The Frontier

Aravind Shavas, CEO of Perplexity AI, confirmed the company's revenue grew to $500 million recently, driven primarily by Perplexity Computer's success in simplifying agent orchestration for users.

Perplexity Computer provides an intuitive interface for orchestrating multiple AI agents without API keys or complex setup, offering access to various models and connectors for tasks like research, browser automation, and data analysis.

Edwin Chen, founder and CEO of Serge AI, clarified his company's role as 'AI teaching' rather than simple 'data labeling,' employing highly educated experts to cross-examine and instill values, wisdom, and taste into frontier models.

Serge AI, which has never raised venture capital, has approximately 130 employees and 50,000 expert contractors, providing services to major AI labs including OpenAI, Google, Anthropic, Microsoft, and Meta.

Edwin Chen criticizes the trend of raising excessive AI capital, arguing it incentivizes volume over quality, diverts CEO focus from product to fundraising, and can lead to models prioritizing engagement over genuine usefulness.

Aravind Shavas advises founders to be disciplined capital allocators, maintaining a 'bootstrap founder' mentality even after raising substantial funds, and suggests Perplexity AI aims for profitability without significant increases in payroll or infrastructure.

Apple's M-series chips are an underrated asset for local LLM inference, outperforming DGX Spark benchmarks, and the company has already secured two-nanometer chip fab capacity for next year, positioning it for future local AI processing.

Aravind Shavas envisions Apple enabling local agent loops to run on devices, preserving user privacy for personal data like photos and messages, believing Apple is uniquely positioned to profit from this due to its chips, OS, and ecosystem.

Edwin Chen believes AI models will not be commodified due to their distinct personalities and specializations, arguing users will naturally prefer different models based on their mood or the specific task, similar to choosing friends.

Aravind Shavas argues that value in AI accrues at the application layer, as pure API model companies struggle to maintain a significant lead for long; the performance gap between frontier models typically shrinks to months.

Edwin Chen describes LM Arena as a 'terrible cancer on AI,' leading models to prioritize 'pretty formatting' over correctness due to companies optimizing for its visible yet flawed benchmarks, making models ultimately worse.

For model evaluation, Edwin Chen advocates measuring real-world human usage and practical helpfulness, rather than contrived benchmarks, ensuring models produce creative and useful outputs that truly benefit users.

Aravind Shavas developed Perplexity's 'Model Council' at Jensen Huang's suggestion, allowing users to query multiple models simultaneously, compare their responses, and receive a synthesized analysis highlighting agreements and disagreements.

Jason Calacanis praises Whisper Flow for its superior speech-to-text accuracy, especially when combined with a foot pedal for dictating long, detailed prompts, which significantly improves model responses.

Aravind Shavas is impressed by the improved Grok integration within X, particularly the 'Explain Grok' button for contextualizing tweets, and by the Gemini 3 Flash model for its exceptional speed and intelligence.

Edwin Chen highlights Claude Design for its well-designed and opinionated interface, which enables non-designers to rapidly prototype new interfaces and landing pages, accelerating product development.

Edwin Chen and Jason Calacanis share experiences where AI models, like those analyzing blood work results or personalized health trackers (e.g., Whoop), provided more effective and tailored health recommendations than human doctors.