There’s an old joke about a helicopter pilot who becomes lost above the clouds. His navigation and communications equipment have failed. Eventually, through a break in the clouds, he spots a tall office office building. He flies close and holds up a sign: WHERE AM I? People inside confer,
There’s an old joke about a helicopter pilot who becomes lost above the clouds. His navigation and communications equipment have failed. Eventually, through a break in the clouds, he spots an office building. He flies close to it and holds up a sign: WHERE AM I? People inside the
People are investing billions in something LLMs monetize — and trust will collapse long before scale arrives. Because… No memory → no trust No trust → no adoption No adoption → no scale No scale → no revenue Nobody was afraid of Facebook when they signed up. Nobody feared Twitter, Pinterest, eBay, etsy, Instagram, SnapChat,
By Alan Jacobson, Systems Architect & Analyst Once upon a time, there were three queries: * One query was very complicated * One query was just so-so * And one query was duck-soup simple But all three queries got the same amount of compute at the same cost – barely makes sense,
By Alan Jacobson, Systems Architect & Analyst That’s right. I said it. We can reduce AI capex by 90%. Before I tell you how, let me explain why this matters — because the importance is both multi-faceted and not immediately obvious. In the meantime, know this: The problem isn’
Wall Street is clear LLMs extended a five-day selloff, even as the NASDAQ was propped up by chipmakers. But flat adoption rates and pre-execute provisioning efficiencies point to falling demand for AI infrastructure. History is clear From the 1873 railroad crash to the 2000 dot-com bubble, when
Unlike ecommerce, search, and social, AI doesn’t get cheaper with scale. It gets more expensive. More users means more compute, and more compute means more cost. So margins don’t expand unless compute per request falls. This earnings season will expose a simple divide: who can reduce compute cost