The AI bubble rewards talk more than results
Schools should pilot, verify, and buy only proven gains using LRAS and total-cost checks
Train teachers, price energy and privacy, and pay only for results that replicate

A single number should make us pause: 287. That's how many S&P 500 earnings calls in one quarter mentioned AI, the highest in a decade and more than double the five-year average. However, analysts note that for most companies, profits directly linked to AI are rare. This situation highlights a classic sign of an AI bubble. Education is proper in the middle of it. Districts are getting pitches that reflect market excitement. If stock prices can rise based on just words, so can school budgets. We cannot let talk replace real change. The AI bubble must not become our spending plan. The first rule is simple: talk does not equal transformation; improved learning outcomes do. The second is urgent: establish strict criteria before making large expenditures. If we make a mistake, we risk sacrificing valuable resources for headlines and later face parents with explanations for why the results never materialized. The AI bubble is real, and schools must avoid inflating it. Setting high standards for AI adoption is crucial, and it's our commitment to excellence and quality that will guide us in this journey.

The AI bubble intersects with the classroom

We need to rethink the discussion around incentives—markets reward mentions of AI. Schools might imitate this behavior, focusing on flashy announcements instead of steady progress. It's easy to find evidence of hype. FactSet shows a record number of AI references on earnings calls. The Financial Times and other sources report that many firms still struggle to clearly articulate the benefits in their filings, despite rising capital spending. At the same time, the demand for power in AI data centers is expected to more than double by 2030, with the IEA estimating global data-center electricity use to approach 945 TWh by the end of the decade. These are the real costs of pursuing uncertain benefits. When budgets tighten, education is often the first to cut long-term investments, such as teacher development and student support, in favor of short-term solutions. That is the bubble's logic. It rewards talk while postponing proof.

But schools are not standing still. In the United States, the number of districts training teachers to use AI nearly doubled in a year, from 23% to 48%. However, the use among teachers is still uneven. Only about one in four teachers reported using AI tools for planning or instruction in the 2023-24 school year. In the UK, the Department for Education acknowledges the potential of AI but warns that evidence is still developing. Adoption must ensure safety, reliability, and teacher support. UNESCO's global guidance offers a broader perspective: proceed cautiously, involve human judgment, protect privacy, and demand proof of effectiveness. This approach is appropriate for a bubble. Strengthen teacher capacity and establish clear boundaries before scaling up purchases. Do not let vendor presentations replace classroom trials. Do not invest in "AI alignment" if it doesn't align with your curriculum. Thorough evaluation is key before scaling up AI investments, and it's our responsibility to ensure it's done diligently.

The macro signals send mixed messages. On the one hand, investors are pouring money into infrastructure, while the press speculates about a potential AI bubble bursting. On the other hand, careful studies report productivity gains under the right conditions. A significant field experiment involving 758 BCG consultants found that access to GPT-4 improved output and quality for tasks within the model's capabilities, but performance declined on tasks beyond its capabilities. MIT and other teams report faster, better writing on mid-level tasks; GitHub states that completion times with Copilot are 55% faster in controlled tests. Education must navigate both truths. Gains are real when tasks fit the tool and the training is robust. Serious risks arise if errors go unchecked or when the task is inappropriate. The bubble grows when we generalize from narrow successes to broad changes without verifying whether the tasks align with schoolwork.

From hype to hard metrics: measuring the AI bubble's learning ROI

The main policy mistake is treating AI like a trend rather than a learning tool. We should approach it in the same way we do any educational resource. First, define the learning return on AI investment (LRAS) as the expected learning gains or verified teacher hours saved per euro, minus the costs of training and integration. Keep it straightforward. Imagine a district is considering a €30 monthly license per teacher. If the tool reliably saves three teacher hours each week and the loaded hourly cost is €25, the time savings alone amount to €300 per teacher per term. This looks promising—if it's validated within your context, rather than based on vendor case studies. Measurement method: track time saved with basic time-motion logs and random spot checks; compare with student outcomes where relevant; adjust self-reports by 25% to account for optimism bias.

This approach also applies to student learning. A growing body of literature suggests that well-structured AI tutors can enhance outcomes. Brookings highlights randomized studies showing that AI support led to doubled learning gains compared to strong classroom models; other trials indicate that large language model assistants help novice tutors provide better math instruction. However, the landscape is uneven. The BCG field experiment cautions that performance declines when tasks exceed the model's strengths. In a school context, utilize AI for drafting rubrics, generating diverse practice problems, and identifying misunderstandings; however, verify every aspect related to grading and core content. Require specific outcome measures for pilot programs—such as effect sizes on unit tests or reductions in regrade requests—and only scale up if the improvements are consistent across schools.

Now consider the system costs. Data centers consume power; power costs money. The IEA forecasts that global data-center electricity use could more than double by 2030, with AI driving much of this growth. Local impacts are significant. Suppose your region faces energy limitations or rising costs. In that case, AI services might come with hidden "energy taxes" reflected in subscription fees. The Uptime Institute reports that operators are already encountering power limitations and rising costs due to the demand for AI. A district that commits to multi-year contracts during an excitement phase could lock in higher prices just as the market settles.

Finally, compare market signals with what's happening on the ground. FactSet indicates a record-high number of AI mentions; Goldman Sachs notes a limited direct profit impact so far; The Guardian raises questions about the dynamics of the bubble. In education, HolonIQ is tracking a decline in ed-tech venture funding in 2024, the lowest since 2014, despite an increase in AI discussions. This disparity illustrates a clear point. Talk is inexpensive; solid evidence is costly. If investment follows the loudest trends while schools chase the noisiest demos, we deepen the mistake. A better approach is to conduct narrow pilots, evaluate quickly, and scale carefully.

A better approach than riding the AI bubble

Prioritize outcomes in procurement. Use request-for-proposal templates that require vendors to clearly define the outcome they aim to achieve, specify the unit of measurement they will use, and outline the timeline they will follow. Implement a step-by-step rollout across schools: some classrooms utilize the tool while others serve as controls, then rotate. Keep the test short, transparent, and equitable. Insist that vendors provide raw, verifiable data and accept external evaluations. Consider dashboards as evidence only if they align with independently verified metrics. This is not red tape; it's protection against hype. UK policy experiments are shifting towards this approach, emphasizing a stronger evidence base and guidelines that prioritize safety and reliability. UNESCO's guidance is explicit: human-centered, rights-based, evidence-driven. Include that in every contract.

Prepare teachers before expanding tool usage. RAND surveys indicate forward movement alongside gaps. Districts have doubled their training rates year over year, but teacher use remains uneven, and many schools lack clear policies. The solution is practical. Provide short, scenario-based workshops linked to essential routines, including planning, feedback, retrieval practice, and formative assessments. Connect each scenario to what AI excels at, what it struggles with, and what human intervention is necessary. Use insights from the BCG framework: workers performed best with coaching, guidelines, and explicit prompts. Include a "do not do this" list on the same page. Then align incentives. Acknowledge teams that achieve measurable improvements and simplify their templates for others to follow.

Address energy and privacy concerns from the outset. Require vendors to disclose their data retention practices, training usage, and model development; select options that allow for local or regional processing and provide clear procedures for data deletion. Include energy-related costs in your total cost of ownership, because the IEA and others anticipate surging demand for data centers, and operators are already reporting energy constraints. This risk might manifest as higher costs or service limitations. Procurement should factor this in. For schools with limited bandwidth or unreliable power, offline-first tools and edge computing can be more reliable than always-online chatbots. If a tool needs live connections and heavy computing, prepare fallback lessons in advance.

A steady transformation

Anticipate the main critique. Some may argue we're underestimating the potential benefits of AI and that it could enhance productivity growth across the economy. The OECD's 2024 analysis estimates AI could raise aggregate TFP by 0.25-0.6 percentage points a year in the coming years, with labor productivity gains being somewhat higher. This is not bubble talk; it represents real potential. Our response is not to slow down unnecessarily but to speed up in evaluating what works. When reliable evidence emerges—such as an AI assistant that consistently reduces grading time by a third without increasing errors, or a tutor that achieves a 0.2-0.3 effect size over a term—we should adopt it, support it, and protect the time it saves. We aim for acceleration, not stagnation.

A second critique suggests schools fall behind if they wait for perfect evidence. That is true, but it doesn't represent our proposal. The approach is to pilot, validate, and then expand. A four-week stepped-wedge trial doesn't indicate paralysis; it shows momentum while retaining lessons learned. It reveals where the frontier lies in our own context. The findings on the "jagged frontier" illustrate why this is crucial: outputs improve when tasks align with the tool, and fall short when they don't. The more quickly we identify what works for each subject and grade, the more rapidly we can expand successes and eliminate failures. This is how we prevent investing in speed without direction.

A third critique may assert that the market will resolve these issues. That is wishful thinking within a bubble. In public services, the costs of mistakes are shared, and the benefits are localized. If markets reward mentions of AI regardless of the outcome, schools must do the opposite. Reward outcomes, irrespective of how much they are discussed. Ed-tech funding trends have already decreased since the peak in 2021, even as conversations about AI grow louder. This discrepancy serves as a warning. Build capacity now. Train teachers now. Create contracts that compensate only for measured improvements—design effective and impactful audits that drive meaningful change. The bubble may either burst or mature. In either case, schools that focus on outcomes will be fine. Those who do not will be left with bills and no visible gains.

Let's return to the initial number. Two hundred eighty-seven companies discussed AI in one quarter. Talk is effortless. Education requires genuine effort. The goal is to convert tools into time and time into learning. This means we must set high standards while keeping it straightforward: establish clear outcomes, conduct short trials, ensure accessible data, provide teacher training, and account for total costs, including energy and privacy considerations. We must align the jagged frontier with classroom tasks and resist broad claims. We need to build systems that develop slowly but scale quickly when proof arrives. The AI bubble invites us to purchase confidence. Our students need fundamental skills.

So, we change how we buy. We invest in results. We connect teachers with tools that demonstrate value. We do not hinder experimentation, but we are strict about what we retain. If the market values words, schools must prioritize evidence. The measure of our AI decisions will not be the number of mentions in reports or speeches. It will be the quiet improvement in a student's skills, the extra minutes a teacher gains back, and the budget allocations that support learning. Talk is not transformation. Let's transform the only thing we invest in.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

Aldasoro, I., Doerr, S., Gambacorta, L., & Rees, D. (2024). The impact of artificial intelligence on output and inflation. Bank for International Settlements/ECB Research Network.
Boston Consulting Group (2024). GenAI increases productivity & expands capabilities. BCG Henderson Institute.
Business Insider (2025). Everybody's talking about AI, but Goldman Sachs says it's still not showing up in companies' bottom lines.
Carbon Brief (2025). AI: Five charts that put data-centre energy use and emissions into context.
FactSet (2025). Highest number of S&P 500 earnings calls citing "AI" over the past 10 years.
GitHub (2022/2024). Measuring the impact of GitHub Copilot.
Guardian (2025). Is the AI bubble about to burst – and send the stock market into freefall?
HolonIQ (2025). 2025 Global Education Outlook.
IEA (2025). Energy and AI: Energy demand from AI; AI is set to drive surging electricity demand from data centres.
MIT Economics / Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative AI (Science; working paper).
OECD (2024). Miracle or myth? Assessing the macroeconomic productivity gains from AI.
RAND (2025). Uneven adoption of AI tools among U.S. teachers; More districts are training teachers on AI.
UK Department for Education (2025). Generative AI in education: Guidance.
UNESCO (2023, updated 2025). Guidance for generative AI in education and research.
Uptime Institute (2025). Global Data Center Survey 2025 (executive summaries and coverage).
Harvard Business School / BCG (2023). Dell’Acqua, F., et al. Navigating the jagged technological frontier: Field experimental evidence… (working paper).

AI energy use is rising, but efficiency per task is collapsing
Education improves outcomes by optimizing energy usage and focusing on small models.Do this, and costs and emissions fall while learning quality holds

The key figure in today's discussion about AI and the grid isn't a terawatt-hour forecast but 0.4 joules per token, a number that reframes AI energy efficiency in education. This is the energy cost that NVIDIA now reports for advanced inference on its latest accelerator stack. According to the company's own long-term data, this shows about a 100,000-fold efficiency improvement in large-model inference over the past decade. However, looking at this number alone can be misleading. Total electricity demand from data centers is still expected to rise sharply in the United States, China, and Europe as AI continues to grow. But it changes the perspective. If energy usage per unit of practical AI work is decreasing, then total demand is not fixed; it's something that can be influenced by policy. Education systems, which are major consumers of edtech, cloud services, and campus computing, can establish rules and incentives that transform quick efficiency gains into reduced bills, lower emissions, and improved learning outcomes. The decision isn't about choosing between growth and restraint; it's about managing growth versus prioritizing efficiency in a way that also increases access to resources.

AI Energy Efficiency in Education: From More Power to Better Power

The common belief is that AI will burden the grids and increase emissions. Critical analyses warn that, without changes to current policies, AI-driven electricity use could significantly increase global greenhouse gas emissions through 2030. Models at the regional level forecast significant increases in data center energy use, with roughly 240 TWh in the United States, 175 TWh in China, and 45 TWh in Europe, compared to 2024 levels, by the end of the decade. These numbers are concerning and highlight the need for investment in generation, transmission, and storage. Yet these assessments also acknowledge considerable uncertainty, much of which relates to efficiency. This includes how quickly computing power per watt improves, how widely those improvements spread, and how much software and operational practices can reduce energy use per task. The risk is real, but the slope of the curve is not set in stone.

The technical case for a flatter curve is increasingly evident. Mixture-of-Experts (MoE) architectures now utilize only a small portion of parameters for each token, thereby decreasing the number of floating-point operations (flops) without compromising quality. A notable example processes tokens by activating approximately 37 billion out of 671 billion parameters, which significantly reduces computing needs per token, supported by distillation that transfers reasoning skills from larger models to smaller ones for everyday tasks. At the system level, techniques such as speculative decoding, KV-cache reuse, quantization to 4–8 bits, and improved batch scheduling all further reduce energy use per request. On the hardware front, the transition from previous GPU generations to Blackwell-class accelerators delivers significant speed gains while using far fewer joules per token. Internal benchmarks indicate substantial improvements in inference speed, accompanied by only moderate increases in total power.

Additionally, major cloud providers now report fleet-wide Power Usage Effectiveness (PUE) of nearly 1.1, which means that most extra energy use beyond chips and memory has already been minimized. Collectively, this represents an ongoing optimization process—from algorithms to silicon to cooling systems—that continues to drive down energy usage per beneficial outcome. Policy can determine whether these savings are realized.

AI Energy Efficiency in Education: What It Means for Classrooms and Campuses

Education budgets are feeling the impact of AI, with expenses including cloud bills, device updates, and hidden costs such as latency and downtime. An efficiency-first approach can make these bills smaller and more predictable while increasing access to AI support, feedback, and research tools. The first step is to establish procurement metrics that monitor energy per unit of learning value. Instead of simply purchasing "AI capacity," ministries and universities should aim to buy tokens per watt or joules per graded essay, with vendors required to specify model details, precision, and routing strategies. When privacy and timing allow, it's best to default to smaller distilled models for routine tasks like summaries, grammar checks, and feedback aligned with rubrics, saving larger models for specific needs. This won't compromise quality; it reflects how MoE systems function internally. With effective routing, a campus can handle 80–90% of requests with smaller models and switch to larger ones only when necessary, dramatically reducing energy use while maintaining quality where needed. A simple calculation using the published energy figures for new accelerators shows that moving a million-token daily workload from a 5 J/token baseline to 0.5 J/token—through distillation, quantization, and hardware upgrades—could save about 4.5 MWh per day before considering PUE adjustments. Even at an already efficient ~1.1 PUE, this represents significant budget relief and measurable reductions in carbon emissions.

Secondly, workload management should be included in edtech implementation guides. Many uses of generative AI in education occur asynchronously—such as grading batches, generating prompts, and cleaning datasets—so grouping tasks and scheduling them during off-peak times can reduce the load without affecting users. Retrieval-augmented generation (RAG) reduces token counts by incorporating relevant snippets, rather than requiring models to derive responses from scratch. Speculative decoding enables a lighter model to generate tokens, which a heavier model then verifies, thereby boosting throughput while reducing energy use per output. Caching prevents the need to repeat system prompts and instructions across different groups. None of these requires the latest models; they need contracts that demand efficiency. Partnering with cloud providers that have best-in-class PUE and ensuring campuses only use on-prem servers when necessary can turn technical efficiency into policy efficiency: lowering total energy while achieving the same or better learning outcomes.

Bending the curve, not the mission

Critics may raise the concern of rebound effects: if we cut the energy required for an AI query by 10 times, won't usage rise by 10 times, negating the savings? Sometimes yes. But rebound is not an absolute rule, especially when buyers enforce limits. Public education can establish budget-based guidelines, such as caps on tokens per student aligned with educational objectives, and a tiered model routing that only escalates when the value demands it. Just as printers evolved from unmanaged to managed queues, AI requests can operate under quality-of-service guidelines that prioritize efficiency and reliability. The overall forecasts that trouble us most assume current practices will remain in place; changing the practices will change the estimates. Moreover, when usage increases for legitimate reasons—such as broader access and improved instruction—efficiency ensures that the extra energy used is lower per unit of learning than it would have been, which reflects responsible scaling.

Another critique is that claims of efficiency are overstated. It's smart to question the numbers. However, various independent assessments point in the same direction. Vendor reports reveal significant improvements in joules per token for recent accelerators, and third-party evaluations analyze these speed claims, showing that while overall rack power might increase, the work done per unit of energy rises at a much faster rate. Additionally, peer-reviewed methods are emerging to measure model performance in terms of energy and water use across various deployments. Even if any single claim is overly optimistic, the trend is clear, and different vendors can replicate the combination of architectural efficiency, distillation, and hardware co-design. For education leaders, the best approach is not disbelief; it's conditional acceptance: create procurement policies that reward demonstrated efficiency and penalize unclear energy use.

A third concern is infrastructure; schools in many areas face rising tariffs and overloaded grids. That's precisely why workload placement is crucial. Keep privacy-sensitive or time-critical tasks on energy-efficient local devices whenever possible; send batch tasks to cloud regions with cleaner grids and better cooling systems. Require vendors to disclose region-specific emission metrics and give buyers choices. Where a national cloud or academic network is available, education ministries can negotiate sector-wide rates and efficiency commitments, including plans for carbon-intensity disclosures per thousand tokens. This isn't unnecessary bureaucracy; it's modern IT management for a resource that is limited and costly.

Some may wonder if high-profile efficiency cases, such as affordable, effective chatbots, are exceptions. They are indications of what's possible. A notable case achieves competitive performance at a fraction of the cost of conventional computing by leveraging routing efficiency, targeted distillation, and hardware-aware training. Independent industry analysis credits its efficiency not to miraculous data but to solid engineering. As these techniques become more widespread, they redefine the efficient frontier for inference costs relevant to education—such as translation, formative feedback, concept checks, and code explanations—where smaller and mid-sized models already perform well if appropriately designed and fine-tuned on carefully chosen data. The policy opportunity is to connect contracts to that frontier so that savings are passed through.

Lastly, there is the challenge posed by climate change. Predictions of AI-related emissions growth are not mere scare tactics; they serve as alerts about a future without discipline in efficiency. If we take no action, power consumption by data centers will continue to rise into the 2030s, and some areas will revert to higher carbon generation to meet peak demands. If we do take action—by establishing efficiency metrics, timing workloads intelligently, and relocating computing resources wisely—education can seize the benefits of AI while reducing the energy required for each learning gain. This isn't just a financial story; it's a matter of credibility for the sector. Students and families will notice whether schools truly embody the sustainability principles they teach.

So, what should leaders do right now? First, revise requests for proposals (RFPs) to make energy per outcome a key criterion for awards, complete with clear measurement plans and third-party audit rights. Second, default to small models using distilled or MoE-routing for routine tasks and only escalate to larger models based on explicit policies. Implement management strategies to handle prompts and caches, minimizing recomputation. Third, partner with organizations that maintain a PUE close to 1.1 and have documented plans for joules per token, while also insisting on region-specific carbon intensity disclosures for hosted workloads. Fourth, strengthen internal capabilities: a small "AI systems" team to tune routing, batch jobs, and RAG pipelines is far more valuable than another generic SaaS license. Fifth, educate: help faculty and students understand why efficiency is equivalent to access and how choices regarding prompts, model selection, and timing impact energy use. This is how education can evolve AI from a flashy pilot into lasting infrastructure.

The final test is straightforward. If, two years from now, your campus is using significantly more AI but spending less per student on energy and emitting less per graduate, you will have bent the curve. The technology already provides the tools: a sub-joule token in the data center, a distilled model on the device, and an MoE gate that only processes what's necessary. The policy work involves placing the fulcrum correctly—through contracts, metrics, and operations—and then applying pressure. The conversation about whether AI inevitably requires ever more energy will continue, but in education, we don't need inevitability; we need results. The key figure to monitor is not solely terawatt-hours but the energy per learning gain. With focus, that number can continue to decrease even as access increases. That's the future we should strive for.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

Bain & Company. (2025). DeepSeek: A Game Changer in AI Efficiency? (MoE routing and distillation details).
Brookings Institution. (2025, Aug. 12). Why AI demand for energy will continue to increase. (Context on the drivers of rising aggregate demand and unit-efficiency trends).
Google. (2024, July). 2024 Environmental Report; Power Usage Effectiveness (PUE) methodology page (fleet-wide PUE ~1.09).
International Energy Agency. (2025, Apr. 10). Energy and AI; Energy demand from AI (regional projections to 2030 for data-center electricity use).
International Monetary Fund. (2025, May 13). AI Needs More Abundant Power Supplies to Keep Driving Economic Growth (emissions implications under current policies).
NVIDIA. (2025, Jun. 11). Sustainability Report, FY2025 (long-run efficiency trend; ~0.4 J/token reference).
Zilliz/Milvus. (2025). How does DeepSeek achieve high performance with lower computational costs? (architecture and training optimizations that generalize).
Zhou, Z. et al. (2025, May 14). How Hungry is AI? Benchmarking Energy, Water, and Environmental Footprints of LLM Inference (infrastructure-aware benchmarking methods).