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The AI counterattack to climate change

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By Matt Cyrankiewicz

· 8 min read

A liminal future

“The two shaping megatrends in our world today are a technology supercycle and a climate supercycle,”

said Tom Friedman, the Pulitzer-winning New York Times columnist,

“… out of which everything else really flows.”

We’re entering a liminal future. A time of uncertainty when anything can happen, from a civilization disaster to a relative utopia. We’re reshaping the planet, what it means to be human and what it means to be a machine. It’s hard to predict what lies ahead and even the best of forecasters, investors and traders get things wrong all the time these days.

In this article, we will explore how we can leverage the power of artificial intelligence (AI) to drive climate entrepreneurs and companies toward an optimistic future where we solve climate change. For-profit or non-profit, certain mindsets can enable quite huge leaps.

A realm of possibilities

First, let’s entertain a chart, and a story:

Graph of climate funding gap

Consider this scenario:

In the coming 18 months, AI displaces millions of knowledge workers. The first ones affected are “career developers”, those who are in it for money, but not passion. Many other professions follow: writers, designers, managers, and lawyers. Millions of smart people will be seeking new opportunities.

At the same time, 70% of young people see a pressing need to do something about the climate but don’t know what. Many might follow the Decarb Bros.

The more talent there is, the more ideas, solutions and companies spawned, and the bigger the difference made. Exponentially. 

Within 2 years, we see previous models becoming obsolete.

New businesses are increasingly powered by AI-driven processes driving ever-increasing efficiency. Something impossible to predict accurately even just months before, just like we didn’t see 1,500+ new AI tools coming… just 2 months ago (more on this below).

Increasing coordination, ever-improving carbon MRV (thereby impacting carbon footprint calculations), and transparency are tightening things, from greenwashing to real investments.

The Paris Accord funding gap quickly shrinks from $7T per year. Not just because of increasing funding, but because of unexpected new efficiency and talent supply. 

Eventually, within this decade, the ends meet. The Technology Supercycle wrestles with the Climate Supercycle - and wins.

Optimistic? Anything is possible in the liminal future.

The Technology Supercycle

I’m a serial founder who has almost always had partners and teams. Impressed by the recent wave of new AI tools and solopreneurship, I decided to do a very personal - and yet very public - deep dive as an experiment: to build a new company in 30 days, running solo, with $0 budget to start and only AI as my partner

The results have been very promising. How did that happen? Well, my AI partner makes a difference.

Graph of exponential AI model growth

The Technology Supercycle is accelerating. The progress with AI is expected to be doubling and quadrupling every year, for the rest of the decade.

But can it match the accelerating Climate Supercycle and even solve it?

Let’s consider the hype.

So much AI hype lately! As part of my 30-day quest, I decided to go on a deep dive and read tens of those new clickbait articles, reviewed hundreds of AI and “AI” tools, and tested tens of them, distilling what can help.

My key takeaways:

  • Can things really be done “in minutes” as most of the hype would proclaim? No.
  • Can 10x optimizations be achieved? Yes, but it depends on where.
  • Will this change the world? It already is.

There is an AI program for it.

There are reasons for the hype. New tools arrive every day. Hundreds of them! I recommend looking here, here and here. The first one even uses AI to find the right AI.

Each of these lists had about 100 just weeks ago, now there are 1500+ and growing. 

Working with AI

It can be overwhelming, so if you or your team are just starting:

  1. Get only ChatGPT Plus with GPT-4 enabled.
  2. Take a few weeks to master prompting.
  3. To start, ask it to teach you something new.

But the potential goes far beyond tools.

It’s all in the teamwork. Except the team is not all human.

As someone working to solve climate change, you probably have a team. Your team members can use AI tools every day. Some tools will increase productivity by 10x, others will only provide a 5% boost and others still will make no difference.

It's all about tying these elements together and implementing the right processes.

To enable real speed, embrace this thought exercise: think of AI instances as team members.

The industry calls them “agents” because they can act on their own. Whether you use an agent such as the recently popular AutoGPT or just a reactive tool (eg. ChatGPT or Midjourney), it helps to think about it as a team member with a certain role. Each of your humans can work with multiple such accomplices.

This, I believe, prepares our mindset for what is to come: AI stepping in as highly focused and capable team members, not just tools.

This is what your team could look like:

Team structure with AI

Now, notice that there are humans who work with AI. Those people are not going away! But they can focus on what they are best at, building up their expertise.

But by weaving together focused human experts and AI tools, we can bring out the best of the two sides.

What does this mean for a climate business?

My AI partner and I are in the design and tech services industry, but our approach can be applied to any field. Here's a general outline of how we can use AI to accelerate our work as a team:

  1. AI training and education: People are not trusting of these tools. The more they ignore them, the more they fall behind. Invest in training and education programs for employees to better understand the capabilities and limitations of AI. Encourage team members to take courses or attend workshops on AI, machine learning, and data analysis so they can effectively integrate AI into their work.
  2. Collaborative AI-human projects: Encourage team members to work on projects that combine their expertise with AI capabilities. This can be done by setting up cross-functional teams that include both human experts and AI "agents" that can support each other in various tasks. For example, pairing a human analyst with an AI tool to perform climate data analysis, where the analyst guides the AI to identify relevant patterns and the AI automates data processing.
  3. Develop AI-enhanced processes: Evaluate your company's existing processes to identify areas where AI can be integrated to improve efficiency and effectiveness. For example, using AI to automate routine tasks, such as data entry or report generation, can free up human team members to focus on more strategic and creative work.
  4. Custom AI tool development: Encourage your team members to experiment with creating their own AI tools or modify existing ones to better suit their needs. This can lead to a more effective use of AI within the company, as the AI tools will be tailored to the specific challenges and requirements of the team (or outsource this part to an external team that has an AI as a partner).
  5. Establish AI best practices: Develop company-wide guidelines and best practices for AI use, ensuring that all team members understand how to effectively work with AI. This can include guidelines on data privacy, ethical AI use, and tips for successful collaboration between humans and AI team members.
  6. Open communication channels: Encourage open communication between team members working with AI and those who are not. Sharing successes, challenges, and insights can help foster a culture of AI-driven innovation and ensure that all team members are aware of the potential benefits and limitations of AI.
  7. Iterative improvement: As AI tools and technologies continue to evolve, it's important to regularly evaluate and update the ways in which your team is using AI. Encourage team members to share feedback and suggestions for improvement, and make adjustments to your AI-enhanced processes and workflows as needed.
  8. Play the long game: It’s reasonable to expect fully functioning AI agents, self-healing code, single-use apps and mad levels of content and marketing automation in the near future. Assume your team will welcome AI or heavily AI-augmented members, but don’t get too distracted by all the tools popping up every week, rather focus on making sure the culture is there.
  9. Share your success: As you implement AI tools and see positive results, share your experience with other organizations in your industry or network. This will help to accelerate the adoption of AI, leading to broader climate change solutions.

The earlier we start, the faster we will be able to move and the faster we can all help drive things towards a future where climate is solved and biodiversity incurs minimum loss. Is a $7 trillion gap still so high, when AI makes it half as expensive to implement the solutions that existed before?

Anything is possible in the liminal future.

AI generated graphic

AI generated graphic

AI-generated image

illuminem Voices is a democratic space presenting the thoughts and opinions of leading Sustainability & Energy writers, their opinions do not necessarily represent those of illuminem.

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About the author

Matt Cyrankiewicz is a serial founder, climate impact investor and former artist and designer. He is the CEO of the Liminal Future Group focused on intertwining award-winning design, technology and a global community to drive climate impact into the age of intelligence.

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