Insights
Frameworks, analysis, and field notes from a team that has built at Carousell, Goldman Sachs, and Bain.
Most agentic AI pilots fail. The ones succeeding share a pattern: narrow scope, high value, and obsessive human-in-the-loop design.
You would never ship code without tests. So why are you shipping AI features without systematic evaluation?
Too many companies have an AI strategy that exists independently of their product strategy. That is not a strategy. It is an expense.
Everyone is racing to secure GPU capacity. Few are asking whether they are building the right infrastructure for what comes next.
The graveyard of enterprise AI is littered with successful pilots that never scaled. The gap between pilot and production is where value dies.
Companies treating the EU AI Act as a checkbox exercise will be outmaneuvered by those treating it as a strategic opportunity.
The best AI systems in 2025 do not rely on a single model. They route between models intelligently based on task, cost, and quality.
Your board thinks data strategy means dashboards and data lakes. The real strategy is about what data you collect and why.
Fewer legacy systems, faster regulation, and a government that treats AI as infrastructure. Dubai's advantages are structural.
Every consulting firm has an AI maturity model. Almost all of them measure activity instead of impact.
The old build vs. buy calculus assumed stable technology. In the AI era, the math changes every quarter.
Companies keep hiring ML researchers when they need AI product engineers. The talent mismatch is costing millions in wasted runway.
Bolting agents onto existing architectures will not work. The shift to agentic AI demands rethinking how software is structured.
Llama, Mistral, and the open source wave are surging in adoption. But adoption and value capture are two very different games.
Wrapping a GPT-4o API call in a React app does not make you an AI company. Product thinking is what separates tools from toys.
Most enterprise AI budgets are ballooning without a single executive willing to ask whether any of it is actually working.
The second half of 2025 will reward focused execution over broad experimentation. Here are the three bets that will separate winners from the rest.
Boards are getting skeptical. Budgets are tightening. The companies that thrive built substance while everyone else was building slides.
Betting on a single AI model or provider is the 2025 equivalent of betting on a single cloud. Diversify your architecture or pay for it later.
Customer satisfaction surveys say the AI chatbot is fine. Usage data says customers are abandoning it for email and phone. The data does not lie.
Most AI due diligence checks the model and team. It misses the data pipeline, evaluation framework, and technical debt. That is where risk lives.
Six months into 2025, the enterprise AI picture is clearer. More spending, harder problems, and a widening gap between shippers and piloters.
Series B startups have enough money to make expensive AI mistakes and not enough money to survive them. The margin for error is razor-thin.
The AI systems you shipped in 2024 are already accumulating debt that traditional engineering practices cannot detect or resolve.
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