If artificial general intelligence exists, it will be able to perform many tasks better than humans. For now, the machine learning systems and generative AI solutions available on the market are a stopgap to ease the cognitive load on engineers, until machines that think like humans exist.
Generative AI is currently dominating the headlines, but its backbone, neural networks, has been in use for decades. These Machine Learning (ML) systems historically acted as cruise control for large systems that were difficult to maintain continuously by hand. The latest algorithms also proactively respond to errors and threats, alerting teams and recording logs of unusual activity. These systems have been further developed and can even predict certain outcomes based on previously observed patterns.
This ability to learn and respond is being adapted to all kinds of technology. One that continues to persist is the use of AI tools in envirotech. Whether it’s enabling new technologies with vast data processing capabilities, or improving the efficiency of existing systems by intelligently adjusting inputs to maximize efficiency, AI has such an open end that it can theoretically be applied to any task.
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The undeniable strengths of AI
GenAI is not inherently energy intensive. A model or neural network is no more energy inefficient than any other piece of software when in use, but the development of these AI tools generates the majority of the energy costs. The justification for this energy consumption is that the future benefits of the technology are worth the cost in energy and resources.
Some reports suggest that many AI applications are “solutions in search of a problem,” and many developers are expending enormous amounts of energy developing tools that can deliver questionable energy savings at best. One of the biggest benefits of machine learning is its ability to read large amounts of data and synthesize insights that people can act on. Reporting is a laborious and often manual process. Time-saving reporting can be used to apply machine learning insights and actively address business-related emissions.
Companies are under increasing pressure to start reporting on Scope 3 emissions, which are the most difficult to measure and the largest contributor to emissions for most modern businesses. Capturing and analyzing these disparate data sources would be a smart use of AI, but would ultimately still require regular human guidance. Monitoring solutions already exist on the market to reduce the demand for engineers. Taking AI one step further is therefore an unnecessary and potentially harmful innovation.
Replacing the engineer with an AI agent reduces human labor, but removes a complex interface to add equally complex programming. That does not mean that innovation should be discouraged. It is a noble goal, but don’t be sold the fairy tale that this will happen without problems. Some engineers will eventually be replaced by this technology, but the industry must approach this with caution.
Think about self-driving cars. They are there, they do a better job than an average human driver. But in some edge cases they can be dangerous. The difference is that this danger is very easy to see, compared to the potential risks of AI.
Today’s ‘smart’ machines are like naive people
AI agents at their current stage of development are similar to human workers: they require training and supervision and will gradually become obsolete unless they are retrained from time to time. Similarly, as observed with ChatGPT, models can degrade over time. The mechanisms driving this degradation are not clear, but these systems are subtly calibrated, and this calibration is not a permanent state. The more flexible the model, the greater the chance that it will fail and function suboptimally. This can manifest as data or concept drift, a problem where a model invalidates itself over time. This is one of the many inherent problems in coupling probabilistic models with deterministic instruments.
A worrying area of development is the use of AI in natural language input, in an effort to make it easier for less technical workers or decision makers to save on hiring engineers. Natural language output is ideal for translating expert, topic-specific output from monitoring systems, in a way that makes the data accessible to those who are less data literate. Despite this power, even summaries can be subject to hallucinations when data is fabricated. This is an issue that persists in LLMs and can cause costly mistakes when AI is used to summarize business-critical reports.
The risk is that we create AI overlays for systems that require deterministic input. Trying to lower the barrier to entry for complex systems is admirable, but these systems require precision. AI agents cannot explain their reasoning, or truly understand a natural language input and elaborate on the real request in the way a human can. Plus, it adds another low-power software to a tech stack for minimal gain.
We can’t leave it all to AI
The rush to realize ‘everything with AI’ results in a huge amount of wasted energy. With the current 14,000 AI startups, how many will actually produce tools that will benefit humanity? While AI can improve the efficiency of a data center by managing resources, it ultimately does not translate into meaningful energy savings because in most cases that free capacity is then channeled to another application, taking advantage of available resource space , plus the cost of even more AI-powered tools.
Can AI help achieve sustainability goals? Probably, but most proponents don’t address the “how” part of that question, in some cases suggesting that AI itself will come up with new technologies. Climate change is now an existential threat and there are so many variables that challenge the understanding of the human mind. Instead of tackling this problem head-on, technophiles are passing the buck to AI, hoping that it will provide a solution at some point in the future. The future is unknown and climate change is happening now. Banking on AI to save us is simply crossing our fingers and hoping for the best, dressed up as neofuturism.
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