Superhuman AI: Decoding the Future

In this episode of Superhuman AI: Decoding the Future, Zain and Hassan share practical strategies for finding business opportunities in AI. They argue that building on foundational technologies like Chat GPT or Claude can be highly lucrative, citing successful examples like Perplexity, backed by Jeff Bezos.They explore four key approaches. The Operational Expenditures Attack reduces operational costs through AI automation, as seen with platforms like Harvey AI, while Feature Arbitrage leverages new AI features to quickly build niche products, exemplified by Plug AI and Jenny AI. The Personal Problem Goldmine emphasizes solving your own challenges for impactful ideas, and the Money Trail Method focuses on industries with heavy funding, like Semantic Health, addressing hospital revenue inefficiencies.Finally, they discuss the X for Y Formula, which adapts successful models like ChatGPT to new domains, and we learn why curiosity and observation are essential traits for spotting opportunities in the AI space.What we talk about:1:12 – How to find product ideas3:03 – All about Operational Expenditures10:37 – Using Feature Arbitrage for new product ideas13:35 – Why being a GPT wrapper isn’t a bad thing16:04 – The Personal Problem Goldmine25:19 – Finding ideas with the Money Trail Method30:33 – The ‘X for Y’ formula

Learn more about AssemblyAI here.In this episode of Superhuman AI: Decoding the Future, Zain and Hassan sit down with Elias Torres, co-founder and CEO of Agency, to learn about his remarkable career and vision for AI’s transformative role in customer success.Elias discusses the motivations behind his entrepreneurial endeavors (founding Performable and co-founding Drift) and the challenges he faced while building innovative companies. He explains why recruiting top talent, fostering strong company culture, and maintaining a customer-centric approach are so important when it comes to scaling, and he explores the potential of AI and large language models (LLMs) in revolutionizing customer success. Plus, Elias offers his insights on private equity and M&A and gives us his practical advice for founders, stressing the value of transparency, decisiveness, and direct customer interaction. We’ll also hear about Elias’ podcast The American Dream and he shares his aspirations for Agency to redefine customer engagement with small, agile teams. What we talk about:2:24 – Why is Elias creating another company?3:52 – What was Elias’ dream before becoming an entrepreneur?5:30 – What were some of the key moments on Elias’ journey to becoming an entrepreneur?8:05 – What is it about “The Valley” that lends itself to startup success?8:48 – What was it that drove Elias to keep pushing after selling Performable?10:16 – What are the top three lessons learned from HubSpot?12:01 – What makes software suck?12:56 – What makes a company special?15:27 – Lessons learned from M&A.19:10 – How hard is it to build a big company?21:53 – What problems does Agency aim to solve?24:14 – How does the AI wave differ from the other technology waves from the last 20 years?26:44 – Where would Elias be today if Drift had failed?30:51 – What does Agency mean for businesses?See more from Elias here:American Dream on YouTubeAmerican Dream on AppleAmerican Dream on Spotify

Learn more about AssemblyAI here.In this episode of Superhuman AI: Decoding the Future, Zain and Hassan sit down with Mike Murchison, co-founder and CEO of Ada, an AI-powered customer service platform. Mike takes us through the journey of Ada’s creation, detailing its early roots and the experiences that shaped its mission to revolutionize customer service through AI. He discusses the transformative impact of AI on customer service roles and how it’s reshaping the industry.Drawing from his firsthand experience as a contact center representative, Mike explains how Ada’s development was deeply informed by the challenges he faced in the field. Plus, he explains how this hands-on insight helped Ada secure its first clients, who quickly became advocates for the platform.Mike also shares key moments in Ada's development, including a strategic pivot to focus solely on customer service and the re-architecture of their platform around language models. He highlights the rapid advancements in AI, particularly in language and voice technology, and underscores the importance of staying agile and continuously learning to remain at the cutting edge of AI applications in customer service.What we talk about:1:24 – What is Ada?4:01 – What does AI mean for the future of customer service?6:53 – How did Mike build Ada and find his audience?9:50 – What does Ada get right when it comes to customer service?11:20 – What were some of the make-or-break points for Ada?17:41 – Is Ada going to utilize voice models?19:53 – What gives Ada its unfair advantage over the competition?22:55 – What are Mike’s thoughts on Perplexity AI?24:47 – How else is Mike using Ada in the office?26:35 – How is the Ada team tackling AI hallucinations?28:20 – What is the market like right now for AI entrepreneurs?32:14 – Which industries will become the first to bring in wider adoption of AI tools?34:04 – What should first-time founders be thinking about?

Learn More about AssemblyAI here.In this episode of Superhuman AI: Decoding the Future, Zain and Hassan speak with Luke Kim, co-founder and CEO of Liner, to explore the journey of building a successful AI search tool. Luke recounts Liner’s beginnings as a simple highlighter extension and its evolution into a specialized AI-powered search tool. Plus, Luke gives us a look into the strategic choices behind developing an AI-driven academic tool and the lessons learned along the way.We hear about the company’s relocation to Silicon Valley and how the team fuelled Liner’s early growth. And Luke shares his experience of angel investments and a Series A round led by Korean venture capitalists, drawn to Liner’s strong international user base. We also learn about Liner’s technical and competitive strategies, focusing on providing verified, reliable sources perfect for higher education users and setting it apart in a crowded AI space. Luke also shares his insights on utilizing a blend of large language models—such as Lama, GPT, and Claude—to ensures both cost efficiency and superior search capabilities. What we talk about:What we talk about:(1:14) - Who is Luke and what is Liner?(4:15) - How did Liner secure its first (of 10 million) customers?(6:37) - How did the move to Silicon Valley improve Liner?(7:30) - How did Luke develop his network when he arrived in Silicon Valley?(8:22) - How was Liner funded in the early days?(11:56) - What did the journey from 1,000 to 10 million users look like?(15:05) - How is Liner utilizing different LLMs?(17:40) - How does Liner fact-check in order to deliver reliable results?(20:23) - How does Liner rank results for its users?(21:43) - What is the 10-year vision for Liner?(24:58) - What advice does Luke have for entrepreneurs starting out in the AI space?(27:01) - What are investors looking for in applied companies?(29:16) - What is Luke most excited about in the AI space in the next 12 months?