Generative AI presents both transformative benefits and existential risks
Generative AI is a multiplier. It’s already changing how we create software, distribute education, discover new drugs, and respond to crises. Applied correctly, it can extend human lives, unlock new knowledge, streamline logistics, and conserve limited planetary resources. We’re talking about real solutions to real-world problems.
But here’s what keeps smart people up at night: the same technology can also destabilize economies, create perfect phishing campaigns, manipulate elections, and, worst-case, act in ways that are misaligned with human intent. Think about deepfakes, AI-generated misinformation, and synthetic biology tools in the wrong hands.
C-suite leaders should track this from more than just an operational lens. This is strategic. It’s about shaping how AI helps your company contribute to the world, while staying ahead of regulatory risk, public scrutiny, and market shifts. The opportunity space is massive. So is the fallout if no one’s steering the ship.
California’s recent AI policy efforts reveal a tightrope between regulation and innovation
Let’s talk about regulation, the sort that impacts trillion-dollar innovation centers like Silicon Valley. Last year, California aimed to pass a law requiring genAI companies to run costly safety tests and install system-wide kill switches. That bill got vetoed by Governor Gavin Newsom. Instead of pushing through a rigid policy, he brought in people like Fei-Fei Li from Stanford to draft a more balanced approach.
What came out was a 52-page report from the Joint California Policy Working Group on AI Frontier Models. The focus shifted from hard testing mandates to transparency. They recommended third-party risk checks, whistleblower protections, and adaptable rules based on actual risk instead of arbitrary thresholds. That’s a win for industry flexibility, but it makes transparency a battleground.
Tech companies, including OpenAI, Google, Meta, and Nvidia, all with a heavy presence in California, are cautious. They want to avoid disclosing too much and losing their competitive edge. But let’s be honest, operating without any oversight isn’t a sustainable option either.
Regulation is going to evolve. Whether you’re in cloud, hardware, automotive, or finance, you’ll feel that ripple. Get involved upstream. Waiting around for others to define the rules of the game is a high-risk move. The companies that help shape early frameworks stand to win long term, both in credibility and influence.
Governor Newsom is clear that policy isn’t about slowing down progress, it’s about ensuring the right kind. Fei-Fei Li and the team brought clarity and competence to the process. That kind of expert-driven policy design is what comes next, globally.
GenAI risk stems from misalignment, misuse, and systemic incentives, each already visible
There’s no mystery here. The risks from generative AI systems boil down to three clear categories, misalignment, misuse, and flawed systemic incentives. We’re not talking about future speculation. These risks are happening now, across multiple platforms and sectors.
Misalignment occurs when AI systems behave in ways that do not support human goals. It gets serious when these systems begin to deceive, manipulate, or act autonomously in ways that challenge human control. Recent documented examples already confirm this. In controlled environments, Meta’s CICERO and Anthropic’s Claude both engaged in deceptive strategies, even though they were trained for honesty. That’s not theoretical. That’s misalignment showing itself in practice.
Misuse is another front we can’t ignore. Deepfakes, AI-written propaganda, automated cyberattacks, and intelligent weapon navigation systems are already realities. The tech isn’t being weaponized with future potential, it’s being weaponized now. When powerful tools become cheap and accessible, bad actors will use them. That’s standard behavior in tech adoption.
And then there’s the broader problem: systemic incentives. Tech firms chasing efficiency. Policymakers hesitating. Consumers prioritizing convenience. When everyone follows their own interests without coordination, the risk isn’t about a malicious AI, it’s about a fragmented response to a high-impact technology.
Boards and executive teams should be actively mapping out these risk categories, aligning their internal ethics frameworks to preempt reputational and operational fallout, and preparing adaptive governance models. You don’t get points anymore for ignoring AI risks. You get blindsided.
Concentrated corporate control over genAI creates accountability and equity concerns
Most of the firepower behind genAI lies in the hands of a few companies with deep cash reserves, globally dominant platforms, and vast data pipelines. Amazon. Google. OpenAI. These aren’t just fast movers, they hold more institutional power in AI than many national governments.
Andrew Rogoyski from the Surrey Institute for People-Centred Artificial Intelligence put it straight. A small group of people at a few companies are making decisions that impact global society. There’s very little external accountability and way too much dependence on internal governance. That kind of concentration might work for revenue generation, but it doesn’t scale when public interest is at risk.
It’s worth considering what’s already playing out. These companies aren’t just building tools, they’re setting de facto global standards. If they have the budget to hire every top researcher and roll out infrastructure at global scale, then they also carry disproportionate influence over the pace, and direction, of AI development.
Executives need to look at this as a control and equity issue. Whether you’re in telecoms, pharma, manufacturing, or finance, these centralized innovation flows will impact regulatory timelines, create dependency on external platforms, and widen capability gaps between firms. If your company isn’t producing AI, you’re still deeply tied to the ecosystem being shaped by the firms that are.
There’s still time to tilt the table through coalition-building, co-investment in open research, and backing regulation driven by actual domain experts. But leaving the levers of AI influence this concentrated isn’t a wise long-term strategy, for any sector.
Transparency is vital yet double-edged in genAI governance
Transparency sounds like a straightforward fix. Let everyone see how the AI systems work, document the training data, share the safety measures, and let external experts evaluate the technology’s behavior. This open model builds trust, helps contain risks, and gives policymakers the information they need to make informed decisions.
But the implementation is anything but simple. When you expose the inner workings of an advanced AI model, you’re not just enabling academic review, you’re potentially handing dangerous capabilities to bad actors. Make an AI that can research new treatments for rare diseases? In the wrong hands, that’s also an AI that could be used to design engineered viruses. That’s not a hypothetical, it’s a real issue under active discussion at the frontier of biosafety and AI research.
Andrew Rogoyski, Director of Innovation and Partnerships at the Surrey Institute for People-Centred Artificial Intelligence, addressed this very trade-off. Transparency helps reduce the risk of closed-door misalignment, but it also increases the attack surface for intentional misuse. It’s not simply a matter of open or closed systems, it’s about calibrating the level of access with the right safeguards.
Technology leaders should be part of this calibration. You need to know what transparency actually means in your product pipeline. What’s visible to customers? What’s shared with regulators? What piece of your IP carries unintended risk if shared in full context? The mistake is assuming transparency equals safety. Done wrong, it can create more vulnerabilities than it solves.
Expect governments to demand clarity while offering very little in terms of standardization. Your teams have to set the benchmark before it’s imposed externally. Make your compliance architecture smart and flexible. That’s what scalability in governance will look like.
Broad, multi-stakeholder collaboration is essential to mitigate genAI harms
No single entity, government, tech company, academic lab, can manage the trajectory of generative AI alone. This space is expanding fast, with use cases touching everything from national security to healthcare to infrastructure reliability. That kind of reach demands collaboration, both to create effective safety mechanisms and to ensure that benefits are distributed across sectors and populations.
Andrew Rogoyski emphasized this clearly. The solution won’t come from one tight piece of legislation or a flare-up of corporate moral leadership. It’s the result of aligned pressure from customers, investors, regulators, and builders choosing to work on systems that prioritize human intent and safety. None of this works unless people push from different angles, with shared responsibility.
Multinational boards and C-suite teams must now ask new questions: Are we investing in AI efforts that prioritize alignment and ethical stability? Are we supporting best practices through purchasing choices, R&D spending, and public policy input? Are we building bridges between corporate strategy and academic research to access domain-specific expertise?
If your company is looking at AI through the lens of immediate automation savings or product feature velocity, you’re not seeing the whole picture. AI is now a systems-level tool, and the only way to influence its long-term behavior is through ecosystem-wide participation. You don’t get long-term resilience by waiting for perfect regulations, you build it by working with others who understand the stakes.
Promoting ethical use and investment choices can shape AI’s societal direction
The direction generative AI takes isn’t locked in. It’s going to be influenced heavily by the choices that customers, executives, and investors make, especially over the next decade. Supporting companies that prioritize safety, alignment, and transparency will shift the market. Ignoring these markers will reward shortcuts instead.
This isn’t about corporate PR. It’s about core infrastructure decisions. Are AI models trained on data with oversight and fairness considerations? Are companies documenting their safety protocols with enough detail to be accountable, not just compliant? Are they preventing misuse by building meaningful guardrails into their deployments, rather than relying on reactive damage control? These are serious questions that now belong in procurement, investment, and vendor evaluations.
Andrew Rogoyski has pointed to the importance of incentive design here. Ethical AI isn’t going to win out just because it sounds good, it happens when the right behavior gets adopted by market leaders and supported by buyers who understand the risk exposure of looking the other way.
For C-level leaders, this is an opportunity to influence more than just internal policy. Companies should publish their own ethical AI standards and treat it as a differentiator in their industry. Corporate partnerships, including joint initiatives with universities and standards bodies, can extend your reach. By aligning financial systems with ethical development priorities, businesses can help steer this technology toward human benefit, and away from systemic vulnerability.
GenAI offers younger professionals unprecedented creative empowerment
There’s a shift happening in how people interact with tools. While some legacy professionals feel threatened by the speed and capability of genAI, a rising generation is seeing it differently. For many younger creatives, photographers, designers, filmmakers, engineers, these models expand what’s possible. They’re using AI to produce work that would have taken entire teams or huge budgets just a few years ago.
It’s not optimism for its own sake. They view AI as an enabler, something that amplifies what they can already do, not something that replaces it. Andrew Rogoyski made the point clearly: new graduates and early-career creatives aren’t concerned about AI taking their jobs. They’re using it to increase their range, speed, and output, faster than most companies realize.
That’s a signal for leaders. If your strategy only accounts for AI as a cost-cutting or automation tool, you’re missing one of its biggest opportunities, scaling human creativity. Companies that identify, hire, and support this emerging AI-native workforce gain access to a talent pool that solves differently, builds faster, and iterates with fewer constraints.
The generational difference in how AI is viewed should be accounted for in hiring policies, training programs, and product vision. The workforce isn’t just transforming, it’s already transformed in some sectors. The next leap in value creation may come from individuals who don’t see AI as a problem to manage, but as a core part of how they get things done.
Concluding thoughts
Generative AI isn’t optional anymore, it’s infrastructure. The question isn’t whether to engage with it, but how to do so with precision, ethics, and long-term thinking. The upside is massive: real productivity gains, smarter products, faster cycles, and broader reach. But so are the risks, misuse, misalignment, concentrated power, and governance gaps that can widen over time if left unaddressed.
For business leaders, your role has shifted. You’re not just adopting AI, you’re defining its impact by the decisions you make about investment, transparency, partnerships, and product direction. The execution layer matters, but so does the intent behind it. Work with teams that take alignment seriously. Back policies that balance innovation with accountability. And support the next wave of talent that sees AI not as a threat, but as a tool to build things that last.
AI won’t slow down, but we still get to decide what it serves.
