At a glance
- AI’s advance will leave few of us unaffected. It has been less than four years since the launch of ChatGPT, yet generative AI is already changing how people think and work—and where it goes from here is deeply uncertain. Will it improve our lives or take our jobs? Lead to unprecedented growth or upheaval? Who will benefit, and who might lose?
- Answering big questions about AI means seeing the whole picture, not just focusing on the technology. To help do that, we mapped the connections between AI and physical resources, institutions, businesses, markets, geopolitics, and people—all of which shape AI and change in response to it.
- Risks and bottlenecks arise because different parts of the AI system are moving at vastly different paces. AI capabilities are advancing exponentially by some measures. Progress in building data centers, power grids, chip factories, and mines is more linear. Redesigning how a business and its people work spans years. As one constraint eases, others can be exposed.
- In the AI era, leaders must therefore learn to manage at multiple speeds. Progress may slow when infrastructure or organizations cannot keep up, while risks can grow when adoption outruns safeguards, governance, or public acceptance. Sometimes the answer is to accelerate what is lagging; other times it may be to slow down. A systems view can help us navigate these choices.
AI may be the most consequential technology of our time. But how far will it spread, how quickly, and with what effects? The answers matter to almost everyone. Workers want to know whether predictions of mass job losses are credible or a scare story; one widely reported, admittedly extreme, forecast says AI could eliminate as many as half of entry-level white-collar jobs within five years. Investors want to know whether the enormous sums being spent on infrastructure will pay off—or leave behind the excess capacity and losses seen after earlier railway and dot-com booms. Policymakers ask how tightly to regulate AI’s risks without putting their economies at a disadvantage; China, the European Union, and the United States are following different paths. For CEOs and CTOs, the question is more immediate: We have rolled out the technology, so why aren’t we seeing it in our results?
None of these questions can be answered in isolation. They require analysis that cuts across disciplines and domains, just as the technology does. Whether jobs disappear depends on whether AI can do the tasks that make up a job as well and as cheaply as a person can, and whether new tasks emerge fast enough to replace the ones it takes over. Investment will pay off only if demand for AI services grows fast enough to fill the computing capacity being built for it—the chips, power, and data centers. Regulators face a trade-off: Move too slowly and risks may go unmanaged; move too quickly and lighter-touch rivals may pull ahead. And whether a company’s rollout produces results depends not only on the technology but also on its economics and, importantly, on whether the organization adapts to capture its full value.
To understand how these forces shape one another, we need to see the whole system and the connections among its parts. Drawing on our prior research and work with clients, we propose a general framework that connects factors often considered separately and reveals relationships that are otherwise easy to miss.
The paper begins by showing how AI’s components can fall out of step with one another and the bottlenecks and risks that follow when they do. It then brings those components together to show how they fit into a single system and how changes in one part can affect others. The framework is a starting point for thinking through the big questions AI raises: how rapidly improving capabilities may translate into higher productivity and economic growth, for example, or how infrastructure, organizational change, or public opinion may affect adoption. A systems view can help us understand the forces shaping AI’s path and where our choices can make a difference.
Chapter 1 looks at how fast different parts of the system are changing and what can happen when they fall out of sync. But first, explore the map below to get oriented (Exhibit 1). Click on any element for a closer look.
In the AI system, changes in one part can ripple through the rest.
Tap any part of the diagram to explore the system
Mismatched timelines: Bottlenecks, risks, and opportunities
General-purpose technologies can transform economies and improve lives, but realizing their full potential takes more than the invention itself. It requires complementary innovation, new infrastructure, new rules, capital, and people and organizations willing to change how they work. The pieces rarely arrive all at once, and the gap between a new capability and its full payoff has historically taken decades to close (Exhibit 2).
A five-column table comparing steam (1770s), electricity (1880s), computers (1970s), internet (1990s), and AI (2020s), linked by a timeline arrow across the top showing their order. Each column lists the technology’s new capability and the constraint that limited its spread — for example, steam’s mechanical power was hard to distribute, while AI’s constraints are physical capacity, organizational adaptation, trust, and governance — followed by a short list of innovations each one enabled. Two rows anchor the comparison: peak share of per capita GDP growth, and years taken to reach it. Steam reached 30% after 100 years; electricity, 10% after 40 years; computers and internet together, 50% after 25 years.
In the early decades of electrification, it was difficult simply to generate enough power to run factories and industrial machinery. Later, as that hurdle eased, the limits of the power grid became apparent. The big productivity gains arrived only once factories were physically redesigned around the electric motor, a step that came later still. The internet’s path was much the same: First fiber-optic networks had to be built, then new business models were needed, and finally companies had to reorganize around the technology.
Mismatches
AI faces a similar challenge, with many necessary parts coming together on vastly different timelines and with different shapes of change. Model capabilities are improving exponentially, doubling every few months by some recent measures. Physical infrastructure builds out more linearly, often taking years. Change at companies may occur more unevenly and can take longer, as businesses develop new products and ways of working.
When the parts move out of step, it can cause bottlenecks that slow down technological advancement or deployment—such as when demand for AI chips outpaced the ability to manufacture them in 2023, discussed below. For organizations, a period of rapid adoption can just as easily be followed by a slowdown or reversal if a product fails or causes unintended harm, or if deployment of the technology does not deliver productivity gains soon enough. At other times, a mismatch can create risks, such as when corporate governance or cybersecurity lags behind the adoption of powerful new tools.
Early in the generative-AI build-out, one of the first hurdles was access to frontier chips and computing power. As those bottlenecks eased, attention shifted to electricity supply and grid connections. The next constraints are more likely to involve applications, workforce skills, and organizational workflows than hardware (Exhibit 3).
Four side-by-side charts cover the same four foundational components introduced in Exhibit 1 — AI capabilities, R&D, physical resources, and institutional stance — each paired with a small illustration evoking its place in the original diagram. AI capabilities, measured as the length of tasks AI can complete with 50% reliability, grows exponentially, with recent models including Anthropic’s Claude Mythos preview and OpenAI’s GPT-5.4 pushing past 15 hours. R&D, global private investment in AI, climbs steadily. Physical resources, global installed data center capacity, grows more gradually. Institutional stance is represented by the share of people who trust companies to protect their personal data, holding flat at around 50%. Together, the four charts show capabilities accelerating faster than the other elements.
Bottlenecks
By 2023, only a year after OpenAI launched ChatGPT, demand for AI chips had outgrown the capacity to package them with the memory they require.
Nvidia, which designs most of the processors that frontier AI runs on, had built exactly the chip for the job, the H100. The problem was fusing it with memory in a single package, so that the hardware could work fast enough. Only one company could do this at the scale AI now demanded: TSMC, the Taiwanese firm that manufactures chips for most of the world’s major technology companies, including Nvidia and Apple. But in 2023, demand for that packaging outpaced even TSMC’s ability to keep up. Buyers reportedly faced lead times of up to 11 months for a finished H100-based server. The fix was more capacity, and TSMC built faster than it ever had.
Other hurdles emerged in a completely different part of the system: electricity. Building and connecting enough power capacity—a challenge for utilities and grid operators, not chipmakers—is another constraint, one familiar from the early era of electrification. A single data center can consume as much power as a small city, and when local grids cannot handle that much new demand, the utility has to build new power lines and substations before the facility can turn on—construction that requires its own permits and land, behind a long queue of other projects waiting for the same thing. Several hyperscalers, the handful of companies building AI at the largest scale, have run into this wall. On an October 2025 earnings call, Microsoft’s CFO told investors that the company’s cloud computing platform, Azure, which is used to handle AI workloads, had been short on operating capacity for several quarters. Electricity was the obstacle. Microsoft had AI chips it could not deploy because it lacked powered, ready-to-occupy data center space. The median new US power plant now takes about five years from interconnection request to commercial operation, up from less than two years in 2008.
Data center construction itself is now shaping up as a bottleneck, especially in the United States, which accounts for nearly half of global installed data center capacity. AI’s compute, power, and water costs per query have fallen as models get more efficient, but total demand is rising faster than efficiency gains or new supply can offset. Construction is expanding rapidly and still can’t keep pace. Many facilities are preleased even before they open.
In some places, local communities are pushing back. Concerns about rising electricity prices and large, unsightly facilities have triggered backlash, leading to permitting delays and, in some cases, construction freezes. At least 75 data center projects worth some $130 billion were blocked or delayed in the United States by community opposition in the first quarter of 2026 alone—matching all of 2025 in three months, according to Data Center Watch. In July, New York’s governor signed the first-ever statewide permitting freeze on large data centers.
Other potential constraints are emerging even as the data center drama plays out. Supplies of copper and other critical minerals needed for chips and wiring are showing signs of stress, but new mines and processing plants take years to permit and build. Unless current plans are accelerated, the world could face a sizable copper shortfall by the mid-2030s, according to the International Energy Agency. AI will be competing with many other industries for those materials. And physical resources are not the only constraint: Data center developers also report shortages of engineers and other workers, who take time to train.
AI’s capabilities, meanwhile, are advancing on an entirely different timescale. The complexity of tasks that AI can reliably handle—measured by how long the same tasks take a human expert—has doubled roughly every seven months since 2019, and roughly every four months since 2023. This is partly because AI increasingly contributes to the research that improves it; existing models can now write the code, generate the data, and run the evaluations used to train the next generation of models. Technologies available today could, in theory, automate more than half of current work hours in the United States, Europe, and Latin America.
So, the tools are here. But the reorganization is not. In a McKinsey survey of 1,719 respondents across 97 countries, conducted in May and June 2026, nearly nine in ten said their organizations regularly use AI in at least one business function. Yet outside a small group of AI high performers, representing 6 percent of the sample, only about one-quarter reported redesigning workflows around AI rather than inserting AI into existing ones.
Risks
Governance is also lagging. A separate McKinsey survey of about 500 organizations on AI trust and governance, conducted in December 2025 and January 2026, found that only about 30 percent had reached level three or higher on a four-level maturity scale. Strategy remains underdeveloped as well. Only 22 percent of operations leaders report a fully developed and implemented enterprise AI strategy, even though more than half name strategy as the single biggest driver of AI returns.
The problem usually stems from fragmentation. Responsibility for AI is split across IT, legal, data, HR, risk, and strategy, with no single owner. Day-to-day, oversight often defaults to the teams closest to the technology—IT or engineering—simply because no one else has claimed it. At the same time, 70 percent of global CEOs say they have the greatest influence on their company’s AI strategy. The gap between who actually runs AI and who is nominally accountable for it creates its own risk. Often, no one is positioned to answer basic questions about autonomous AI systems—which actions they may take, which decisions need human approval, who is accountable when something goes wrong.
Similarly, roughly one in three organizations still have no formal process to check AI systems for security risks before deploying them. Weak links may include smaller players, such as local governments, which may lack the resources for advanced security but remain connected to larger systems, creating vulnerabilities that extend beyond their own networks.
As adoption increases, so does the risk of cyberattack. More AI systems in more places give criminals more surface area to exploit. The technology is also becoming a more capable tool for hackers. In April 2026, the UK AI Security Institute (AISI) found that Anthropic’s Claude Mythos Preview could autonomously execute multi-step attacks on test networks that would take human professionals days of work. Just two years earlier, the best available models in AISI’s evaluations could barely complete beginner-level cyber tasks. The risks extend beyond businesses and governments to individuals. AI-generated phishing and fraud are harder to spot because AI can produce personalized, convincing messages at a scale no human scammer could match.
Just as with a large governance failure—that causes or allows major financial or physical harm—a single large AI-enabled cyberattack could trigger public backlash and stricter regulation that slows adoption well beyond the companies directly involved.
Opportunities
Major breakthroughs could also change several of these timelines at once. A leap in model architecture—or in an adjacent field like quantum computing—could loosen several of AI’s constraints at the same time, the way the invention of the transistor simultaneously advanced computing, communications, and electronics. The benefits could flow the other way, too. AI-driven breakthroughs in fields like energy or biotechnology could unlock innovation and growth well beyond the AI industry itself.
AI as a system: How the pieces fit together
Many of today’s hardest challenges and most important developments must be understood as complex systems. This is the case for AI. Its effects come from the interaction of many interdependent parts, not from any one part alone. Capability, R&D, physical resources, institutions, and adoption each shape the others (see sidebar, “Why a systems view matters”).
To make the system easier to visualize, we divide it into three analytical layers (Exhibit 4). Foundations shape what AI can do and the conditions under which it develops. Transmission is how people and organizations put AI’s capabilities to work. And outcomes are the effects that follow. The framework organizes the factors we think matter most; it is not a full inventory, and the categories overlap. Innovation, for example, is intertwined with new demand.
The value of a systems view
A systems view can help identify where action could have the greatest effect. For businesses, it can reveal what is holding back results; for investors, how opportunities and risks are shifting; for policymakers, where rules or public investment could help; and for individuals, which skills and opportunities may become more valuable.
For business leaders
- Look beyond efficiency for lasting advantage. AI can reduce costs and make existing work more productive, but competitors with access to the same technology may achieve similar gains, driving down prices and eroding any initial advantage. Long-term outperformance requires companies to combine AI with advantages that rivals cannot easily replicate, creating competitive moats. These can include proprietary data, scale, strong customer relationships, and access to scarce physical assets, combined in a business model that delivers greater value to both customers and suppliers.
- Build new things. The first uses of major new technologies are rarely the most consequential. Electricity began as a replacement for gas lighting before transforming industries and enabling entirely new forms of economic activity. AI may likewise give rise to new products, businesses, and markets that are difficult to anticipate today. Using the technology to automate existing work is a first step, but leaders should use the resources freed up to build new sources of growth. The potential impact extends beyond individual organizations: A small number of standout companies have driven a disproportionate share of economy-wide productivity growth, largely through shifts in strategy and moves into new businesses that reshape their industry segments, rather than by making existing businesses more efficient.
- Learn to learn faster. AI capabilities can improve exponentially, much faster than many companies can change their technology, workflows, or organization. New businesses may have an edge because they can build around AI from the start, without having to adapt or throw out legacy systems. But established companies have an advantage, too: their scale gives them more customers, workers, and operations across which to test new approaches, gather feedback, and learn what works. To make that advantage count, they need to build the capacity to adapt their systems and ways of working quickly enough to keep pace with evolving AI capabilities.
- Give people a reason to support change. Whether employees, customers, and communities see AI as making their lives better or worse will shape their willingness to accept and use it. Leaders therefore need to deliver benefits that people can see and experience rather than simply explaining what they are trying to achieve with the technology. If people experience better products, services, or ways of working, they may be more likely to support change; if they feel that the harms outweigh the benefits—that their jobs, livelihoods, and privacy are at risk—resistance may mount. A systems view shows that despite technological progress, businesses may struggle to create value if societal resistance is high.
- Develop a view of how AI could reshape your industry. AI could change who has an advantage, how companies compete, and where profits are made. It may lower barriers to entry, shift power among companies, customers, and suppliers, or change the advantages of scale. Leaders should consider how these forces could play out in their industry—and what they mean for strategy.
- Watch the whole system. AI’s trajectory will depend on more than improvements in the technology itself. Changes in adoption, infrastructure, investment, regulation, and economic outcomes can reinforce or constrain one another. The framework in this report identifies metrics for tracking these changes across the AI system.
For investors
- Follow the bottlenecks. Constraints can create investment opportunities because they generate demand for solutions. When computing power was scarce, opportunities emerged around chipmakers and frontier-model companies. As constraints shift toward electricity, infrastructure, and putting AI to productive use inside organizations, new opportunities may emerge around solving those problems.
- Expect different growth paths. Exponential growth in AI capabilities or usage will not translate into exponential growth across every business that uses or supplies the technology. Model capabilities can improve rapidly, while physical infrastructure expands much more slowly and companies may take years to translate AI into productivity and profits. Investors should be clear about which of these growth paths their assumptions depend on and not mistake rapid growth in one part of the system for rapid growth in another.
- Look past adoption. A company can adopt AI, even on a grand scale, without changing its long-term prospects. For signs of lasting advantage, investors should ask: Are productivity gains supporting growth rather than cost reduction alone? Are new products, businesses, and markets emerging rather than simply improvements to existing ones? Is the company overcoming legacy technology and organizational constraints? And is it shifting resources from the existing business toward new opportunities?
- Look for correlated risks. The performance of investments in the AI system may be highly correlated even when the companies involved operate in entirely different industries. Investments in chips, data centers, power, and AI software, for example, could all be affected by the same slowdown in AI demand or investment. Investors should account for these connections when assessing how diversified their exposures really are.
For policymakers
- Address system constraints. What holds AI back will differ by country or region and will change over time, from electricity and digital infrastructure to permitting, skills, and institutional capacity. Policymakers should identify the constraints that matter most in their economies and focus their efforts on relieving them. As the constraints change, policy should move with them.
- Promote competition. AI may lower the cost of creating new products and services. To help make that opportunity broadly available, policymakers should ensure that rules do not unnecessarily favor incumbents or make it harder for new entrants to compete and grow. They should also broaden access to the skills, tools, and capital people and companies need to build with AI.
- Make policy responsive. Regulators need to protect the public from harm. But AI capabilities and applications can evolve faster than laws and regulations. Not every new use or risk can be anticipated and some predicted risks may never materialize. Rules therefore need to be adjusted as evidence changes rather than trying to settle every question in advance. Requiring the industry to self-regulate is a good first step. Staying close to businesses, workers, researchers, and the public to monitor impacts across the full system can help policymakers see when more substantive regulatory adjustment is needed.
- Build trust and confidence. AI could support economic growth and improve living standards for decades, but realizing that potential may depend on public willingness to accept the changes it brings. Policymakers need to address legitimate concerns about AI’s potential effects on jobs, privacy, and safety while also making the case for what AI could make possible. As noted above, responsive policy can help build trust. Confidence also depends on whether people believe they can share in the economic opportunities AI creates. Proactive policies that expand access to skills, technology, and the resources needed to start and grow businesses can help.
- Foster investment. Decide which AI capabilities must be developed at home and where reliable access through partners is enough. Public investment, incentives, and partnerships can help strengthen computing infrastructure, energy, research, and talent without trying to replicate the entire AI ecosystem domestically. Some governments are already pursuing versions of this approach: the United Kingdom has established a Sovereign AI Fund, while the European Union’s InvestAI initiative aims to mobilize public and private investment in AI infrastructure.
For individuals
- Cultivate AI fluency. As AI becomes a bigger part of work and everyday life, people will need to become increasingly fluent in using the technology. AI fluency also means knowing when to use the technology and when to rely on human judgment. Skills such as creativity, empathy, adaptability, and critical thinking may become more valuable as the division of labor between people and AI changes. That boundary will continue to shift, requiring people to keep learning and adapting.
- Be thoughtful about what you share. AI tools can become more useful when people give them personal information as context, but that can also create risks. Before sharing sensitive financial, health, or personal information, consider where it could end up, who might gain access to it, and how it could be used.
- Expand your ambitions. AI gives individuals access to capabilities that once required specialized expertise, substantial resources, or large teams. This can change not just how people do things, but what they can realistically attempt: learning a new field, creating a product or service, starting a business, or pursuing an idea that previously might have been out of reach. AI can allow people to think bigger about what they might accomplish—and give them the ability to pursue those ambitions.
History suggests that the economic impact of new technologies may be arriving faster with each successive wave. Steam power took about a century from commercialization to its peak impact on economic growth, electricity about 40 years, and computers and the internet about 25. It is too early to know whether AI will compress that timeline further. But the possibility adds urgency because businesses, workers, and governments may have less time to adjust.
AI’s trajectory will not be determined by the technology alone. Models will improve, costs will fall, and new uses will emerge. But what follows will depend on how capabilities interact with physical resources, institutions, organizations, markets, and people—all moving at different speeds. A constraint in one part of the system can slow progress elsewhere; removing it can reveal another. And as AI spreads, the changes it produces will feed back into the system itself.
That is why the whole system matters. The task is not to predict a single AI future, but to understand how the pieces interact, where mismatches may emerge, and how the choices people make can alter what happens next.