Your signal. Your price.
Jason Calacanis says startup founders should ignore traditional TAM analysis for novel ideas, citing Airbnb and eBay as companies that induced entirely new markets. He says bad VC behavior often stems from an inability to assess non-existent markets.
Sue Kim says Brilliant’s pricing is benchmarked against human tutors, not casual apps. The goal is a product that does 95% of a tutor's job for 30 dollars a month, a fraction of the typical 10,000 dollar annual tutoring cost.
Jason Calacanis recounts a story where John Doerr attended a pitch meeting directly from the emergency room after a biking accident, viewing it as a sign of ultimate commitment despite Doerr being groggy.
Sue Kim says 40% of Brilliant’s users are in the US, with 60% international. This drove the choice of the name Cooji, which is short, globally accessible, and not tied to a specific language.
Sue Kim says Brilliant chose a direct-to-consumer model over B2B sales to schools to stay close to learner feedback. They read every app store review and customer email for real-time product development insights.
Sue Kim says the ability of frontier LLMs to tutor well has plateaued since GPT-3.5 because they lack verifiable reward signals for learning outcomes. Brilliant's unique dataset of tutoring sessions provides that signal for model improvement.
Jason Calacanis tells a story of a VC firm canceling a meeting while he was driving to it after a cross-country flight. He confronted the investor, calling him the worst venture capitalist of all time.
Sue Kim says Brilliant’s vision is a world-class tutor in every home for every subject and language. They are expanding from math and coding into science and younger age groups, leveraging LLMs for high-quality localization.
President Trump confirmed the government is exploring taking an equity stake in major AI labs, citing a concept where 'the American public essentially becomes a partner with the companies' to benefit from AI success.
OpenAI is pitching the idea of donating equity to the US government to seed a public wealth fund, viewing it as a way for the public to benefit from AI growth through potential dividend distributions.
Google signed a three-year deal to pay SpaceX $920 million per month to rent compute capacity, granting access to at least 110,000 Nvidia GPUs from October 2024 through June 2029.
With the Anthropic and Google deals, xAI will receive $26 billion per year in compute licensing revenue, implying an 18-month payback period on its reported $40 billion data center investment.
SpaceX has 550,000 GPUs, making it the largest neo-cloud provider with more than double the capacity of CoreWeave, and GPU rentals are now its biggest business line.
Nvidia secured a two-year supply deal with SK Hynix to ensure high-bandwidth memory for its next-generation Vera Rubin chips, deepening ties as demand creates industry-wide shortages.
Financial Times reported OpenAI is planning its biggest ChatGPT overhaul to transform it into a 'super app' combining coding tools and AI agents, targeting business customers and higher revenue.
OpenAI's enterprise business now represents 40% of revenue from 2 million business customers, with a target to reach 50% by December, as the company shifts focus towards lucrative enterprise clients.
OpenAI CFO Sarah Friar said free ChatGPT users average 7 turns per day, while paid tier Plus users ($20/month) do 3x that and Pro users do 11x, revealing a major usage gap between casual and power users.
The AI advantage gap is compounding as agent users experience exponential value growth through coding loops, while regular chat users see only linear gains, pushing labs to overhaul interfaces.
Developers are shifting from prompting coding agents to designing automated loops that prompt agents, a next-level abstraction exemplified by Claude Code creator Boris Cherney who now writes loops instead of code.
Nikesh Arora argues AI democratizes intelligence, allowing 250 marketing employees to produce 90% consistent output and enabling 5,000 customer-facing staff to operate uniformly.
Using Mythos, Palo Alto Networks found code vulnerabilities in six weeks that would have taken five to seven years using traditional methods, though the AI had a 30% false positive rate.
Arora states that AI models with Mythos-level capability for finding code vulnerabilities are already available in the wild and could be three months from widespread open-source release.
He claims analytical SaaS companies are dead because enterprises can run LLMs against their own data instead of paying for third-party analysis, citing a 90% cost reduction after replacing a 20-seat SaaS tool with AI agents.
Arora predicts enterprise data storage needs will increase tenfold within three years, creating demand for core infrastructure software like databases, while UI-heavy enterprise software will be replaced by agentic backends.
He identifies the major profit pools for AI as applications, not foundational models, and expects a new layer of AI-native application companies to emerge serving common enterprise needs.
Arora sees hardware as the cheapest way to manage low-latency, high-throughput data, noting financial firms resist cloud migration due to latency cost, and predicts a hardware manufacturing boom.
Under Arora, Palo Alto Networks grew from a $17 billion to a $238 billion market cap in eight years, and he suggests the company may expand beyond cybersecurity after proving it can run an enterprise with 90% gross and 40% net margins.
Arora believes Google is underrated and will be the first $10 trillion company due to its assets and enterprise sales force, contrasting with model-focused AI companies.
He argues national security threats from AI are overblown, stating 89% of breaches stem from stolen credentials, not sophisticated code cracking, and the real risk is economic chaos from attacks on small businesses.
Arora says AI has increased Palo Alto Networks' need for technical staff, countering the narrative that AI reduces headcount.