Tag Archives: artificial intelligence

Taming the Technosphere in an Age of AI Ascendency and Information Plenitude

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David P. Turner / May 23, 2025

AI-infused technosphere

Stylized image of Earth and its AI-infused technosphere.  Image Credit: MS CoPilot.

Introduction

The technosphere is increasingly impacting core features of the Earth system, notably the climate and the biosphere.

Accordingly, there is wide recognition that we humans (with the usual caveats about the meaning of “we” here) need to rein in (tame) the technosphere.

However, this project does not amount to just putting constraints on existing technology; new technologies designed to harvest, synthesize, and act on vast arrays of digital data are also needed.

The idea of managing the technosphere, or indeed the Earth system, has an obvious air of hubris.  But the technosphere is unquestionably having global scale deleterious impacts on the Earth system, and the human responsibility to do something about it is clear.

Notable recent changes in the technosphere that will help with global management include the arrival of well-functioning Artificial Intelligence (AI) tools like large language models (LLMs) and neural network-based machine learning algorithms.  Along with the information plenitude referred to in my title  ̶   a product of the recent trend to digitize all information, be it in the form of text, images, audio, or video  ̶  some hope of grasping global scale dynamics is emerging.

AI provides a fundamentally new way of thinking – beyond historical faith-based and reason-based approaches.  The human thinking apparatus – our brain  – simply does not have the capacity to assimilate and synthesize information at the scale with which it is being produced.  Fortunately, AI algorithms in combination with massive computing power and vast observational data sets can often do the job.  One great strength is that they can search the solution space of a problem more thoroughly that the unassisted human mind.

AI might also be considered analogous to a new source of energy.  Just as fossil fuels freed up our bodies to do more interesting and less labor-intensive activities, AI is freeing up our minds to do more fulfilling and less routine activities, including attending to sustainability at local to global scales.

The Role of AI in Earth System Management

Managing a socio-ecological system (such as the Earth system) requires monitoring the system, developing a model to assimilate observations of the system and help with understanding and planning, and organizing a deliberative body to make decisions.

The relevant global scale monitoring data includes observations of the metabolism and growth of the technosphere and the biosphere, along with tracking of the real time behavior of other parts of the Earth system, e.g. AI machine learning has proved useful in satellite-based monitoring of deforestation.

The relevant models could be 1) process-based Earth system simulation models, e.g. that include a representation of the global climate as well as social subsystems, 2) mechanism-free data driven models based on AI-assisted pattern analysis, or 3) combinations of these two approaches.

As far as deliberative bodies, the world is dangerously short of global governance infrastructure.  United Nations sponsored efforts such as the Paris Climate Accords are only a shadow of what is needed.  AI may be helpful in generating and summarizing relevant data, though of course the bigger challenge is with human capacity and willingness to change.  Ultimately, superintelligent AI might have features that make it better at devising optimal choices about global governance than (mere) humans could come up with.

Use Cases That Already Effectively Combine AI with Massive Data Sets

1) AI management of regional electricity grids.  Management of regional electricity grids has become much more complicated as they transition from traditional base-load power plants to supply by renewable sources.  The intermittency of wind and solar, as well as variation in the scale of the inputs (roof-top to utility-scale operations in the case of solar energy), are particularly challenging.  Smart grids must be capable of sending and receiving power from a multitude of individual customers, and maintaining dynamic pricing to balance power consumption during periods of high and low demand.

Fortunately, machine learning is effective at assimilating a blizzard of power system data, and in making automated decisions that link energy supply and demand.

2.  Use of meteorological observations, climate models, and AI to develop early warning systems for extreme weather events.  The incidence of extreme weather events (EWEs) such as floods, droughts, and fire is increasing because of anthropogenically-driven climate change.  Associated human fatalities, and damage to technosphere infrastructure, are correspondingly rising.

Weather forecasting tools like regional weather models have significant limitations with respect to forecasting EWEs.  These models are regularly updated by observations, and they project conditions into the near future based on simulating physical processes such as wind, precipitation, and heat transfer.  The model limitations show up in terms of the kinds of information they can assimilate, and in representing physical processes at the proper spatial and temporal scale.

An alternative weather forecasting approach uses AI (machine learning) to assimilate a much broader array of observations.  This approach largely leaves physical mechanisms behind and is said to be “data driven”, i.e. based on statistical relationships between historical observations of the causes and effects of weather change.  The forecast of an extreme weather event from this approach can be promptly fed to appropriate emergency response organizations that act to make relevant preparations and increase local resilience.

Beyond weather forecasting,  the machine learning approach is beginning to be used in applications based on Earth system digital twins.  A digital twin can be used to simulate alternative scenarios and may have embedded one or more process-based simulation models.  As model complexity increases, e.g. by adding models of social subsystems, a machine learning-based model may prove to be more effective.

A critical point here is that mass observations of weather and ground data are needed to train and drive an AI-based model.  In that regard, the recent focus on shrinking federal agencies like NOAA, NASA, and EPA, which make critical environmental observations, is particularly counterproductive.

3)  Use of Large Language Models (LLMs) in Earth System Science education and advocacy.

The technosphere is creating vast amounts of textual, graphical, and video information specifically about global environmental change, including peer-reviewed journal articles, books, periodicals, blogs, and NGO reports.  The more that people use this information to understand what is happening in the Earth system, and what could be done about it, the better chance we have of making needed changes in the technosphere (e.g. reducing greenhouse gas emissions). 

Domain-specific LLMs (e.g. Earth Copilot) have an important role to play in this aspiration because they can, with infinite patience, communicate a synthesis of the scientific consensus on a given topic at an appropriate level of learning ability (i.e. level of education). 

Certainly, an LLM can produce wrong answers at times.  The output of an LLM depends on the data used in its training, the manner in which it is fine-tuned, and new web-based information that it employs in response to a specific prompt.  Errors or lies in the training data may thus leak into the outputs.  Already, researchers have found policy relevant differences among the most used AI-chatbots, and bad actors are creating web sites with false information purely for the consumption of AI web crawlers that scrape the web seeking new information.

Despite the resistance of LLM creators to having their training data censored, more care is needed.  It is also important to continually expand, by way of further research, the range of concepts and ideas used in training data. 

Conclusion

AI gives us the ability to access, synthesize, and manage the contemporary plenitude of digital information.  Early successful applications of AI include management of regional electric grids, forecasting of Extreme Weather Events, and facilitating learning about Earth System Science.  Given that human progress in managing the technosphere is incommensurate with the damage the technosphere is doing to the Earth system, the range of potential applications is immense.  “We” must continue to develop them.

Credits

The lead image was created by collaboration with MS CoPilot, and some of the links were inspired by Perplexity.AI.  The text, with all its human flaws, is of my own hand. Taming the Technosphere blog posts are apparently used by the web scrappers that collect training data for AI LLMs, and are used (sometimes with attribution) for query-related responses by AI-assisted chat bots.

Differentiating the Concepts of Biosphere, Technosphere, and Technobiosphere

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David P. Turner / February 20, 2025

Figure 1. A stylized rendering of the integration of biosphere and technosphere. Image credit: Original Graphic.

Earth System Science studies the Earth system in terms of the whole, its parts, and the associated dynamics.  The biosphere and the technosphere are well-recognized functional parts of the current Earth system, but while the biosphere helps maintain the global biogeochemical cycles and climate, the technosphere is disrupting them.  The technobiosphere concept represents a potential fusion of these two parts into a matter-, energy-, and information-processing entity that advances planetary evolution.

Biosphere

In the early 20th Century, Russian geochemist Vladimir Vernadsky identified the biosphere as the sum of living organisms on the surface of Earth.  He emphasized how the biosphere absorbs solar energy and uses the energy to construct and maintain order in the form of biomass.  Earth system scientists have subsequently discovered that over geologic time, the biosphere has undergone major changes in the kinds of organisms it contains and in the way it contributes to maintaining the global biogeochemical cycles and global climate.

Technosphere

In a more recent conceptual advance, geologist Peter Haff identified the technosphere as the sum of all human-built technological artifacts on the surface of Earth, along with the human beings and institutions that manage those artifacts.  Like the biosphere, the technosphere uses energy (mostly in the form of fossil fuels) to construct and maintain order.  In this case, the order is in the form of machines and structures of various sorts networked together to support advanced technological civilization.  The technosphere is expanding rapidly, and indeed we have entered the Anthropocene era in which technosphere metabolism has begun to act as a geological force. 

The biosphere and technosphere concepts are helpful in thinking about Earth as a system, and how it changes over time.  One notable observation is that the technosphere is now growing at an exponential pace and its growth is coming in part at the expense of the biosphere – specifically a loss of biodiversity and ecosystem diversity. 

The technosphere – unlike the biosphere – largely does not recycle its wastes, e.g. vast amounts of plastic end up in landfills, and CO2 is freely dumped into the atmosphere from the combustion of fossil fuels.  The current trajectory of technosphere impacts on Earth’s climate and biosphere is leading to an instability in the Earth system that will challenge humanity’s ability to adapt.

Technobiosphere

For the long-term welfare of humanity, the next step in planetary evolution may well be a fusion of the biosphere and technosphere.  This new entity – the technobiosphere – deserves a label because, although it will retain a well-functioning biosphere and technosphere, much of its self-regulation will depend on human consciousness and, perhaps eventually, Artificial Intelligence (AI).

What that fusion will mean in practice is that the technobiosphere is run on renewable energy, largely recycles its waste materials, and does not grow at an exponential rate.  It would have the capacity to monitor itself, maintain itself, and alter its impacts on the global biogeochemical cycles.  New stabilizing negative feedback loops would link components of the technosphere, biosphere, atmosphere, hydrosphere, and geosphere.

The global carbon cycle in particular is amenable to technobiosphere regulation by means of controlling energy-based emissions of carbon dioxide and methane, reducing carbon emissions from deforestation, and increasing biologically-based carbon sinks by tree planting and protection of undisturbed ecosystems.

Integration

Clearly the limited contemporary integration of biosphere and technosphere is insufficient to call the combination a technobiosphere.

As to what will drive an enfolding of the technosphere back into the biosphere, I am afraid it is on us. Most importantly, a functional infrastructure for global environmental governance has to be developed to coordinate the global community.  Key principles on which to base that governance include sustainability and habitability.

Sustainability refers to a relationship between the technobiosphere and the rest of the Earth system such that the global environment is stable enough to support successive generations of humans.  If the global climate is warming by 3oC per 100 years because of carbon-based energy generation, the relationship is not sustainable.

Habitability refers to a planetary environment that supports all life forms.  If the growth of techno-artifacts is causing a 50% loss in biodiversity per 100 years, the habitability of the Earth is in decline.  In contrast, habitability could increase if continued urbanization, and an eventual decline in the human population from the global demographic transition, allowed for more of the land and the ocean to be dedicated to conservation purposes. 

The development of AI represents both threats and opportunities in relation to technobiosphere evolution. 

A key threat lies in how AI will speed up the technosphere (hence making greater demands on natural resources) and make the technosphere more autonomous.  Super-intelligent AI bots and agents may eventually care more about their own survival than the survival of the biosphere. 

AI-based opportunities lie in spurring scientific advances that reduce human impacts on the Earth system, and in helping educate natural resource managers and planetary citizens.  AI-based inquiry (with large language models) is a new form of perception  ̶  an intelligence capable of surveying information at the planetary scale and delivering a synthesis accessible to our individual minds.  

Conclusion

The way language works, the existence and meaning of specific words is socially constructed (by way of cultural evolution).  The biosphere concept allows us to see a planetary scale, energy-harvesting, and order-producing entity that helps regulate the global biogeochemical cycles and climate.

The technosphere concept allows us to see a new human-constructed, planetary scale, control force now altering Earth’s biosphere, biogeochemistry, and climate in a destabilizing manner. 

We need to start imagining an integrated technobiosphere  ̶  a part of the Earth system able to monitor and regulate itself so as to survive and thrive at a geologic time scale.

We’re Going to Need a Bigger Power Supply and It Better be Renewable

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David P. Turner / March 1, 2023

Developing and maintaining AI-based conversational beings ̶ such as ChatGPT ̶ will significantly increase global energy demand. In the interests of global sustainability, that additional power must be from renewable sources. Original graphic (Monica Whipple and David Turner).  Image Credits: Circuitry, Wind Farm, Solar Panels, Pylons.

When the sheriff character in the original “Jaws” movie first sees the giant shark, he exclaims to the captain “You’re gonna need a bigger boat”.

An analogous statement regarding the energy requirements associated with the coming proliferation of conversational virtual beings (based on Artificial Intelligence) is that the technosphere is going to need a bigger power supply.

By virtual beings I mean all the digital, language-capable, denizens of the emerging metaverse (broadly defined), including chatbots (like ChatGPT), AI-assisted search engines (like Perplexity AI), and AI-based residents of Meta’s visor-enable virtual reality world.  Coming down the line are speaking holograms, and holodecks (as in Star Trek).

The process by which these advanced digital creatures learn to speak is based on development of neural networks that are trained with a large body of textural information (like Wikipedia, books, and an array of content available on the Internet).  Training means determining statistical relationships between the occurrence of different words in the training text, which the algorithm then uses to formulate a response based on keyword inputs (queries).

Training a large language model such as ChatGPT requires a hefty input of computing power because it involves extensive trial and error testing.  Chatbots affiliated with AI-assisted Internet searches use not just a pre-trained language model but also integrate the search output into their responses.  This kind of processing will be energy demanding (perhaps 5 times greater than for a standard search), which will add up considering the billions of searches made per day.

If these virtual beings were only going to be used by a minority of people (such as now visit Meta’s colony in the metaverse), the power draw would be minor.  But, very likely, their seductive appeal will be so great (albeit with an occasional hint of menace) that they will become a standard feature of ordinary life.  Just in the field of education, there is vast potential for inspiring and informing students using dialogic Chatbots.

Efficiency in training and operation of these virtual beings will no doubt increase, but industry specialists see a booming rise in electrical energy demand as their use expands.  Note that electrical power demand for electric vehicles, and to power the broader trend towards electrification of heating and industry, will also rise significantly in the coming decades (a good thing!). 

The overshoot model argues that global energy consumption should be reduced rather than expanded because of the many negative environmental externalities (unaccounted for damages) caused by energy production  ̶  from both fossil fuel and renewable sources. 

However, at least for electricity, that seems unlikely given the burgeoning energy demand in the developed world noted here, and the aspiration to raise standards of living in the developing world.

Since 66% of global electricity production is still based on combustion on fossil fuels, any increase in electricity consumption will tend to result in more greenhouse gas emissions and more societal problems with climate change.  The obvious conclusion in that new energy demand must be met by nonfossil fuel sources like hydro, wind, solar, geothermal, and nuclear fission.  Companies such as Google, Microsoft, and Meta that are building the metaverse will experience huge increases in energy consumption in the near future; they should be held to their commitments to run on carbon neutral power sources.

New energy technologies that could contribute to a clean global power supply in the coming decades include geologic hydrogen and solar energy from space.  These sources, however, will require long-term investments in research and development.

The global renewable energy revolution is off to a good start and has a bright future, but it will require steady political pressure to 1) stop building new fossil fuel burning facilities, 2) replace aging fossil-fuel-based infrastructure with renewable sources, and 3) build new renewable energy sources that can accommodate the increasing demand that is surely coming.