The rapid ascent of AI agents in the enterprise signals a paradigm shift in business operations, but seasoned investors understand that true, sustainable growth hinges on trust. Drawing a compelling parallel to the meticulous, trust-first engineering of NASA’s Space Shuttle program, former aerospace engineer Adam Markowitz illustrates why building robust “trust operating systems” into AI from day one is not just a matter of safety, but a critical driver of long-term value and an unmissable investment opportunity in the burgeoning AI governance, risk, and compliance (GRC) sector.
The business world stands on the cusp of an artificial intelligence revolution, with estimates predicting that by 2027, as much as 50% of enterprises will deploy AI agents, and by 2030, these agents could perform up to 30% of all work. However, amidst this rapid adoption, a fundamental question emerges for investors: how do we ensure this transformative technology is built on a foundation of trust, much like the critical systems that power aerospace missions?
Adam Markowitz, a former aerospace engineer for NASA’s Space Shuttle program and current CEO of compliance automation platform Drata, argues that the principles of trust and safety ingrained in aerospace engineering are directly applicable to the responsible deployment and investment potential of AI. His insights offer a robust framework for understanding the long-term value creation in the AI sector.
Aerospace Engineering: A Precedent for Trust in High-Stakes Systems
Markowitz’s experience at NASA highlighted that trust wasn’t merely a desired outcome; it was an operational imperative. Every component, every line of code in the Space Shuttle had to be rigorously validated and tested. This unwavering commitment ensured astronauts returned safely, building profound public and internal trust in the entire program. He posits that the “move fast and break things” ethos prevalent in some tech circles is dangerously mismatched for AI, where stakes are equally high, albeit in a different domain.
For investors, this historical precedent is crucial. It suggests that companies prioritizing AI safety, security, and ethical deployment from the outset are laying the groundwork for more resilient and valuable long-term enterprises. Conversely, those that rush without adequate guardrails risk not only catastrophic failures but also a significant erosion of consumer and market trust, directly impacting their bottom line and stock performance.
Drata’s Ascent: Trust as a Business Enabler
Markowitz’s entrepreneurial journey after NASA further reinforces the economic power of trust. His first company, a platform for skill credentialing, struggled to gain traction with universities until it could prove impeccable security for sensitive student data. This experience was foundational for Drata, a company he co-founded to build a “trust layer” between businesses. By streamlining Governance, Risk, and Compliance (GRC) processes, Drata helps companies demonstrate their security posture to customers, partners, and auditors.
The results speak volumes: Drata rapidly scaled from $1 million to $100 million in annual recurring revenue in just a few years. This trajectory illustrates a critical shift in how businesses view GRC teams – not as cost centers, but as essential business enablers. Markowitz notes that security teams using Drata’s SafeBase Trust Center have influenced an impressive $18 billion in revenue, providing tangible evidence that a proactive trust strategy directly translates to significant financial returns and a competitive advantage in the market.
The Evolving Landscape of AI Compliance and Regulation
Current compliance frameworks like SOC 2, ISO 27001, and GDPR were primarily designed for data privacy and security, predating the rise of generative AI systems that make autonomous decisions. This gap presents both a challenge and an opportunity for innovators in the GRC space.
While regulators are beginning to catch up, as evidenced by California’s newly enacted AI safety standards, waiting for new rules is insufficient. Businesses cannot afford to delay implementing robust AI governance. For investors, this signals a burgeoning market for companies developing solutions to bridge this compliance gap, offering services that ensure AI systems are transparent, accountable, and secure within the existing, and future, regulatory environment.
The Imperative: A New AI Trust Operating System
Deploying AI without a profound understanding of its risks is akin to launching an untested rocket—the consequences can be catastrophic, leading to a loss of public trust and business integrity. To mitigate these risks and unlock AI’s full potential, Markowitz advocates for a new “trust operating system” based on three core pillars:
Transparent: Just as exhaustive documentation drives accountability in aerospace, AI systems need clear traceability. This means a verifiable chain from policy to control, evidence, and attestation. Investors should seek companies that prioritize audit trails and explainable AI.
Continuous: Trust cannot be a one-time checkbox. Like NASA’s continuous mission monitoring, AI trust programs must be ongoing. This involves continuous monitoring of controls, shifting audit readiness from a last-minute scramble to an inherent state of being. Companies offering real-time AI governance solutions present a strong investment case.
Autonomous: Modern rocket engines manage many operations autonomously. Similarly, as AI agents become ubiquitous, trust validation must also become autonomous. This ensures that humans, agents, and automated workflows can deterministically and unambiguously validate trust during their continuous interactions.
The Interconnected Future of Agentic AI and Investment Opportunity
Markowitz emphasizes the interdependence seen in aerospace—thousands of components, built by different teams, all functioning perfectly through mutual trust and documented decisions. This principle is directly transferable to the emerging era of “agentic AI,” where hundreds, and eventually thousands, of AI agents, human workers, and various systems will constantly interact, generating immense volumes of data and touchpoints.
The tools driving this future are powerful, and the opportunities are vast. However, the ability to earn and sustain trust in every single interaction will be the differentiator. Companies that successfully cultivate a culture of transparent, continuous, and autonomous trust will not only lead the next wave of innovation but also deliver exceptional long-term value to discerning investors. Building AI on trust is not just an ethical choice; it’s the foundation of future enterprise success.
