Essays and field notes on enterprise systems, operations, institutions, and technology adoption.
24 entries
Enterprise security has spent decades protecting credentials. Agentic AI shifts the problem from secrets at rest to authority in motion, forcing us to rethink identity, delegation, and execution in autonomous systems.
Alex Karp’s critique matters because it comes from inside the AI wave: tokens are not yield, usage is not value, and enterprise AI must be judged by the consequences it produces.
The queue was never just a process detail. It was the real operating model: a system for turning uncertainty into controlled human coordination.
As AI makes execution cheaper, direction matters more: what to build, whom to serve, and whether the problem is worth solving.
At the AI Summit, the sameness of AI products and marketing is hard to miss. Credibility will belong to companies with sharper, truer, more specific stories.
Markets are pricing AI on a future shaped by foundational model capability and rapid workflow innovation. As leaderboards continue to shift, the case for broad AI pessimism remains premature.
Companies can fall into an AI loop of modernization and cost savings. Breaking it requires wanting more: resilience, intelligence, and speed.
Distribution is no longer a durable moat. The next wave will win with modular, AI-native technology, outcome-based pricing, and domain-specific units of intelligence that augment higher-value work.
AI is making people-heavy operations less necessary. Indian IT firms must shift from billing for headcount and hours to pricing technology margins, outcomes, and capabilities that expand what clients can do.
Data centers are the new factories, and compute is becoming a foundation of sovereignty. India must build its own AI backbone or risk renting its digital destiny from others.
Cheaper code and faster delivery do not guarantee a useful product. Conviction grounded in customer understanding gives the work direction.
Most festivals celebrate the victors. Onam is.
AI did not erase research’s value; it removed the scapegoat. Gartner’s deeper challenge is a generation of builders acting on conviction, accepting failure, and refusing to outsource accountability.
As Microland turned 36, Nextronauts brought together hands-on exploration, discovery, and imagination—reminding us that curiosity and possibility are the real engines of progress.
AI hype can make the job market feel overwhelming. Build genuine skill, pursue valuable work, and stay grounded in fundamentals instead of chasing every shiny narrative.
AI-first operations integrate intelligence into existing workflows, combining human judgment, modular tools, and governance without requiring a wholesale replacement of the stack.
Platforms still matter, but the monopoly on workflow is fading. Enterprises are drifting toward smaller, sharper tools that fit the work.
Low-code can accelerate prototypes; it cannot repeal complexity. Real enterprise software still needs engineers who understand consequences.
The future of work may favor AI, resilience, and creativity, but those skills depend on the basics: reading, writing, mathematics, reasoning, and the ability to teach others.
The money in AI is not where the models are smartest. It is where the model gets embedded into a workflow someone will actually pay for.
An AI-first partner should change how work happens, not just sell clever demos. If it does not reshape the operating model, it is not AI-first.
AI becomes valuable when it stops being impressive and starts being useful. Cool demos do not pay bills.
