Today's article differs slightly from the usual reflection or recommendations for CTOs and other engineering leaders. It sits at the intersection of culture, technology, and environment.

Everybody and their mother have been jumping on the GenAI psychedelic bus. As is often the case in online discourse, the discussion is acutely polarised.

On the one side, people believe the immediate next step after GenAI is AGI, superintelligence, a modern take on the Philosopher's Stone¹.

On the other side, we have people who believe AI will lead to the doom of humanity in the long term, starting by replacing the jobs of millions of people until it finally takes over control of the entire planet.

While I'm wary of the totalitarian techno-optimist approach, for reasons that I'll detail later, I also don't believe AI, and specifically the current generation of GenAI solutions primarily based on LLMs, will end up having a massive impact suggested by the darkest predictions.

Nobody is in a position to predict what the long-term implications of such technology will be for humanity. I'm particularly skeptical of individuals or organizations venturing into such predictions, as these have more to do with religion and faith than facts and observations.

Humans are terrible at long-term predictions in two ways:

• We are awful at predicting the long-term effects of actions, decisions, or discoveries happening in the present. When Leo Baekeland invented Bakelite in 1907², the first industrial plastic based on a synthetic polymer, I don't think he'd have expected that about a century later, micro-plastic would be everywhere, including dogs and human testicles.³⁴

• We tend to be overly optimistic about our ability to deal with a current problem later in the future. It happens with mundane tasks that lead us to procrastination, up to the issue of dealing with technical debt, or with the consequences of problems caused today. We're the species that dominates all other living creatures at one thing: kicking the can down the road, hoping that some magic trick of technology will make it easier to deal with it in an unspecified later moment, or “ahorita”, as they'd say in Mexico.⁵⁶

But while everybody is busy doing just that — trying to predict the long-term implications of this new technology — marking a new high in online tribalism, we're not paying enough attention to something more mundane that is already happening.

GenAI is driving a surge in energy and water consumption right now when technology should be deployed to shift the trend in the opposite direction.

If we don't reverse this trend, someone will have to revise the ridiculous prediction that 50 billion humans will live on this planet anytime soon.⁷

On a more serious note, let's look at some data and facts on our overall situation.

• January 2024 marked the first time since we started tracking temperatures in which temperatures averaged 1.5C above pre-industrial times.⁸

• The original target of hitting net zero by 2050 to have a decent chance of containing warming to 1.5C seems already unrealistic. Recent studies suggest that the deadline should be moved forward to 2034.⁹

• 2023 was a record year on two nefarious metrics: fossil fuel use and emissions. You'd expect that after the Paris Agreement 2015 and the relatively short time horizon projected by the scientific community, we should already be reducing our emissions globally. We're not.¹⁰

With all that in mind, let's look at the world of Big Tech and GenAI.

Dr. Sasha Luccioni is the Climate Lead at Hugging Face, focusing on understanding LLMs' environmental implications. One interesting finding of her research is summarised in the following quote:

From my own research, what I’ve found is that switching from a nongenerative, good old-fashioned quote-unquote AI approach to a generative one can use 30 to 40 times more energy for the exact same task. So, it’s adding up, and we’re definitely seeing the big-picture repercussions.¹¹

Let's put this into perspective.

In 2022, Data Centers worldwide accounted for about 2% of global energy consumption. That number is expected to double by 2026.

Roughly the amount of energy used by Japan:

The IEA estimates that, added together, this usage represented almost 2 percent of global energy demand in 2022 — and that demand for these uses could double by 2026, which would make it roughly equal to the amount of electricity used by the entire country of Japan.

I'm not sure how those estimates were made, but with more and more companies integrating their self-proclaimed “smart” tools into their existing products, this number could grow even faster. With Google rolling out its AI answers everywhere, we'll have enough data to update the forecast soon.¹²

Some might argue that my concerns are unjustified.

After all, Big Tech is pushing its renewable energy agenda. Everything they do has been green since its inception. Everything they do is carbon neutral, so why should I even bother?

This reasoning is weak on two main points:

• Deploying renewable energy to cater exclusively to additional energy demands does nothing to solve the global energy crisis.

• Big Tech is not as virtuous as they'd like you to think when it comes to energy production and the environmental impact of its operations.

Let's take the example of Microsoft, the company that prides itself on being a first mover in the LLMs gold rush.

In 2020, Microsoft made an ambitious commitment to make its operations carbon-neutral within the decade. That means by 2030.

The reality today is quite different. Depending on the accounting approach, today's emissions at Microsoft are between 29% and 40% higher than in 2020.

Definitely a disruptive way to reduce emissions.

When asked about it, Melanie Nakagawa, Microsoft’s chief sustainability officer, has a beautiful answer to offer:

“When we set these targets and 2020, these were our moonshot,” Nakagawa said. “And we remain steadfast in our commitment to getting there. We still have six years to go. We believe there’s incredible innovations, both happening today as well as what we are investing for, that will be online by 2030.”¹³

Nakagawa didn't say it explicitly, but she's referring to nuclear fusion, the final solution to climate change predicated by most Techno Optimists.

There is a religious-like belief shared by most tech bro's that nuclear fusion will solve all our problems with renewable energy. Maybe.

Google's SREs made famous their motto: “Hope is not a strategy”. Yet, that's precisely the strategy deployed in this case.

While nuclear fusion could play a significant role in the long term, its viability within the short time horizon we need to operate in to avoid hard-to-predict catastrophic events is unlikely to be substantial.

In the words of another Microsoft employee, Kate Crowford:

Most experts agree that nuclear fusion won’t contribute significantly to the crucial goal of decarbonizing by mid-century to combat the climate crisis. Helion’s most optimistic estimate is that by 2029 it will produce enough energy to power 40,000 average US households; one assessment suggests that ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes. It’s estimated that a search driven by generative AI uses four to five times the energy of a conventional web search. Within years, large AI systems are likely to need as much energy as entire nations.¹⁴

For those unaware of it, Crowford is talking about Helion's Energy. Sam Altman is the company's board chair and has invested $375 million in it. OpenAI is apparently in talks with Helion to buy massive amounts of fusion power… whenever that will be produced. Another rabbit hole I don't have the intention to dig into right now.¹⁵

Then, there is the problem of water consumption.

A paper from October 2023¹⁶ shed some light on the less talked about externality of GenAI. According to the research, the impact is far from trivial:

To put it in perspective, training a single AI model like GPT-3 could require as much as 700,000 liters of water, a figure that rivals the annual water consumption of several households. Moreover, a simple conversation with ChatGPT consisting of 20 to 50 questions can cost up to a 500ml bottle of freshwater.¹⁷

Though there are expectations that energy efficiency and new GPUs will improve energy efficiency, this inefficiency is real today. Widespread usage and glorification of LLM-based tools are making the problem more acute.

Where is all this taking us? Should we stop using AI and become Luddites?

Not at all. But we should be mindful of the implications of our decisions and actions. We should ask ourselves whether the benefits of using specific tools are worth the environmental cost or externalities, as economists call them.

This is something we can do at the individual level.

When it comes to the overall tech industry, this is ultimately an issue of prioritization.

Money is a finite resource, as is time. As any good investor does, opportunity cost should be evaluated when deploying capital for some purpose.

When all the VC money in the world is invested in a technology that so far is only making matters worse for the climate, we have the moral obligation to consider the opportunity cost of such investments: what would be the impact on life on this planet if part or all of that capital was deployed to reduce carbon emissions as quickly as possible?

And don't tell me that this is utopian as the ROI would not be worth the deployment of capital, markets self-regulate, and that GDP growth is the only thing that matters.

Call me a socialist if that makes you feel better; I don't care.

But while you do so, please share your arguments to explain the ROI calculation of making life on this planet miserable for billions of people.¹⁸

I haven't found a manifesto covering that yet.

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