Bill Gates' 2026 Warning: Mapping the AI S-Curve Against Bioterrorism and Job Disruption

Bill Gates has issued a stark warning: the most immediate threats from artificial intelligence are not distant sci-fi scenarios, but risks that could materialize sooner than society is ready for. In a recent blog post, he explicitly cautioned that AI risks, including bioterrorism and job disruption, will emerge sooner than many expect. This is not a call for panic, but a plea for deliberate action before the damage becomes unmanageable. He drew a direct parallel to the pre-COVID era, recalling his own 2015 TED Talk warning about pandemic preparedness. Five years later, the COVID-19 pandemic struck, a tragic validation of the cost of delayed response. Now, he urges the world not to repeat that mistake with AI.

The two-pronged threat he identifies is clear. First, there is the existential risk of misuse. Gates stated that an even greater risk than a pandemic today is the use of AI by bad actors to design a bioterrorism weapon. The technology to engineer pathogens is advancing rapidly, and AI accelerates that process exponentially. Second, and more immediately, is the economic friction of widespread automation. He noted that AI has already become good enough to cause disruption in areas such as software development, and he warned that the impact of AI on the job market will only grow over the next five years.

This framing is critical for investors. It separates the immediate, tangible risks from the longer-term paradigm shifts. The bioterrorism threat is a near-term systemic vulnerability, while the job market impact represents a five-year adoption curve of labor-saving AI tools. Both demand policy and corporate attention now. Yet, crucially, Gates' warnings do not negate the core S-curve of AI adoption for infrastructure and productivity. They highlight that the most disruptive phases-those causing societal friction and requiring governance-are arriving ahead of schedule. The setup for exponential gains in compute power and automation remains intact, but the path there is now seen through a sharper lens of near-term risk.

The Bioterrorism Risk: From Niche Tool to Strategic Threat

Bill Gates' warning that an even greater risk than a pandemic today is the use of AI by bad actors to design a bioterrorism weapon frames a critical near-term vulnerability. This is not a hypothetical future threat but a risk that is already being enabled by the technology's current capabilities. The parallel to his 2015 pandemic warning is deliberate: the tools for catastrophic misuse are advancing on a similar exponential curve to the benefits.

The evidence suggests this risk is not a distant sci-fi scenario but a present-day capability. Gates noted that AI has already become good enough to cause disruption in areas such as software development. This demonstrates the core problem: the same AI systems that automate coding tasks can also accelerate the design of biological agents. The barrier to entry for a malicious actor is collapsing. Open-source tools, which Gates specifically mentions, lower the technical expertise required, turning a niche scientific challenge into a potential strategic threat.

This risk demands proactive governance, as Gates emphasized. The recent regulatory pressure on companies like xAI to restrict harmful content generation is a first step in managing misuse. However, the scale of the bioterrorism threat is orders of magnitude greater than generating illicit images. It requires a new level of international cooperation and technical safeguards-think of it as building a firewall for the biological realm.

Yet, crucially, this threat is not expected to derail the core adoption S-curve for AI infrastructure and productivity. The risk is a systemic friction point, not a fundamental limit on the technology's progress. The S-curve of compute power and automation is driven by massive investments in hardware, software, and data centers, which continue unabated. The bioterrorism risk introduces a layer of regulatory and security overhead that could slow deployment in sensitive areas, but it does not flatten the underlying exponential growth in capabilities. The path forward is one of managed acceleration, where the rails for the next paradigm are laid even as we build the guardrails to prevent derailment.

Labor Market Disruption: The 5-Year Transition on the S-Curve

Bill Gates' warning of a five-year transition period for AI job disruption aligns with a key insight from the S-curve: the most painful phase is the inflection point itself, where adoption accelerates but the economy is still adjusting. The data suggests this transition will be marked by significant churn, but not a collapse in employment. Goldman Sachs Research estimates that the overall impact on unemployment will be modest, projecting a half-percentage-point rise in the jobless rate during the adjustment period. This is a temporary friction, not a permanent structural shift. The research notes that displacement tends to disappear after two years, echoing historical patterns where automation reallocates work rather than eliminating it.

The net effect is already visible in the job market. In 2024, the direct creation of AI roles far outpaced displacement. Estimates show more than 8,900 employees added to develop, train, and operate AI models. When combined with the construction boom for data centers-adding over 110,000 jobs-the technology generated about 119,900 direct jobs last year. This dwarfs the approximately 12,700 jobs lost due to AI, a figure that represents just a fraction of total layoffs. The narrative of AI as a net job destroyer is not matching the on-the-ground reality of last year.

More importantly, AI is changing the value equation for workers. PwC's analysis reveals that industries more exposed to AI are seeing 3x higher growth in revenue per employee. This suggests the technology is not just replacing labor, but making it more productive and valuable. The study also found that wage premiums for AI skills have risen, and that skill change is accelerating fastest in automatable jobs. Workers in roles like software development and customer service are being augmented, not merely replaced. The bottom line is a potential shift toward higher revenue per worker and faster adaptation, even in the most automatable positions.

Viewed through the S-curve lens, Gates' five-year warning captures the period of maximum friction. The inflection point is here, and it will cause churn. But the historical pattern and current data point to a resilient labor market that adapts. The risk is not mass unemployment, but a period of painful reallocation where policy and corporate training initiatives will be critical to smooth the transition. The exponential growth in productivity promised by AI is the long-term driver, and the economy has a track record of catching up.

Policy and Preparedness: Shaping the 2026 S-Curve

The policy landscape is now actively shaping the trajectory of the AI S-curve. A new federal executive order, issued in late December, aims to preempt a patchwork of state regulations and create a clearer, more innovation-friendly framework. This move is a direct attempt to accelerate the infrastructure build-out by removing what the administration calls "cumbersome regulation." The order explicitly targets state laws like Colorado's AI Act, framing them as barriers that create compliance challenges and, in some cases, force models to produce false results to avoid bias claims. By establishing an AI Litigation Task Force to challenge inconsistent state laws, the policy push is designed to streamline the path for companies scaling AI systems.

This regulatory clarity aligns with the exponential adoption curve. A minimally burdensome national standard could significantly reduce friction for startups and established firms alike, potentially accelerating the deployment rate of AI tools across industries. The order's focus on national security and economic dominance frames AI as a critical strategic asset in a global race. Section 1 states that U.S. leadership in AI will promote national and economic security and dominance across many domains. This reframes AI not just as a commercial product, but as essential infrastructure for future competitiveness.

The setup is one of managed acceleration. The policy is not a blank check; it introduces a new layer of federal oversight and litigation risk. Yet, for investors, the key signal is the direction of travel. The administration is betting that a unified national framework will foster faster innovation and investment, ultimately winning the strategic race. The coming months will test this bet, with the secretary of commerce's assessment of state AI regulations due in March and the FTC's policy statement on deceptive practices following soon after. If successful, this policy push could flatten the near-term regulatory S-curve, allowing the underlying exponential growth in compute power and automation to proceed with fewer guardrails. The bottom line is that preparedness now includes navigating a new federal-state dynamic, where the center of gravity is shifting toward a more permissive national standard.

Catalysts and Risks: The Path to Exponential Growth

The path to exponential AI growth is now defined by a race against complacency. The primary near-term risk is not a technical failure, but a societal one: missed deadlines for artificial general intelligence (AGI) can create a dangerous false sense of security. As Bill Gates warned, when people in the AI space predict that AGI or fully humanoid robots will come soon and then those deadlines are missed, it creates the impression that these things will never happen. This complacency leaves the world unprepared for the real, near-term threats that are already emerging.

The transition hinges on managing two critical frictions. First is labor market disruption. While the net job impact is expected to be modest and temporary, the adjustment period will be painful. Goldman Sachs estimates a half-percentage-point rise in the jobless rate during the shift, with displacement concentrated in roles like software development and customer service. The second friction is policy. The recent federal executive order aims to accelerate the infrastructure build-out by preempting state laws, but the implementation speed is uncertain. The policy push is designed to promote innovation, yet the regulatory landscape remains a patchwork, creating compliance overhead for companies scaling AI systems.

For investors, the forward-looking watchlist is clear. Watch for policy implementation speed and any bioterrorism incidents as leading indicators of risk materialization. The federal order references state laws like Colorado's as examples of "cumbersome regulation," but no timeline has been set for its full effect. The coming months, with the secretary of commerce's assessment due in March, will test whether this push for a unified national framework can indeed flatten the regulatory S-curve. Simultaneously, any incident involving AI-assisted biological weapon design would validate Gates' most urgent warning and likely trigger a swift, severe policy response that could slow deployment.

The bottom line is that exponential growth requires both catalysts and guardrails. The catalysts are the massive investments in compute and data centers, and the policy shift toward a more permissive national standard. The risks are the societal complacency that follows missed AGI timelines and the potential for catastrophic misuse. The path forward is one of managed acceleration, where the rails for the next paradigm are laid even as we build the guardrails to prevent derailment.