14 posts tagged with "Data Strategy"
Palantir just gave a name to the thing I've been writing about for a year: tokenmaxxing. Enterprise AI rollouts are getting paused, token budgets capped, and leadership is finally asking what the spend actually buys. The story is breaking, and the wallet is what broke it.
Frontier models are genuinely better, and they still leak at the seams the moment an app has to scale. Dwarkesh Patel just named why: sample efficiency. The takeoff story everyone is selling assumes the AI inside the box will build its own way out, which is circular. A look at the data ceiling and what it means for anyone planning around imminent ASI.
The industry keeps promising AI will get cheap. The math doesn't support it, and leaders planning on cheap-AI as a future certainty are walking into a wall they can't see. Three structural reasons the cheap-AI forecast can't be defended.
Boardrooms are pricing engineering as a token line item. The math looks like obvious arbitrage — until you check what the spreadsheet is missing. Three holes in the assumption, with receipts.
Anthropic tested 16 frontier models and found that instructions alone don't prevent harmful behavior. The implications for every organization deploying AI agents are severe — and most aren't ready.
Token spend is the new cloud bill — except it's growing faster, harder to predict, and almost nobody has governance around it. Here's what the shift from compute-based to intelligence-based costs means for data leaders.
AI is making software nearly free to build. But the companies winning the next era aren't the ones shipping fastest — they're the ones customers trust most. Here's why the shift from SaaS to Outcome as a Service changes everything.
Everyone promises self-service analytics, but most implementations fail spectacularly. Here's why it doesn't work and the Snowflake + Tableau approach that actually delivered.
Four times as the first hire, I've built data teams from nothing to 50+ people. Here's the real playbook for hiring sequences, culture building, and avoiding the mistakes that kill growth.
Most data warehouses become dumping grounds for every table and pipeline. Here's how to recognize the signs, calculate the real cost, and execute a cleanup that sticks.
Skip the vendor marketing. Here's what actually matters when choosing between Snowflake and Databricks: your team's skills, your data patterns, your budget, and your existing stack.
Every company says data is strategic. Almost none of them fund it like it is. Here's what happens when you treat your data organization as a cost center — and how to fix it before the crisis forces your hand.
Everyone is focused on AI models getting smarter. Almost nobody is talking about the hardware crisis that could stop AI scaling dead in its tracks: overheating chips, melting data centers, and the death of Moore's Law.
Every enterprise is running AI pilots. Almost none are reaching production. Here are the 10 systemic traps that kill corporate AI initiatives — and what leaders can do about it.