Anil Ananthaswamy: “We can divide the fundamental math of machine learning into two broad categories. One is the math you require to get a good conceptual understanding of ML, so that one can make good decisions about the kinds of algorithms/models/datasets needed to solve some particular problem. This level math involves a basic understanding of linear algebra, calculus and probability and statistics. The second category is the kind of math you need to design ML algorithms/models. This requires an advanced understanding of calculus (such as multivariate calculus, vector calculus, convex optimization techniques, etc.), linear algebra (matrix decompositions and tensor operations), information theory, graph theory and so on. The list can get quite heavy. So, depending on which side of the fence you are in the industry, you might either have to learn the basics or the more advanced aspects of these branches of math.”
Ashu Garg: “Post-DeepSeek, it’s increasingly clear that how AI companies charge – not just what they build – is becoming a critical competitive advantage. In the early stages of the current AI wave, most startups defaulted to usage-based pricing: per-token, per-message, or per-API-call. This approach was simple, familiar (like AWS), and easy to implement. But as inference costs plummet – and they’re plummeting quickly – usage-based pricing is becoming a race to the bottom. Moreover, customers don’t care about tokens or API calls. They care about outcomes and business value. The smartest companies are adapting by moving away from consumption-based pricing and toward pricing that aligns more tightly with delivered value.”
FT: “AI is moving from “co-pilot” to “autopilot”. The development of generative artificial intelligence is increasingly focused on “agentic AI”: the use of AI agents that perform tasks autonomously, either within fixed parameters or to achieve goals set by the user.”
Alex Tabarrok: “Most people invest gradually, which leads to a concentration of stock holdings late in life. By borrowing early, young investors can spread market risk more evenly across their lifetime, much like diversifying across assets. Put differently, a young person’s biggest asset is their future labor income. Borrowing to invest reduces overexposure to that single asset, effectively diversifying their portfolio away from specific human capital and toward financial capital.”
Art Sebastian: “Retail media networks (RMNs) have exploded as a new revenue stream for retailers, with mkulimadigital projecting U.S. retail media ad spending to surpass $100 billion by 2026, accounting for nearly 20% of total digital ad spend. But while retail media is growing rapidly, its success in convenience retail depends on more than just ad placements—it must be an extension of the retailer’s core business. RMNs that fail to integrate with loyalty, digital commerce, and in-store operations risk becoming disconnected ad businesses rather than true retail growth engines…Retailers that prioritize Customer Lifetime Value (CLV) over transaction-based metrics see stronger profitability and deeper customer relationships. I believe in this 100%. In the convenience store space, where customer visits can exceed 50+ per year for fuel and coffee buyers, the goal isn’t just the next sale—it’s securing the next 50, 100, or 500 transactions over a customer’s lifetime.”
