Podcast Overview
The episode of People by WTF recorded in San Francisco is hosted by stock‑market investor Nikhil Kamath. He brings together four AI‑stack builders – Akshay Kothari (Co‑founder of Notion), Azalia Mirhoseini (Co‑founder of Ricursive Intelligence), Anna Goldie (Co‑founder and CEO of Ricursive Intelligence) and Vijay Krishnan (Co‑founder of Turing) – to debate whether the current AI boom is real and where it is headed.
Key Figures
One panellist estimates total AI‑related spending this year at roughly USD 700 billion, while enterprise AI revenue is cited at about USD 100 billion. The discussion notes that software accounts for only about 1 % of U.S. GDP, but services and knowledge work – the larger share of the economy – are expected to capture most of the AI‑driven value.
Economic Implications
Despite the massive capital inflow, the panel observes that productivity gains have not yet shown up in GDP figures. Software teams in the Bay Area can now operate at 40‑60 % of their former headcount, a reduction described as a “giant productivity improvement”. However, Krishnan challenges the notion of broad revenue impact by asking for a Fortune 500 company outside the AI business that has meaningfully grown revenue or cut costs.
Compute Demand & Chip Innovation
Mirhoseini warns that AI inference demand could increase 10 to 100 times current levels, and on a conservative projection the world will need 100,000 times more compute in five years than today. She argues that scaling data centres alone will not meet this need. Ricursive Intelligence’s answer is AI‑designed custom chips that target specific workloads, aiming to escape the “hardware lottery”. Their AlphaChip system, originally built at Google for chip layout, has been used across five generations of TPUs, as well as in Google’s Axion, Pixel phones, Waymo chips, and has been adopted by MediaTek.
AI Productivity & Workforce Impact
Krishnan explains that high‑quality, targeted data can dramatically improve models, prompting firms to hire experts to craft problems that current models cannot solve. Turing leverages more than 5 million knowledge workers across 70 plus countries, ranging from software engineers to doctors. He cautions that today’s work will become obsolete within nine months as models learn the tasks.
Adoption Patterns
Kothari notes that at the start of the year only 1 % of Notion’s users interacted with AI agents; that share has risen to 30 %. Users who engage via agents without opening the app are 2‑3 times more active, reflecting a shift from “token maxing” to “token economics”. Engineers in the Valley have moved from writing code to managing agents in under a year.
Risks & Safety Concerns
The conversation touches on AI safety: Krishnan likens autonomous‑weapon logic to nuclear‑age game theory, while Mirhoseini and Goldie (both former Anthropic employees) affirm that Anthropic’s leadership takes safety concerns seriously.
Implications for India
Kamath argues that India must “build compute” as a first step. He warns that the AI transformation will leave a gap where traditional IT outsourcing once operated, and that Indian IT firms have missed the chance to become the world’s “Turings”. Kothari points out the difficulty of starting a business in India, citing a three‑month paperwork and notarisation process for a single angel investment. Goldie mentions a Stanford collaborator selected by the Indian government who received thousands of GPUs to build a speech model for Indian languages. Kamath also cites Skyroot, the Indian space startup attracting U.S. investors, as an example of India excelling at delivering high‑cost global capabilities at a fraction of the price.
Investment Perspective
When asked where he would allocate USD 100 million in the AI stack, Mirhoseini suggests focusing on compute efficiency – spending $100 million on a model that is ten‑times more efficient is equivalent to a $1 billion spend. Goldie advises founders to ask whether only they can solve a problem; if not, she suggests refraining from starting a company. Other suggestions include establishing a university for product design in India and creating AI tools that enable children to learn ten times faster.
Closing Remarks
Kamath reflects that if he had $100 million, his instinct would be to invest in social‑media‑type platforms that eventually push users away from devices. He also shares a personal anecdote: Sam Altman once advised him to buy luxury brands like Hermès and LVMH, betting on rising productivity‑driven consumer spending; those stocks have since fallen. The episode is now available on YouTube.