Brodetskyi. Tech, VC, Startups

Y Combinator Group Partner, Pete Koomen, led his startup, Optimizely, to $100M ARR. In this video, Pete breaks down the enterprise sales funnel and shares his top tips on how a technical founder can start closing real deals for their startup. Apply to Y Combinator:

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The AI bubble is reaching a tipping point. As we continue to follow the GPUs, navigating what comes next will be essential.

David Luan is the CEO and Co-Founder at Adept, a company building AI agents for knowledge workers. To date, David has raised over $400M for the company from Greylock, Andrej Karpathy, Scott Belsky, Nvidia, ServiceNow and WorkDay. Previously, he was VP of Engineering at OpenAI, overseeing research on language, supercomputing, RL, safety, and policy and where his teams shipped GPT, CLIP, and DALL-E. He led Google’s giant model efforts as a co-lead of Google Brain. ----------------------------------------------- Timestamps: (00:00) Intro (01:03) Lessons from Google Brain & Their Influence on Building Adept (05:06) Why It Took 6 Years for ChatGPT to Emerge After Transformers (06:49) Takeaways from OpenAI (09:57) The Key Bottleneck in AI Model Performance (16:06) Understanding Minimum Viable Capability Levels & Model Scale (20:17) The Future of the Foundational Model Layer (33:26) Adept’s Focus for Vertical Integration for AI Agents (35:53) The Distinction Between RPA & Agents (40:24) The Co-pilot Approach: Incumbent Strategy or Innovation Catalyst (42:46) Enterprise AI Adoption Budgets: Experimental vs. Core (46:53) AI Services Providers vs. Actual Providers (49:32) Open vs. Closed AI Systems for Crucial Decision Making (54:18) Quick-Fire Round ----------------------------------------------- In Today’s Episode with David Luan We Discuss: 1. The Biggest Lessons from OpenAI and Google Brain: What did OpenAI realise that no one else did that allowed them to steal the show with ChatGPT? Why did it take 6 years post the introduction of transformers for ChatGPT to be released? What are 1-2 of David’s biggest lessons from his time leading teams at OpenAI and Google Brain? 2. Foundation Models: The Hard Truths: Why does David strongly disagree that the performance of foundation models is at a stage of diminishing returns? Why does David believe there will only be 5-7 foundation model providers? What will separate those who win vs those who do not? Does David believe we are seeing the commoditization of foundation models? How and when will we solve core problems of both reasoning and memory for foundation models? 3. Bunding vs Unbundling: Why Chips Are Coming for Models: Why does David believe that Jensen and Nvidia have to move into the model layer to sustain their competitive advantage? Why does David believe that the largest model providers have to make their own chips to make their business model sustainable? What does David believe is the future of the chip and infrastructure layer? 4. The Application Layer: Why Everyone Will Have an Agent: What is the difference between traditional RPA vs agents? Why is agents a 1,000x larger business than RPA? In a world where everyone has an agent, what does the future of work look like? Why does David disagree with the notion of “selling the work” and not the tool? What is the business model for the next generation of application layer AI companies? ----------------------------------------------- Subscribe on Spotify:

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(GeekWire File Photo / Kevin Lisota) Amazon is amping up its AI efforts by hiring executives from Adept, a San Francisco-based startup building "agents"

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