19 Jul 2026
Two philosophies are fighting over how artificial intelligence should be built — one chases scale at any cost, the other asks what that cost actually is Ujjwal K Chowdhury Strapline: For a decade, AI research had one scoreboard: accuracy. A new one is forcing its way onto the field — energy, water, carbon and hardware. The contest between “Red AI” and “Green AI” is no longer academic; it is shaping how the world’s most powerful technology gets built. The paper that named the problem In 2020, a small group of computer scientists — Roy Schwartz, Jesse Dodge, Noah A. Smith and Oren Etzioni — published a short, blunt paper in the Communications of the ACM with a title that stuck: “Green AI.” It drew a line through the field. On one side sat what the authors called Red AI: research that chases state-of-the-art results by throwing ever more computation at a problem, treating accuracy as the only currency that matters. On the other side stood Green AI: research that treats efficiency — the resources spent per unit of result — as a first-class scientific goal, not an afterthought. The label was provocative on purpose. Red AI was not, the authors were careful to say, morally wrong. It had produced genuine breakthroughs. But it had also quietly normalised an arms race in which each new record-setting model consumed dramatically more compute than the last, with the environmental bill rarely itemised in the paper’s appendix, let alone its abstract. Six years on, that argument reads less like a provocation and more like a prophecy. Generative and agentic AI systems now sit inside search engines, office software, customer service lines and increasingly autonomous workflows that plan, browse, code and retry without a human in the loop. The scoreboard Schwartz and colleagues warned about has expanded from leaderboard rankings to gigawatts, litres and tonnes of carbon dioxide. Two philosophies, one industry Red AI, at its core, is a bet that more computation reliably buys more capability — bigger models, longer training runs, wider search over architectures, more parameters, more data, more reasoning steps at inference time. It is the logic behind scaling laws, and it has worked spectacularly well as a research strategy. But it has a hidden accounting problem: the “winning” run reported in a paper or press release is usually just the tip of an iceberg of failed experiments, architecture searches, ablations and evaluation runs that never make it into the final number. Recent lifecycle research — including a 2025 study led by Jacob Morrison that traced the full environmental cost of building a language-model family — found that model development contributed roughly half of the total training-related impact, not the celebrated final run alone. Green AI, by contrast, asks a different question of every architectural choice, every training run and every product feature: what is the smallest, most efficient way to achieve an acceptable outcome? It treats efficiency — measured in floating-point operations, energy, water and, increasingly, successful outcomes per unit of resource — as an evaluation criterion sitting alongside accuracy, not subordinate to it. Crucially, Green AI has matured past its original, somewhat narrow framing. In 2020 it was largely about training compute. Today, researchers describe it as the quality- and outcome-constrained minimisation of lifecycle environmental impact — a formulation that captures something Red-versus-Green rhetoric can miss: a computationally hungry model is not automatically the villain, and a lean one is not automatically virtuous. A large model solving a genuinely high-value problem in a handful of steps can outperform, environmentally, a small model that fails repeatedly and triggers costly retries. The real dividing line is not model size; it is whether computation is productive. Why the contest matters now The urgency comes from scale. According to the International Energy Agency’s most recent assessment, global data-centre electricity demand rose roughly 17% in 2025 alone — more than five times faster than overall global electricity growth — while electricity consumption specifically tied to AI-focused facilities surged around 50% in the same year. The IEA’s satellite-tracking programme, which watches construction of dedicated “AI factories” from orbit, found that their combined capacity has more than tripled in eighteen months. Data-centre electricity use worldwide, which stood at roughly 415–485 TWh depending on the estimate and year, is on a trajectory toward roughly 950 TWh to beyond 1,000 TWh by 2030 — comparable to the entire annual electricity consumption of Japan. FAST FACTS > - Global data-centre electricity demand: ~485 TWh in 2025, heading toward ~950 TWh by 2030 (IEA) > - AI-focused data-centre demand: up ~50% in 2025 alone > - US data-centre share of national electricity: 4.4% in 2023, projected 6.7–12% by 2028 (LBNL) > - AI-rack power density: up roughly elevenfold, 2020–2025 (IEA) > - Ireland’s data centres already draw over a fifth of national electricity; Dublin’s local share runs close to 80% This is precisely the terrain Red AI was warned about: growth compounding on growth, with local grids in Ireland, Northern Virginia and parts of the Netherlands already straining, and utilities in the United States requesting billions of dollars in rate increases partly attributable to data-centre load growth. Energy-policy academics have begun asking, pointedly, whether ordinary electricity customers should effectively subsidise the power appetite of trillion-dollar technology companies — a question with no comfortable answer for regulators. Where the two camps actually clash The Red AI/Green AI split is not simply “big model bad, small model good.” It shows up in concrete engineering and business decisions: 1. Model selection. Red-style practice defaults to the most capable, largest available model for every task, regardless of whether the task warrants it. Green practice builds a portfolio: small or domain-specific models for routine work, escalating to frontier models only when complexity demands it. Systems such as FrugalGPT, which learned to route easy queries to cheaper models and reserve expensive ones for hard cases, demonstrated cost reductions of up to 98% on selected benchmarks without materially sacrificing quality. 2. Reporting practice. Red AI habitually reports only the final training run’s cost. Green AI insists on lifecycle transparency — development experimentation, fine-tuning, evaluation, and the electricity, water and embodied-hardware cost of years of subsequent inference, which can dwarf the original training bill many times over. 3. Agentic design. This is the newest and sharpest fault line. An autonomous agent can quietly multiply a single user request into dozens or hundreds of model calls, tool invocations, retries and multi-agent “debates.” Early benchmark research has found up to a 9.4-fold energy difference between agent-framework designs solving the same software-engineering tasks, driven mostly by wasted loops and redundant verification. A 2026 preprint proposing a metric called Energy per Successful Goal (EpG) found that agentic workflows consumed, on average, 4.33 times more energy per completed goal than equivalent linear, non-agentic approaches. Red AI treats agent autonomy as an unqualified upgrade; Green AI treats it as a resource-management problem requiring budgets, loop detection and outcome-based evaluation. 4. The rebound trap. Perhaps the most uncomfortable insight from Green AI research is that efficiency gains alone do not guarantee lower total impact. If a model becomes twice as cheap to run, organisations often respond by running it far more than twice as often — generating more content, running more experiments, automating tasks nobody previously bothered to automate. This is a version of the century-old Jevons paradox, in which efficiency improvements in coal-fired steam engines led, historically, to more coal consumption, not less, because cheaper power expanded its uses. Green AI researchers now argue that intensity metrics (energy per task) must be paired with absolute-impact accounting (total annual energy, water and carbon) precisely to catch this rebound before it erases hard-won efficiency gains. The measurement mess neither side can ignore Part of what makes the Red/Green debate so combustible is that reliable, comparable numbers are still scarce. A landmark 2025 measurement of Google’s production systems found a median energy cost of just 0.24 watt-hours and 0.26 millilitres of water per text prompt — a strikingly small figure. Around the same time, a separate academic benchmark estimated that complex, long-context reasoning queries on certain models could consume more than 33 watt-hours — over a hundred times more. Both figures are credible within their own scope; they simply describe different systems, different tasks and different accounting boundaries. A 2025 peer-reviewed review of data-centre water use went further, finding that water consumption per workload can vary by more than 10,000-fold depending on cooling technology, grid water intensity, climate and utilisation. This is why serious Green AI researchers are wary of single, universal “footprint per query” numbers circulating in the media — they tend to flatten an extraordinarily heterogeneous reality into a misleadingly precise soundbite. The more defensible approach, gaining traction in both research and emerging regulation such as the European Union’s data-centre reporting rules, is a layered hierarchy: from raw activity counts (tokens, model calls), up through compute energy, facility-adjusted energy, environmental impact (carbon and water, adjusted for time and place), full lifecycle impact including embodied hardware emissions, and finally outcome-normalised impact — energy and water per successfully completed task, not per token generated. Not a morality play — a design discipline It would be easy, and wrong, to read Red AI and Green AI as heroes and villains. Some of the most consequential AI applications — climate modelling, grid forecasting, drug discovery, materials science for batteries and solar cells — are legitimately compute-intensive, and restricting them to “small and frugal” would forfeit real value. The IEA itself estimates that mature AI applications could trim energy costs across several industries by 3 to 10 percentage points, and Google has reported enabling tens of millions of tonnes of avoided CO2-equivalent emissions through AI-optimised products in a single year. Green AI’s actual claim is narrower and more rigorous: that value should be measured against lifecycle cost, that claims of benefit require credible counterfactual evidence, and that scale should be earned by demonstrated necessity rather than assumed by default. HIGHLIGHT > “A Green AI system is not simply smaller or faster. It is appropriately capable, transparently measured, powered and cooled responsibly, designed to avoid waste, and deployed where its verified value exceeds its environmental cost.” What comes next Expect the Red/Green fault line to move from academic papers into contracts and regulation. Procurement teams are beginning to demand model-level energy and water disclosures before signing cloud contracts. The EU’s AI Act ecosystem is developing standards for reporting the resource performance of general-purpose AI systems. Enterprises are experimenting with model-routing rules that default to the smallest sufficient model rather than the flashiest one. And a growing chorus of researchers argues that the next frontier metric will not be accuracy, or even energy per token, but energy per successful goal — a number that punishes both wasteful agents and models that fail so often they need constant escalation. The Red AI era was not a mistake; it built the models the world now depends on. But the bill for that approach is now visible in gigawatts, litres and rising electricity tariffs, and it is arriving at a moment when climate constraints leave little room for waste. Green AI’s proposition is simple, if not easy: intelligence, at any scale, should have to justify its keep. Reading the two camps side by side Red AIGreen AICore metricAccuracy / benchmark scoreQuality-adjusted efficiency (energy, water, carbon per successful task)Model choiceBiggest available, by defaultSmallest sufficient model, escalate only when neededReportingFinal training run onlyFull lifecycle: development, training, inference, hardwareAgentsAutonomy as unqualified upgradeAutonomy as a budgeted, monitored resourceRiskRebound erases efficiency gainsAbsolute-impact caps alongside intensity targets Framed this way, the contest is less a war between two tribes of researchers than a description of a choice every AI-building organisation now has to make, explicitly or by default, every time it ships a feature. The instinctive path — reach for the largest available model, let an agent iterate until it seems to have solved the problem, publish the headline benchmark and move on — is Red AI, whether or not anyone in the room uses the term. The alternative requires more upfront engineering discipline: measuring what a task actually needs, instrumenting the full resource cost, and being willing to report a less flattering number if that is the honest one. Neither side of the debate disputes that AI can create enormous value. The disagreement is about method — whether that value is pursued by default at maximum scale, or earned deliberately at the scale a task actually requires. As electricity bills, water permits and carbon disclosures increasingly follow AI systems out of the lab and into public scrutiny, that distinction is starting to carry real financial and regulatory weight, not just scientific interest. ...Read more
17 Jul 2026
It is not one invention but a discipline — spanning smarter models, honest measurement and hard limits on waste Prof Ujjwal K Chowdhury Strapline: Green AI will not arrive as a single breakthrough. It is being assembled, piece by piece, out of smarter algorithms, redesigned data centres, new accounting rules and a willingness to ask whether a task needed a supercomputer in the first place. Defining the term properly “Green AI” is often used loosely, as a synonym for “AI that feels less wasteful.” Researchers who work in the field define it more precisely: the quality- and outcome-constrained minimisation of the lifecycle environmental impact of an AI system. Every word in that definition is doing work. “Lifecycle” means the accounting cannot stop at a single training run — it must include research and experimentation, data preparation, fine-tuning, round-the-clock inference, agent orchestration, data-centre construction and cooling, electricity generation, and the mining, fabrication and eventual disposal of hardware. “Quality- and outcome-constrained” means Green AI is not simply “use less compute” — a model that saves energy but fails at its task, or that needs constant human correction, has not achieved anything green at all. The term traces to a 2020 paper by Roy Schwartz and colleagues that contrasted this approach with “Red AI” — the pursuit of state-of-the-art accuracy through ever-larger computation, with efficiency treated as an afterthought. Since then the field has broadened well beyond machine-learning theory into data-centre engineering, materials science, distributed systems, water science, economics and public policy. How Green AI actually reduces impact — the toolkit Researchers and engineers now have a genuine, tested toolkit for cutting AI’s resource footprint, operating at every layer of the stack. At the model level. Not every task needs a frontier-scale model. Task-specific and domain models can match performance on narrow jobs at a fraction of the memory and compute cost, and can often run on local devices rather than cloud data centres. Model cascades — systems that route easy requests to small, cheap models and escalate only genuinely difficult ones to larger models — have demonstrated dramatic savings; the research system FrugalGPT showed cost reductions of up to 98% on selected tasks while preserving output quality. Quantisation (reducing the numerical precision of a model’s internal weights) and distillation (training a smaller “student” model from a larger “teacher”) both cut deployment energy substantially, at the cost of some upfront retraining effort. Sparse or mixture-of-experts architectures activate only a fraction of a model’s total parameters for any given input, expanding capacity without proportionally expanding energy use per request. At the inference level. Because generating output token-by-token is often the most expensive part of serving a model, techniques such as speculative decoding (a small draft model proposes text that a larger model merely verifies, rather than generating from scratch), key-value caching (reusing previously computed information instead of recalculating it), and adaptive or “early-exit” reasoning (stopping once a model is confident enough, rather than always running the maximum computation) can cut energy per request without changing the underlying answer. A large 2025 study measuring more than 32,000 configurations across models and GPU hardware found that matching architecture to hardware, and tuning batching and utilisation, mattered as much as model choice itself. At the infrastructure level. Data-centre engineering has moved from generic efficiency metrics toward site-specific redesign: direct-to-chip and immersion liquid cooling, which can cut water use dramatically compared with evaporative systems in the right climate; heat-reuse schemes that feed data-centre waste heat into district heating or industrial processes; battery storage and demand-response systems that let facilities absorb the rapid power swings AI workloads create; and a shift from annual renewable-energy accounting toward genuine hour-by-hour carbon-free electricity matching, which prevents companies from claiming a “clean” annual average while still drawing fossil-heavy power at peak evening hours. At the agent level — the newest frontier. Because autonomous, tool-using agents can silently balloon a single request into dozens of model calls, Green AI research has begun proposing agent-specific controls: hard budgets on the number of steps, tokens and tool calls a workflow may use; automatic loop detection to catch agents stuck repeating unproductive cycles; routing that assigns the smallest capable model to each sub-task, escalating only when genuinely necessary; and outcome-based evaluation using a proposed metric called Energy per Successful Goal, which penalises agent designs that waste computation on retries, redundant multi-agent debate or failed verification. Selected Efficiency Results FrugalGPT model-cascade routingUp to 98% cost reduction on selected tasksAgent-framework redesignUp to 9.4× difference in energy use for the same completed task (2025 benchmark)Direct chip-level closed-loop coolingOne major operator claims more than 125 million litres of water saved per data centre annually GoogleReports 12 GW of clean energy contracted in 2025 and 78% of freshwater consumption replenished (company-reported) Measuring what matters — and admitting what we don’t know yet A recurring theme among Green AI researchers is that measurement itself remains immature, and that this is not a minor technical gap but a governance problem. A landmark 2025 study of Google’s production systems reported a median cost of just 0.24 watt-hours and 0.26 millilitres of water per typical text prompt, alongside large year-on-year efficiency gains. A separate academic benchmark, using different models and a different methodology, estimated more than 33 watt-hours for complex, long-context reasoning queries — over one hundred times higher. Both numbers are legitimate; they simply describe different systems and different task complexity, which is exactly the problem: without a shared functional unit and quality threshold, headline “AI footprint” figures cannot be meaningfully compared, and companies can select whichever framing flatters them. In response, researchers have proposed a seven-level hierarchy of Green AI metrics, moving from crude activity counts (tokens, model calls) through compute energy, facility-adjusted energy (including cooling and idle capacity), full environmental accounting (carbon and water adjusted for time, place and water-basin stress), lifecycle impact (including embodied hardware emissions), outcome-normalised impact (per successfully completed task), and finally absolute organisational impact — the only level capable of catching rebound effects that intensity metrics alone miss. Open measurement tools such as CodeCarbon, Carbontracker and the industry-standard MLPerf Power benchmark are improving reproducibility, but researchers caution that no measurement tool can fix a poorly defined functional unit or a missing supply-chain boundary. The next phase of research should make energy, carbon, water and materials first-class optimisation variables. The next phase of policy should make claims auditable and local impacts visible. The current state of progress: real, but partial How far has Green AI actually come? The honest answer is: further than five years ago, but nowhere near far enough to offset AI’s raw growth in scale. On the positive side of the ledger: efficiency per individual task is, by most credible measures, improving faster than at almost any point in computing history, driven by better model architectures, smarter serving systems and the techniques described above. Major cloud operators report substantial renewable-energy procurement and water-replenishment programmes, alongside progress on closed-loop and liquid cooling that can cut onsite water use sharply where deployed. Regulatory frameworks are catching up: the European Union now requires structured energy and water reporting from data centres, and the EU AI Act ecosystem is developing standardised resource-reporting rules for general-purpose AI models. Independent benchmarking initiatives, including the AI Energy Score project, are beginning to let outside researchers compare model efficiency on defined tasks rather than relying solely on company claims. On the other side of the ledger: global data-centre electricity demand is still climbing steeply — up roughly 17% in 2025 alone, with AI-specific facilities growing around 50% in the same year — showing that efficiency gains are, so far, being outpaced by sheer volume growth exactly as the rebound-effect research predicted. Agentic AI is expanding faster than the tools built to measure or govern its resource use, and remains, by most researchers’ assessment, in an “early-stage, high research priority” state rather than a solved problem. Water accounting remains inconsistent enough that credible studies report more than a 10,000-fold variation across otherwise comparable workloads — a sign that the industry still lacks agreed, auditable standards. And embodied hardware impact — the minerals, fabrication water and manufacturing emissions locked into every accelerator before it processes a single request — remains the least developed area of lifecycle assessment, largely because supply-chain data is scarce and closely guarded. What a genuinely green deployment looks like Researchers increasingly converge on a simple decision rule for organisations deciding whether to deploy AI at scale: proceed only when four conditions are jointly met. Necessity — the application creates material, demonstrable value. Proportionality — the model and any autonomous agent built around it are no larger or more independent than the task actually requires. Transparency — energy, carbon, water and lifecycle impacts can be measured or credibly estimated, not merely asserted. Net benefit — the quality-adjusted value delivered, socially, economically or environmentally, exceeds the lifecycle cost, with rebound effects actively monitored rather than assumed away. The opportunity for fast-growing, water-stressed economies For countries such as India — with rapid digital growth, hot climates, constrained grids and water-stressed cities — Green AI is not only a defensive necessity but an industrial opening. Policy researchers argue that fast-growing digital economies should avoid simply importing data-centre designs optimised for cooler, water-abundant regions, and instead map proposed AI capacity against transmission constraints, renewable supply and urban water plans from the outset — favouring non-potable cooling sources, dry or hybrid cooling systems and seasonal operating limits rather than defaulting to the energy- and water-intensive designs common in temperate markets. That same constraint creates a market. Efficient small models tuned for Indian languages, energy-aware edge AI that keeps processing on-device rather than in the cloud, low-water cooling technology, power electronics, and auditable sustainability software are all areas where necessity could plausibly drive genuine innovation rather than imitation. Public procurement has real leverage here: governments that require energy and water reporting as a condition of AI contracts can create demand for exactly the transparent, efficient systems Green AI research is trying to build — turning a regulatory requirement into a home-grown industry. The bottom line That rule captures what Green AI has become, seven years after the phrase was coined: not a call to make AI smaller for its own sake, but a discipline for making sure every unit of computation has to earn its keep — a shift from celebrating efficiency in isolation to managing absolute impact in full view. The technology is not yet there. But for the first time, the tools, the metrics and the regulatory appetite to get there all exist simultaneously — which is more than could be said even three years ago. ...Read more
12 May 2026
The second great challenge of corporate sustainability lies in the physical reality of production. For a century, industrial success was measured by throughput—the speed at which raw materials could be converted into products and sold. The barrier here is the Linear Infrastructure Lock-in. Billions of dollars are invested in factories, power plants, and logistics networks designed for a one-way flow of resources. Transitioning to a sustainable model requires more than just "doing less harm"; it requires a move toward Regenerative Business Models that actively contribute to the restoration of the ecosystems they draw from. One of the most significant barriers to this pivot is the Resource Scarcity-Complexity Trap. As companies try to move away from fossil fuels, they encounter a massive surge in demand for "transition minerals" like lithium, cobalt, and rare earth elements. This creates a new set of ethical and environmental dilemmas. The innovation solving this is the Circular Design Paradigm. Instead of simply looking for "better" materials to extract, innovative firms are designing products for "Disassembly." By using modular components and avoiding toxic glues or complex alloys, companies like those in the electronics and appliance sectors are ensuring that today’s products become the "urban mines" of tomorrow. This "Closed-Loop" manufacturing eliminates the need for virgin extraction and insulates companies from the volatility of global commodity markets. Another major hurdle is Energy Intermittency and Industrial Heat. While many corporations have successfully transitioned their offices to renewable electricity, the "Hard-to-Abate" sectors—such as steel, cement, and chemical production—require intense heat that solar and wind struggle to provide. Here, innovation is taking the form of Industrial Symbiosis. In "Eco-Industrial Parks," the waste heat or byproduct of one company becomes the fuel or raw material for its neighbor. For example, a data center’s excess heat can be piped into a nearby greenhouse, or a steel mill’s carbon emissions can be captured and converted into aviation fuel. This mimics natural ecosystems where "waste" does not exist, and every output is a useful input for another organism. The barrier of Consumer Inertia also plagues corporate progress. Even when a company develops a truly sustainable product, consumers are often reluctant to change their habits or pay a premium. To counter this, businesses are innovating through Behavioral Economics and Choice Architecture. Instead of making the "green" option a specialized luxury item, companies are making it the "default" setting. Whether it’s a logistics company defaulting to carbon-neutral shipping or a food giant reformulating its core products to be plant-forward, these subtle shifts utilize human psychology to drive mass-scale sustainability without requiring constant, conscious effort from the end-user.Finally, the evolution of Corporate Governance is providing the ultimate solution to the barrier of accountability. We are seeing the rise of "Benefit Corporations" (B-Corps) and legal frameworks that mandate directors to consider the interests of all stakeholders—employees, communities, and the environment—rather than just shareholders. This legal "hard-coding" of sustainability ensures that the mission survives leadership changes and economic downturns. As AI and machine learning begin to optimize supply chains for "minimum carbon" rather than just "minimum cost," the corporation is being redefined. It is moving from being a mere profit-extraction machine to becoming a sophisticated engine of social and ecological value, capable of thriving within the boundaries of a finite planet. ...Read more
11 May 2026
The Architecture of Transparency – Frameworks and Standards the Evolution from Voluntary Reporting to Global Mandatory Standards. The landscape of sustainability reporting has shifted from a "nice-to-have" marketing supplement to a rigorous financial and operational requirement. For decades, the primary barrier to effective ESG performance was the "alphabet soup" of reporting frameworks. Organizations struggled to choose between the Global Reporting Initiative (GRI), the Sustainability Accounting Standards Board (SASB), and the Task Force on Climate-related Financial Disclosures (TCFD). Each offered a different lens: GRI focused on the impact of the company on the world (multi-stakeholder), while SASB focused on the impact of the world on the company (financial materiality). In 2026, we have moved toward a more unified architecture. The International Sustainability Standards Board (ISSB) has successfully integrated many of these frameworks into IFRS S1 and S2. This consolidation allows investors to compare ESG performance across borders with the same rigor as traditional balance sheets. However, the challenge for organizations remains the concept of Double Materiality. This principle requires companies to report not only on how sustainability issues affect their bottom line but also how their operations impact the environment and society. Implementation of these standards requires a massive overhaul of internal data systems. Unlike financial data, which is captured in standardized ERP systems, ESG data is often "unstructured"—hidden in utility bills, manual spreadsheets, or third-party supplier reports. To achieve 1,000-word depth in this area, one must analyze the role of Auditability. As regulators like the SEC in the US and the CSRD in Europe mandate limited and eventually reasonable assurance, sustainability reports must be "investment-grade." This means every data point, from carbon emissions to gender pay gaps, must have a clear audit trail. Organizations are now treating their Sustainability Report with the same gravity as their Annual 10-K, moving the responsibility from the PR department to the CFO’s office. ...Read more
