15)

5. Comparison to Other Tools (Base44, etc.) (10:32–12:19)

• ATOMS as a ‘Business in a Box’:

  • Handles not just app building, but all critical SaaS business functions: database, payments, storage, recommendations, deployment, user management.
  • Direct comparison to BASE44, but with broader, more integrated capability.

  “Everything end to end. And those are the core features. We support those... you can basically store your data. We can support login and logout... recommendation engine... all these features are the very core capabilities for our product.”
  — Ethan Ouyang (10:55)

6. Cost Efficiency and Technical Edge (12:27–13:53)

• How ATOMS Keeps Costs Down:

  • Decades of foundational research, efficient multi-agent orchestration, and the flexibility to use open-source models on the backend when possible.

  “We use different foundation models. Sometimes we use open source models which is way cheaper than closed source. We have a good way to deliver the same impact ... with lower cost. That’s our advantage and that’s pure technology.”
  — Ethan Ouyang (12:34)

  • Company-wide AI Adoption:
    • ATOMS is an “AI-native” company: everyone from designers to engineers leverages AI daily.

7. Debugging, Reliability, and User Support (13:53–15:36)

• Addressing Non-Technical User Concerns:

  • Continuous internal efforts to “kill bugs,” raise performance, and iterate the platform.
  • Proactive user support: documentation, Q&A, onboarding resources for founders without engineering backgrounds.

  “Most of the time our users don’t know how code works … but that’s fine actually. They are our targeted audiences. So we try to help them on board ... they should know it’s not the end of the world.”
  — Ethan Ouyang (14:37)

8. Ideal Users & Expanding Use Cases (15:36–17:16)

• Who Should (and Can) Use ATOMS:

  • Best for solo founders, indie hackers, small businesses, teams lacking deep technical/domain knowledge.
  • Real users: from DTC brands to Florida-based insurance companies to service businesses (window cleaning), all able to consolidate systems into one AI-generated platform.

  “It’s talking on solo founders, indie hackers or small small business or small teams ... That’s our targeting audiences ... you can build whatever you want to build almost right?”
  — Ethan Ouyang (15:42)

Notable Quotes & Memorable Moments

• “You don’t have to have a very clear idea. You don’t have to control everything. You just need to make key decisions.”
  — Ethan Ouyang (08:44)

• “We used to live in a world where the how really mattered because to get it done, you needed to know how. Now it’s more what do you want?”
  — Ryan Alford (08:44)

• “Everybody in the company uses AI. Not just engineers. Designers use AI to create prototypes. Engineers ... use AI to write better performance code and co-design the system.”
  — Ethan Ouyang (13:22)

Timestamps for Key Segments

| Timestamp | Segment | Key Takeaway | |-----------|-------------------------------------------------------|------------------------------------------------------------| | 00:00 | What is Agentic AI? ATOMS explained | Multi-agent, business-ready AI | | 02:43 | Autonomous Decision-Making | Beyond traditional AI tooling | | 06:53 | Real-world: DTC brand built with ATOMS | Step-by-step workflow for non-engineers | | 09:12 | The rise of accessible entrepreneurship | Lowering the barrier, focus moves to vision and judgment | | 10:32 | Feature depth and app comparisons | ATOMS vs. BASE44 and other tools | | 12:34 | Cost efficiency strategies | Open-source models, multi-agent orchestration | | 13:53 | Debugging & Reliability | AI for non-technical users, support focus | | 15:36 | Ideal Customers & Use Cases | Small businesses, solo founders | | 17:23 | Where to learn more | Website and social info |

Conclusion & Next Steps

Ryan wraps by emphasizing the moment for action—not just “what is AI,” but “how can you use it to build, monetize, and innovate?” ATOMS (atems.dev) is positioned as a tool for those ready to build, not just dream. The episode ends with practical details, encouraging listeners to reach out and explore ATOMS themselves.

Learn More

• Website: atems.dev
• Socials: X (@atems.dev), LinkedIn (Deep Wisdom / ATOMS)
• Try for Free: Sign up directly on the website for hands-on experimentation.

Summary prepared to reflect the episode’s original language, tone, and practical focus for doers and dreamers in business and technology.