Greg Whalen has spent his career at the intersection of research, engineering, and large-scale technology operations, and that vantage point has given him a particularly sharp view of what is missing in today’s AI boom. As CTO of Prove AI, he is focused on a problem many companies know they have but continue to postpone: making generative AI systems observable, governable, and operationally trustworthy before they become business-critical.
Whalen’s background stretches from early AI research in healthcare to leadership roles at major global technology companies, including AWS, Experian, and Xendit. Across those environments, one issue kept resurfacing. Organizations could build predictive models, deploy sophisticated systems, and talk confidently about machine learning or generative AI, yet still struggle with a more basic reality: once these systems were in use, it was often difficult to understand how they behaved, why they failed, how they should be supported, and what accountability looked like when something went wrong.
That challenge is central to Prove AI’s mission. The company is tackling the infrastructure layer that sits beneath AI enthusiasm and above raw model capability. In Whalen’s view, the real bottleneck to broader enterprise adoption is not simply model performance. It is the absence of consistent visibility into how AI systems operate in the real world. Without that visibility, troubleshooting becomes slow, governance becomes vague, and scaling becomes risky.
A major part of that conversation centers on telemetry. In practical terms, telemetry is the stream of operational information that helps engineers understand what an AI system is doing and why. For generative AI, that means more than traditional logs. It includes the traces of user interactions, the context windows influencing outputs, the flow of requests through a system, and the pieces of information that shape outcomes at each step. When those signals are incomplete or fragmented, teams are forced into reactive detective work. Engineers lose time investigating symptoms instead of improving products.
Whalen sees that as one of the defining pains of modern AI development. Many systems appear impressively mature early in the process, especially with the help of coding assistants and fast-moving GenAI tooling. But that sense of completion can be deceptive. A product may look nearly ready while still lacking the operational scaffolding required to survive real usage at scale. In earlier generations of software, products often looked unfinished long after much of the hard backend work was already complete. Generative AI has inverted that pattern. Interfaces and demos can advance quickly, while supportability, observability, and remediation lag behind.
That inversion creates a dangerous gap between perception and readiness. Teams may feel pressure to productionize quickly because the product appears close to done. Yet once traffic increases and real user behavior begins to surface edge cases, hallucinations, broken flows, or unpredictable interactions, the absence of telemetry becomes painfully obvious. At that point, engineers are left supporting code they did not fully write, behavior they did not fully anticipate, and systems they did not instrument well enough.
Prove AI is designed to close that gap. Rather than treating governance and observability as afterthoughts, the company is positioning them as foundational to the AI lifecycle. Its approach reflects Whalen’s belief that the healthiest path is not to force organizations into a rigid framework, but to help them build the right practices faster and more effectively. Teams generally understand that they need better telemetry and better visibility. The issue is rarely awareness. The issue is competing priorities. With feature delivery under pressure, operational discipline is often pushed into the future.
That is where Prove AI’s strategy becomes distinctive. Instead of merely telling teams what they should do, the company aims to reduce the effort required to do it. By giving developers a head start through open-source tools and practical guidance, it meets them where they are. The idea is not to replace their ownership of the stack, but to accelerate their path to observability and give them a better footing for future troubleshooting and governance.
Whalen’s thinking also extends beyond engineering mechanics into the broader issue of AI governance. He sees governance becoming as essential to AI as cybersecurity and financial controls are to the rest of the business. That does not mean every company already has a clear regulatory mandate or a mature governance model. In fact, many do not. Some know they need to prepare but are unsure what exactly they are preparing for. Others are tempted to wait until rules become more explicit.
Whalen argues against that passive stance. His perspective is that companies should not wait for perfect external guidance before acting responsibly. Instead, leaders should approach AI ownership with the same common-sense discipline they would apply to any critical system. They should ask what customers would reasonably expect, what data practices would feel fair and transparent, what accountability should exist when automated decisions affect outcomes, and how they would defend their choices if questioned later. That mindset creates a more durable foundation than scrambling to react once governance becomes urgent.
This philosophy places a particular burden on technical leadership, especially CTOs. Whalen believes generative AI is disruptive enough that it cannot be managed solely through delegation or isolated centers of excellence. In past shifts, such as cloud adoption or enterprise analytics, it often made sense to create cross-functional programs, launch pilots, and let teams explore. GenAI, in his assessment, is different. It is changing the mechanics of software development itself, collapsing boundaries between building and operating systems, and altering the responsibilities of the people involved.
For that reason, he sees this as a moment when CTOs need to get closer to the work rather than farther from it. They need to understand the details, work directly with teams, and lead from inside the shift instead of from above it. Generative AI is not so technically opaque that leadership must remain hands-off, but it is disruptive enough that traditional oversight models may not move quickly or clearly enough. Whalen’s model is more grounded, more iterative, and more startup-like: start small, work closely, understand what is changing, and expand carefully.
That same flexibility shapes how he thinks about product strategy. One of the most important decisions at Prove AI has been resisting the urge to become overly prescriptive in a rapidly evolving market. In many startup environments, the common advice is to narrow the target, define the “right” way to solve a problem, and build a strongly opinionated product around that assumption. Whalen has taken a more adaptive stance. He has seen firsthand that different teams are building GenAI in very different ways, often for good reasons tied to their applications, workflows, and business needs.
Instead of assuming there is one correct AI stack or one correct operating model, Prove AI is embracing that diversity. That choice makes product development harder, but it also makes the company better suited to a market that is still changing underneath everyone. In Whalen’s view, locking too tightly into one approach too early would have created strategic fragility. In a fast-moving ecosystem, certainty about change can be more valuable than certainty about one rigid method.
Underneath all of this is a broader leadership lesson shaped by Whalen’s experience across research labs, enterprise platforms, and global engineering organizations. Different software environments call for different modes of development. Teams usually make process decisions for reasons that fit the problems they are trying to solve. Strong leadership means understanding those reasons rather than forcing ideology onto every situation. That lesson has become especially relevant in generative AI, where tools, workflows, and engineering patterns remain unusually fluid.
What makes Whalen’s perspective stand out is that it is not driven by hype. It is shaped by operational reality. He is less interested in AI as spectacle than in AI as a system that must be monitored, supported, explained, and improved under real-world conditions. That focus gives Prove AI a clear role in the current technology landscape. As enterprises rush to deploy chat interfaces, agentic systems, and AI-assisted workflows, the companies that succeed will not simply be the ones that build fastest. They will be the ones who understand what they have built, can see how it behaves, and are prepared to own the consequences.
For Whalen, that is the real ground floor of enterprise AI: not the launch, but the discipline that makes the launch sustainable.
*article and podcast From the Ground Up