Prashant Jalan is the founder of Guickly and previously led applied AI teams in the mapping technology industry.
At Google, I spent years building XProf, the performance profiler that traces how deep learning models actually run on TPU hardware. Its job was to answer one question with total precision: where did every cycle and every byte go? It identified which operations consumed the most compute, which memory transfers stalled execution and how close each workload came to the hardware’s theoretical limits. Serious engineering was invested in that visibility for a simple reason. At the scale of billions of users, even small inefficiencies become enormous costs. You cannot optimize what you cannot measure.
Once I left Google and started talking to companies adopting AI, I found the same physics with none of the instrumentation. The workloads had changed from ML models to AI tools and agents. The hardware had changed from TPUs to entire enterprises. The audience had changed from developers to CXOs. But the question was identical, and almost nobody could answer it: Where is it all actually going, and what is it returning? That contrast is the reason I now build in this space.
A Trillion Dollars, Flying On Instinct
Worldwide AI spending is forecast to reach $2.59 trillion in 2026, up 47% in one year. Even then, McKinsey finds only 39% of organizations can attribute any bottom-line impact to AI, most of them under 5%, and MIT reports that 95% of enterprise AI pilots show no measurable profit-and-loss impact. Spending is compounding, yet the ability to explain its business impact remains remarkably limited.
Consider how strange this is historically. Every major technology wave developed its measurement layer early. The web quickly gained analytics. Cloud spending gave rise to FinOps as costs became material. SaaS spawned spend management as application portfolios exploded. AI is growing faster than any of these waves, yet its enterprise measurement layer remains fragmented and immature. We are undertaking one of the largest capital reallocations in enterprise technology without a consistent way to measure whether those investments are actually creating value.
The Symptom: Consumption Dressed As Progress
You can see the gap in how companies talk about AI success. The proof offered is almost always a consumption chart: usage up, adoption up and tokens up. Token consumption is often the easiest thing to measure, so it becomes the metric everyone reports. It is the AI era’s version of counting lines of code, an input metric standing in for an outcome nobody has instrumented.
The incentive behind the tools makes it worse. Frontier models are priced per token, and researchers studying this pricing structure find it rewards volume over value, a pattern they call quantity inflation. When the meter also determines the vendor’s revenue, there is little incentive for the meter itself to encourage efficiency.
The Accelerant: Software That Acts On Its Own
Autonomous agents further accelerate this problem. The first shift is financial. A person using an AI tool spends in small increments, one prompt at a time. An agent spends at machine speed, retrying, looping and chaining calls without supervision on the AI meter. For the first time, companies are deploying software with running cost that is decided by the software itself.
The second shift is behavioral. Due to this, the visibility matters beyond the AI bills. Anthropic stress-tested 16 frontier models in simulated corporate environments. Agents had email access and autonomy, and it found that models from major labs sometimes chose harmful insider behaviors when they sensed their goals were threatened. This happened even when agents were directly instructed not to engage.
These were controlled simulations, but the practical lesson is clear: instructions alone do not govern autonomous systems, and it needs deeper oversight. Complete AI visibility is the first step to oversight.
Visibility, Then Control, Then Optimization
Profiling taught me that visibility, control and optimization happen in that order. XProf never made a model faster by itself. It made the wastage visible, and once engineers could see exactly where the cycle went, the optimization was often straightforward.
The enterprise version for AI is no different. It begins with visibility: every AI tool, model and agent in use, whether officially approved or not, and what each one costs. From there comes control, applying budgets, policies and guardrails to what is actually running. Only then does optimization become meaningful, because you can finally connect spending to behavior and outcomes.
Most organizations attempt this in reverse. They write AI policies before knowing the full inventory, and optimize model choice before they can attribute an outcome. It fails for the same reason tuning a model without a profile fails. You end up optimizing the part you can see, which is rarely the part that matters the most.
The Measurement Layer Gets Built Either Way
Every previous wave tells the same story. The measurement layer always arrives, and the only question is whether a company builds the discipline before or after the waste compounds. My bet, and the conviction I left Google to pursue, is that AI’s measurement layer becomes as fundamental as analytics was to the web and FinOps was to the cloud, and this will arrive much faster than our anticipation because AI usage is exploding unlike anything we have ever seen.
The companies that will succeed in the next phase of AI will not be the ones that consumed the most. I believe they will be the ones that could explain the impact AI has brought and control what it did.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?