Saving energy using Predictive Thermal AI Models

The year is 2035. Artificial Intelligence has taken over the world. Everywhere one goes, they are met with some form of artificial intelligence or the other, be it a simple LLM or a pseudo-sentient intelligence that does all of your daily tasks for you.

Now, this also means the establishment of hundreds, if not thousands of data centers by 2035, using up tons upon tons of electricity to power their respective servers.

The largest cloud providers are collectively spending hundreds of billions of dollars on AI infrastructure.

AI-related capital expenditure is projected to reach trillions of dollars by 2035, implying a massive expansion of data-center capacity.

AI data-center electricity demand is expected to increase from 415 TWh in 2024 to about 1,200 TWh by 2035. In a high-growth scenario, demand could exceed 1,700 TWh by 2035.

For context, 1,200 TWh is greater than the current annual electricity consumption of many major industrialized countries.

AI data centers could emit unto half a gigaton of carbon dioxide annually by the year 2035 if non-renewable and polluting sources such as coal are used for electricity generation which would account for 1 - 1.5% of total carbon dioxide emissions by that year.

In the year 2035, I propose a carbon credit trading project which will minimize electricity usage by AI companies and hence the consequential emissions. AI companies can mitigate electricity use by AI driven cooling optimization.

Data centers must maintain:

Server inlet temperature: typically 18–27°C (ASHRAE recommended range)

Humidity control

Airflow uniformity

The companies will use their own product to reduce the emissions caused by the product itself. Companies will use predictive thermal models of artificial intelligence in order to predict temperatures of CPUs and GPUs minutes or maybe even hours into the future so as to optimize the energy required for cooling the processing units at that temperature. The intelligence unit will collect data such as rack inlet/outlet temperatures, floor tile temperatures, temperature gradients using thermal sensors and fan speeds, air pressure, using airflow sensors to completely optimize the cooling process so as to use the least amount of energy.

AI companies will be incentivized to develop and use predictive thermal models at their own cost

Through a workable incentive structure.

1. First of all, the entry cost will be made near zero. There will be a “shadow mode” period where the model runs and predicts, but doesn’t control anything yet, so companies can validate its accuracy and risk before applying it to the actual cooling process.

2. Credits will be tied only to verifiable outcomes, not just using the model. They will be earned through measured energy reduction from an independently audited baseline.

3. Certain reputational incentives will also be implemented: A public leaderboard ranking companies by verified emissions reduction.

Case studies and press features for top performers, which is free marketing.

These models made by AI companies will not be used only for themselves, but also externally, in sectors such as Industrial process cooling & manufacturing, Cold chain & refrigeration, Mining and underground facilities, Greenhouses and more, as all of these sectors use up a lot of energy in cooling processes. Therefore, AI companies can earn additional credits by allowing companies in other sectors to use their models.

Credit Trading Structure:

Earning Credits

This system is designed to reward creation and spread of good thermal models, not just using one internally.

1 carbon credit = 1 metric ton of CO₂-equivalent avoided

Emissions avoided = (Baseline energy use - Actual energy use) x Grid carbon intensity

Baseline: What the company’s cooling energy use would have been without the predictive model, set by an independent auditor using more than a year of pre-adoption training data.

Grid Carbon Intensity: kg CO2 per kWh for that specific region and time.

The baseline audit is the most exploitable part of this system, so it needs a periodic re-baselining, so a baseline set in 2026 doesn’t stay artificially favorable in 2032.

AI companies can earn two types of credits:

Deployment Credits - earned form the companies own verified energy saving internally using their own model.

Diffusion Credits - earned when other organizations in any sector adopt a model this company built, and their own verified savings get traced back to it.

Trading Credits:

There are two main buyers

Compliance buyers - companies in regulated cap-and-trade schemes that need credits to meet legal emissions targets. This is the deepest, most reliable pool of demand.

Voluntary buyers - corporations doing ESG offsetting, NGOs, and sustainability-focused funds.

Credits will be sold in an auction market, not at a fixed price. Real voluntary carbon credits currently trade anywhere from ~$3 to $50+ per ton depending on verification quality high-integrity, well-audited credits command a premium. This gives AI companies a direct incentive to invest in better verification, since it raises the price their credits fetch.

Where the money goes once a credit sells:

A cut to the registry/verifier (funds the ongoing audit infrastructure — this needs to be self-sustaining, not grant-dependent).

A cut into the user token pool.

The remainder to the AI company as direct revenue on top of the electricity savings they already got from using less power

Value to Users:

AI companies will only be incentivized to build thermal models and reduce emissions if they get something in return, which is obviously profit. For this, they need to appear more appealing and better than their other competitors to widen their client base and reward their already existing clients, thus increasing profit.

Conversion mechanic: a fixed share of each company's earned credits ( 20-30%) is set aside and converted into tokens distributed to that company's users, rather than sold on the open market.

one tokens redemption value will be tied to a fraction of the prevailing credit market price, so that tokens aren’t just a fake internal currency but have some form of stable backing.

These tokens can be traded in for basic necessities such as food, water and clothing, through partner companies such as grocery chains, general stores, restaurants and clothing chains that form connections with these AI companies.

Therefore, this system will benefit AI companies through carbon credits, increased client base and wider reach. It will benefit other companies that require cooling processes through the thermal models and it will benefit the general public through tokens, and obviously the most important factor, reduction of carbon dioxide and other greenhouse gases released into the atmosphere.