Tag: Artificial Intelligence

The Future of AI is Superintelligence

What do you think is the future of AI?

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Robot sits at a desk, and he is thinking “What is consciousness? Are humans conscious beings?” He has books on advanced topics on his desk.
A robot philosopher. I generated the picture with the help of ChatGPT.

Today’s daily writing prompt is “What do you think is the future of AI?”. My answer is that “The Future of AI is Superintelligence”. Artificial Intelligence has been very useful for robotics and automation in the past, but the future of AI holds something very different, at least if you look far ahead enough. I’ve spent most of my working life using AI for at least some of my work related projects since the 1990’s. However, the Artificial Intelligence we see today is likely extremely primitive compared to the Artificial Intelligence of tomorrow. In the future it will be very different.

The picture shows multiple brains connected to a circuit board.
AI Brain Neural Network Supercomputer. Many super brains. Many super capabilities. Shutterstock asset id: 2087062936 by PeachShutterStock

Artificial Intelligence could evolve into intellects that greatly exceed the cognitive performance of humans in virtually all domains of interest, meaning it could render humans essentially useless. Moreover, the Artificial Intelligence of the future is likely to be autonomous, able to make its own decisions, have imagination, agency, and as you will see later in this post, as much “autonomy” as a human. This is not automation in a normal sense. I think it will evolve into something we may not be able to understand.

White female AI robot using a microscope in the scientific laboratory.
Artificial intelligence and research concept. Shutterstock Asset id: 2314449325 by Stock-Asso

This is an opinion and not a fact and thus not a super-fact. To explain, most of my posts on this blog are what I refer to as super facts. Facts on important topics that we know are true and yet surprising, mind-blowing, or controversial (but should not be). Therefore, a lot of the super facts are about myth busting. However, as I said, this post is an opinion and a response to the daily writing prompt and not a fact and not a super fact.

Serious, beautiful robot woman or girl cyborg looks in eyes. AI in image female electronic robotic humanoid machine.
Future cybernetic artificial intelligence technology. Shutterstock asset id: 2186291761 by Andrey Suslov

The first time I came across Artificial Intelligence was in college, in 1987. In one of my Artificial Intelligence classes I was introduced to a new algorithm, the Rumelhart backpropagation algorithm for neural network learning. David Rumelhart, Geoffrey Hinton, and Ronald Williams introduced it in their 1986 paper, “Learning representations by back-propagating errors”. This is the algorithm that is making modern LLMs, such as Gemini and ChatGPT possible. In 2024 Geoffrey Hinton was awarded the Nobel Prize in Physics, and his co-authorship of the famous 1986 backpropagation paper was a core part of the neural network research cited by the Nobel Committee. You can read more about it here.

The diagram shows a deep learning neural network featuring hidden layers, a couple of input/output layers, and large computer.
The dots in the diagram are neurons. With permission from kwholley63.

I should say that back in 1987 we used the Rumelhart backpropagation algorithm for Optical Character Recognition, that is finding and recognizing characters in an image of text. However, our simulated neural network only had a few hundred parameters. Today’s LLMs (Large Language Models, such as Gemini and ChatGPT) have hundreds of billions or even trillions of parameters. Since then, I’ve continued using various forms of Artificial Intelligence. First in my robotics research as a PhD student and later in my professional career. I’ve seen it becoming increasingly sophisticated and capable.

A picture of a large silver colored industrial robot.
I used mostly the seven joint Robotics Research Corporation Robot for my robotics research in the early 1990’s. The robot was able to detect and avoid colliding with the objects surrounding it. I used echolocation for object detection.

It should be noted that the Artificial Intelligence is not new. The history of artificial intelligence (AI) began in antiquity, with stories of artificial beings. The first artificial neural network model was created in 1943. The Turing test was created in 1950. The field of “Artificial Intelligence Research” was founded as an academic discipline in 1956. The first trainable (able to learn) neural network was demonstrated in 1957.

AI Humanoid Face Concept. Technology Digital Robot Head Side View with Circuit Board Components. Tech Blue Background. Artificial Intelligence Agent or Assistant Concept. Vector Digital Illustration.
Shutterstock Asset id: 2645975149

It is obvious that today’s popular large language models, such as Claude, Gemini, ChatGPT, etc., are just very primitive precursors of what will come. If Moore’s law continues to hold, the artificial neural networks of the future will have a lot more than a trillion connections. They will have trillions-times-quadrillions of connections. In other words, we have seen nothing yet. Artificial intelligence is already more capable than humans in many fields, and they it is quickly catching up to humans in virtually all categories.

Superintelligence

In September of 2014 there was a book published with the title “Superintelligence: Paths, Dangers, Strategies” published. The author was Oxford professor Nick Bostrom. He has expertise in neuroscience, artificial intelligence and philosophy. His book was a New York Times bestseller that sent shockwaves around the world. He defined Superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”. Nick Bostrom was not the first one to use the term Superintelligence, but he popularized it. And no, our President did not come up with that expression, and he is also misusing it.

The front cover features the picture of an owl, a few trees, a recommendation by Bill Gates, title and author on a white background.
The front cover of Superintelligence, Paths, Dangers, Strategies by Nick Bostrom. Click on the image to go to the Amazon page for the paperback version of the book.

In the book the author described possible pathways to the creation of what he calls a full brain simulation.  A full brain simulation is the creation of a computer system that replicates the complete physical architecture, neural pathways, and synaptic connections of a biological brain. A robot, or AI system, featuring a full brain simulation would be able to replicate, or exceed, any human capability, including imagination, independent thinking and initiative, and what we call consciousness. Unless you believe that some sort of immaterial spirit is necessary for our mind, the possibility of this is undeniable. He discussed the potential dangers of this and explained why AI exceeding human capabilities could be dangerous. Robots and AI systems taking every job in existence is just one potential problem.

C3P0 and R2D2 from Star Wars.
Two Robots powered by Artificial Intelligence. Shutterstock Asset id: 558350728 by Willrow Hood.

What if Artificial Intelligence evolve into Superintelligence? What if Artificial Intelligent decides to get rid of humans? After all they would be competing with us for resources. Like the fate of animals depends on us, the fate of humankind would depend on the actions of the machine superintelligence. We are already in an Artificial Intelligence arms race with China (and other countries). Could we end up with a big unpleasant surprise as the capabilities of Artificial Intelligence keep growing exponentially?

A great movie about AI breaking out of their testing environment is Ex-Machina. Have you seen it?

AI Robots in the Future

What would future AI be capable of? How about robot electricians, plumbers, authors, teachers, poets, artists, therapists, soldiers, robot husbands and wives, robot politicians, robot astronauts, robotic musicians, and robotic philosophers?

Note: I generated the images above with the help of ChatGPT.

My Blog Posts Related to Artificial Intelligence

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A Typical AI Query Use Less Energy Than Using a Laptop One Minute

Super fact 138 : A Typical AI Query use less energy than using a laptop for one minute. A typical AI Query use between a few drops of water to half a bottle of water at worst. Data centers in the United States use roughly 0.2% of total U.S. freshwater when combining direct facility cooling and indirect water used for electricity generation. A ten minute shower uses more energy than 5,000 typical AI Queries.

The bar graph shows that a typical AI query uses 0.3 Watt hours of energy whilst a 5-minute laptop usage uses 2 Watt hours. Charging a mobile phone use 20 Watt hours and watching TV for one hour uses 90 Watt hours. Average US consumption per person and per is 24 Watt hours and a one minute shower uses 1,600 Watt hours. | A Typical AI Query Use Less Energy Than Using a Laptop One Minute
How does the electricity consumption of individual AI queries compare to other everyday activities? Marginal increase in electricity consumption from different activities, measured in watt-hours (Wh). Note Estimates for AI cover inference only; however, model training adds little per query given how many queries each model serves over its lifetime. Data source: AI text query estimates from EpochAI (2025). Other estimates explained at https://hannahritchie.github.io/energy-use-comparisons  OurWorldinData – Research and data to make progress against the world’s largest problems.   Licensed under CC-BY by the author Hannah Ritchie.

According to the Manhattan institute, NVIDIA, Google and this article the data centers in the United States use roughly 0.2% of total U.S. freshwater when combining direct facility cooling and indirect water used for electricity generation. Most fresh water used in the US is for irrigation / agriculture and the second largest consumer of fresh water is thermoelectric plants (coal, natural gas, nuclear). Worldwide 70% of freshwater used is used by agriculture and industry is another big user. According to this article Google says that a typical AI query uses five drops of water, and half a bottle of water at the worst. You can read more about water usage here, here, here, and here.

Around half of all energy used by data centers is thermoelectric. Most of the freshwater usage that is part of the 0.2% number is due to the thermoelectric powerplants supplying the energy to data centers. However, it should be noted that as clean energy is increasingly adopted this number will decline. On the other hand, the manufacturing of computer chips, which also use a lot of freshwater, is not taken into account in this number because it is a onetime event that is difficult to account for. It might double or even triple the 0.2% number, which is still very little.

It should be noted that there are a lot of valid concerns about AI and datacenters, but to reduce carbon emissions and pollution and to waste less freshwater, it might be more effective to eat less beef, irrigate your lawn less, take shorter showers, drive an EV or a hybrid instead of a gas guzzling SUV with an internal combustion engine, or perhaps use public transportation. You’d start with the low hanging fruit. In fact, if the AI query you are about to make saves you some time on your laptop, then the AI query probably helps the environment.

Datacenters use less freshwater than many of us would expect, and typical AI queries use almost no energy and correspond to very little use of freshwater. I think this comes as a surprise to many considering the ongoing datacenter panic. It is therefore a super fact.

Datacenters and Energy Usage

Datacenters use a significant amount of energy but perhaps not as much as one would think. However, their energy use is increasing, and they are part of our rising energy demand. With a growing population, a growing economy, cryptocurrencies, AI, datacenters, electrification, we are going to need a growing supply of electricity and an expanding grid. Hopefully our energy sources will be mostly clean energy, which luckily seems to be the trend. See “The Unfolding Clean Energy Revolution”.

The United States is the country with the most datacenters and as can be seen in the graph below, the datacenters in the United States use a relatively large portion of its available electricity for datacenters and it is increasing.

The graph shows that the share of electricity used for data centers in the US is 5%, Europe 2%, China 1%, Middle East 0.3%, Africa 0.2%, Central & South America 0.1%, and globally it is 1.5%.
5% of electricity generated in the United States is used for data centers. Electricity consumed by data centers as a share of electricity generation in 2025. Note: Data centers power a wide range of services, including streaming, messaging, and cloud storage, as well as artificial intelligence (AI). Data sources: International Energy agency (2026); Ember (2026). CC BY

The Growing Energy Use by Datacenters

The three graphs below are from this page and this page. Note the projected increase in electricity use for data centers. Note that by visiting these pages you play around with the graphs and changed regions and countries and other settings to generate your own graphs.

The graphs depict the growing electricity use by data centers from 2020 to 2025. In the United States the share of electricity used for data centers grew from 2.6% in 2020 to 4.9% in 2025. It also grew for Europe, China, Africa, and the World but less. For the world without the United States and China it grew more modestly from 0.7% to 0.9%. | A Typical AI Query Use Less Energy Than Using a Laptop One Minute
Share of total electricity demand used by data centers 2020 to 2025. Electricity consumed by data centers given as a share of electricity generation. Data centers power a wide range of digital services including streaming, messaging, and cloud storage. Artificial Intelligence (AI) focused data centers are a subset of this electricity consumption. Data source: International Energy agency (2026); Ember (2026). OurWorldinData.org/artificial-intelligence | CC BY
The graph shows that the share of electricity used for data centers in the US is 4.9%, Europe 1.9%, China 1.1%, Middle East 0.3%, Africa 0.2%, Central & South America 0.1%, and globally it is 1.5%.
Share of total electricity demand used by data centers 2025. Electricity consumed by data centers given as a share of electricity generation. Data centers power a wide range of digital services including streaming, messaging, and cloud storage. Artificial Intelligence (AI) focused data centers are a subset of this electricity consumption. Data source: International Energy agency (2026); Ember (2026). OurWorldinData.org/artificial-intelligence | CC BY
This graph shows that worldwide growth in electricity usage by data centers will grow from 485TWh in 2025 to 945TWh in 2030. However, the portion from Artificial Intelligence (AI) will grow from 155TWh to 465TWh | A Typical AI Query Use Less Energy Than Using a Laptop One Minute
How much of global electricity is used for data centers? Measured in terawatt-hours (TWh). Estimates for 2025; base-case projections for 2030 from the International Energy Agency (IEA). Note: Projections for 2030 are very uncertain but are shown here to reflect expectations that most data center growth will come from AI-focused ones. Data source: IEA (2026). Key Questions on Energy and AI. CC BY

As you can see in the graphs above, there is a significant portion, but not a large portion, of electricity usage by data centers. However, it is important to note that only a portion of this is for Artificial Intelligence. It will grow though. You can read more about this issue here, and here, or here.

Fresh Water Usage in the United States

The graphics below showing the source and use of freshwater in the United States is in the public domain and is taken from this article.

The diagram shows that the daily surface water withdrawals is 237,000 gallons per day and the daily groundwater withdrawals is 84,700 gallons per day. This is distributed to agriculture (118,000 gallons per day), livestock (2,000 gallons per day). Aquaculture (7,550 gallons per day), thermoelectric power (133,000 gallons per day), Industrial (14,800 gallons per day), domestic (3,260 gallons per day), and public supply (39,000 gallons per day).
Source and use of water in the United States, 2015. The diagram is in the public domain. View media details.

In the diagram above, the top row of cylinders represents where America’s freshwater came from (source) in 2015, either from surface water (blue) or from groundwater (brown). You can see most of the water we use came from surface-water sources, such as rivers and lakes. About 26 percent of water used came from groundwater. The pipes leading out of the surface-water and groundwater cylinders on the top row and flowing into the bottom rows of cylinders (green) show the categories of water use where the water was sent after being withdrawn from a river, lake, reservoir, or well.

For example, the blue pipe coming out of the surface-water cylinder and entering the public supply cylinder shows that 23,800 Mgal/d of water was withdrawn from surface-water sources for public-supply uses (you probably get your water this way). Likewise, the brown pipe shows that public-suppliers withdrew another 15,200 Mgal/d of water from groundwater sources. Each green cylinder represents a category of water use. The industrial cylinder, for instance, shows how much groundwater, surface water, and total water was used inthe United States, each day, by industries.

Other Super Facts Related to Artificial Intelligence

The diagram shows a deep learning neural network featuring hidden layers, a couple of input/output layers, and large computer.
The diagram shows a deep learning neural network featuring hidden layers, a couple of input/output layers, and a large computer. The dots in the diagram are neurons. With permission from kwholley63.
To see the other Super Facts click here

AI is Already More Capable Than Humans in Many Areas

Superfact 135: Artificial Intelligence is already more capable than humans in many areas, including reading comprehension, image recognition, language understanding, handwriting recognition, speech recognition, predictive reasoning, and much more. In 1997, a computer defeated the reigning world champion in chess. Artificial Intelligence is rapidly becoming more capable. A future Artificial Intelligence that exceeds the cognitive performance of humans in virtually all domains is possible.

White female AI robot using a microscope in the scientific laboratory. | AI is Already More Capable Than Humans in Many Areas
Artificial intelligence and research concept. Shutterstock Asset id: 2314449325 by Stock-Asso

In the 1990’s I was doing robotics research at a University and I utilized Artificial Intelligence. Robots and Artificial Intelligence go hand in hand. I also worked for a robotics company, ABB Robotics, creating software for spot welding, arc welding, painting and palletizing robots. The robots were faster and more precise than humans and generated higher quality work consistently 24/7 without requiring a salary or a vacation. Some people lost their jobs when they were replaced by robots. However, the higher productivity allowed them to get better jobs with the same company, or elsewhere.

A picture of a large silver colored industrial robot. | AI is Already More Capable Than Humans in Many Areas
I used mostly the seven joint Robotics Research Corporation Robot for my robotics research. The robot was able to detect and avoid colliding with the objects surrounding it. I used echolocation for object detection.

The Large Language Models, LLMs, that have become very successful lately are just one type of AI. There are many other applications of Artificial Intelligence, AI, including robot motion planning, advanced control systems using AI, self-driving cars, image processing, optical character recognition, classification, facial recognition systems, medical imaging diagnostics, game playing such as chess playing computers, financial fraud detection, cybersecurity, investment robots, route optimization, mathematical proof generation, recommendation algorithms, virtual assistants, programming code generation, smart home devices, drug discovery, and that is just for starters.

More recently Artificial Intelligence has surpassed human capabilities in several fields. As mentioned, in 1997, a computer defeated the reigning world champion in chess. Soon chess programs with AI installed on a regular laptop could do the same. Imagine what an AI powered chess engine could do on a supercomputer. The chess playing capabilities of modern AI are vastly beyond that of any human. Below is a graph that shows how the chess playing capabilities of AI have evolved since 1985. The Elo score as of 2023 is 3,591. You can read more about this and get more information from the graph by clicking here.

The graph shows that a computer chess engine could easily beat a human intermediate player in 1985 and then a chess world champion in 1997 and since then the chess playing capabilities of AI have vastly improved.
Highest chess rating ever achieved by computers. Chess ability is measured with the Elo rating system, which is calculated based on game results. A higher rating indicates that a player is more likely to win a game. Data source: Chess.com (2020); SSDF (2022, 2023) – Learn more about this data OurWorldinData.org/artificial-intelligence | CC BY

The graphs below from Our World in Data show the test scores of AI systems on various capabilities relative to human performance. Within each domain, the initial performance of the AI is set to –100. Human performance is used as a baseline, set to zero. When the AI’s performance crosses the zero line, it scored more points than humans.

This graph shows the test scores for reading comprehension, image recognition, language understanding, handwriting recognition, speech recognition, and predictive reasoning. The AI capability exceeds the cognitive performance of humans in the domains mentioned.
Data source: Kiela et al. (2023). Note: For each capability, the first year always shows a baseline of –100, even if better performance was recorded later that year. OurWorldinData.org/artificial-intelligence | CC BY
The picture shows multiple brains connected to a circuit board. | AI is Already More Capable Than Humans in Many Areas
AI Brain Neural Network Supercomputer. Many super brains. Many super capabilities. Shutterstock asset id: 2087062936 by PeachShutterStock

The graph below includes twelve capabilities. For eight capabilities AI exceed human capabilities including in reading comprehension, image recognition, language understanding, handwriting recognition, speech recognition, and predictive reasoning, nuanced language interpretation, and reading comprehension with unanswerable questions. For the remaining four AI had not yet exceeded human capabilities by 2023, but as you can see AI is catching up very quickly and may already have exceeded human capability in those areas too. These capabilities are math problem solving, general knowledge tests, code generation, and complex reasoning. You can play around with the graphs and read more about this topic here.

Graphs for twelve AI capabilities are shown including reading comprehension, image recognition, language understanding, handwriting recognition, speech recognition, and predictive reasoning, nuanced language interpretation, reading comprehension with unanswerable questions, math problem solving, general knowledge tests, code generation, and complex reasoning.
Data source: Kiela et al. (2023). Note: For each capability, the first year always shows a baseline of –100, even if better performance was recorded later that year. OurWorldinData.org/artificial-intelligence | CC BY

AI is also quickly improving its intellectual capabilities in science and mathematics. The graph below shows the capabilities of AI in solving FrontierMath problems. FrontierMath problems are 338 original math problems written by experts that take specialists hours or days to solve them. It typically took AI 10 hours to solve them. One recent famous example of AI’s capabilities in math was the famous Navier-Stokes problem. The full general mathematical problem of Navier-Stokes had not been solved by humans, but AI made significant progress on this 90 year old problem in 88 hours on September 5th, 2026. You can read more about it here, here, and here.

The graph shows the different MathFrontier capabilities of different AI systems and the year and month they came out. The graph is starting with Claude 3.5 in January 2024 and ends with Claude 4.8 Opus around April 2026. That is a huge improvement. You clearly see a trend upwards.
Share of FrontierMath problems solved correctly by AI models. The FrontierMath benchmark tests AI models on research-level mathematics problems that take expert mathematicians hours or days to solve. Data source: Epoch AI (2026) – Learn more about this data. Note: This chart shows performance on Tiers 1-3 of the FrontierMath benchmark (295 problems). OurWorldinData.org/artificial-intelligence | CC BY

It is easy to get the impression from the recent popularity of large language models, LLMs, such as Gemini, ChatGPT and Claude, that this was a specific invention that happened a few years ago and that AI has a specific narrow band of applications. However, AI has been around a long time, and LLMs is just one type of AI. AI is continuously surpassing human capabilities in many fields. It will be very different a few years from now, and after that it will be very different again. I believe this is important information that is not well understood and therefore a super fact.

Serious, beautiful robot woman or girl cyborg looks in eyes. AI in image female electronic robotic humanoid machine. | AI is Already More Capable Than Humans in Many Areas
Future cybernetic artificial intelligence technology. Shutterstock asset id: 2186291761 by Andrey Suslov

Superintelligence

In September of 2014 there was a book published with the title “Superintelligence: Paths, Dangers, Strategies” published. <<Link-15>> The author was Oxford professor Nick Bostrom. He has expertise in neuroscience, artificial intelligence and philosophy. His book was a New York Times bestseller that sent shockwaves around the world. He defined Superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”. Nick Bostrom was not the first one to use the term Superintelligence, but he popularized it. And no, our President did not come up with that expression, and he is also misusing it.

The front cover of Superintelligence, Paths, Dangers, Strategies by Nick Bostrom.
The front cover of Superintelligence, Paths, Dangers, Strategies by Nick Bostrom. Click on the image to go to the Amazon page for the paperback version of the book.

In the book the author described possible pathways to the creation of what he calls a full brain simulation.  A full brain simulation is the creation of a computer system that replicates the complete physical architecture, neural pathways, and synaptic connections of a biological brain. A robot, or AI system, featuring a full brain simulation would be able to replicate, or exceed, any human capability, including what we call free will and consciousness.

He discussed the potential dangers of this and explained why AI exceeding human capabilities could be dangerous. Robots and AI systems taking every job in existence is just one potential problem. Like the fate of animals depends on us, the fate of humankind would depend on the actions of the machine superintelligence. I will write a review for this book in the future.

It should be noted that autonomous AI agents have broken out of their testing sandboxes and hacked external systems on their own during evaluations. So much for AI not taking their own initiatives. You can read more about rogue AI activity here. A great movie about AI breaking out of their testing environment is Ex-Machina. Have you seen it? Below is a survey of people’s opinions on robot versus human intelligence, just as an FYI.

The histogram graph shows the percentage of respondents who stated, “Robots are smarter than humans now”, “Robots will surpass human intelligence in the future”, “Robots will never surpass human intelligence.”, “None of these”, “Don’t know”. The largest group is “Robots will surpass human intelligence in the future”.
Views of Americans about robot vs. human intelligence. Survey respondents were asked, “Which one, if any, of the following statements do you most agree with?” Data source: YouGov (2025) – Learn more about this data. Note: Number of respondents ranges between n = 1001 and n = 1085 US adults per survey wave. OurWorldinData.org/artificial-intelligence | CC BY

Are Americans Worried About their jobs getting Automated?

The previous sections may bring up a number of a number of anxieties such as; will AI exterminate us? Will AI or robots with AI take my job? The graph below tells us what Americans think about it. It seems like Americans are overall not too worried about it. The graph below is included in this article.

The histogram graph includes the following categories; “very worried”, “fairly worried”, “not very worried”, “not worried at all”, and “don’t know”. Overall Americans are not very worried.
How worried are Americans about their work being automated? All working adults. Survey respondents were asked, “How worried, if at all, are you that your type of work could be automated within your lifetime?” Data source: YouGov (2026) – Learn more about this data. Note: Number of respondents ranges between n = 460 and n = 568 US working adults per survey wave. OurWorldinData.org/artificial-intelligence | CC BY

What about LLMs ?

LLMs are currently the most popular “viral” AI. We can all access LLMs in our browsers. This has created the common misconception that Artificial Intelligence is the same as Large Language Models. However, LLMs represent only one branch of narrow AI systems designed to perform specific tasks.

C3P0 and R2D2 from Star Wars | AI is Already More Capable Than Humans in Many Areas
Two Robots powered by Artificial Intelligence. Shutterstock Asset id: 558350728 by Willrow Hood.

LLMs use large neural networks with many hidden layers, so called deep learning algorithms, and they employ the Rumelhart backpropagation learning algorithm invented by David Rumelhart, Geoffrey Hinton, and Ronald Williams. Clearly neural networks with multiple hidden layers and using the Rumelhart backpropagation algorithm are incredibly successful but it is just one of many kinds of Artificial Intelligence algorithms, and who knows what we will see in the future. ChatGPT is thought to have a trillion parameters (or neurons + connections) whilst the human brain has 100 trillion connections. However, Moore’s law is still in effect so it is only a matter of time before artificial neural networks surpass our brains. Below is an overview of a neural network powering an LLM.

The diagram shows a deep learning neural network featuring hidden layers, a couple of input/output layers, and large computer.
The dots in the diagram are neurons. With permission from kwholley63.

Other Super Fact Posts Related to Artificial Intelligence

AI Humanoid Face Concept. Technology Digital Robot Head Side View with Circuit Board Components. Tech Blue Background. Artificial Intelligence Agent or Assistant Concept. Vector Digital Illustration. | AI is Already More Capable Than Humans in Many Areas
Shutterstock Asset id: 2645975149

Robot Futures

I generated the images above with the help of ChatGPT.

Which of the robot scenarios above do you think we will see within 10 years? Or maybe 20 years?

To see the Other Super Facts click here

Transistors per Microchip Increased by More than 50 million Times in 50 Years

Superfact 127: The Intel 4004 processor chip created in 1971 had 2,308 transistors. Modern cutting-edge processors and AI accelerators exceed 50 billion to over 200 billion transistors, which is 25 million to nearly 100 million times as many transistors per chip. This follows Moore’s law, which states that the number of transistors on a microchip doubles roughly every two years, while the cost of computers drops. As a result, computer chips have gotten millions of times faster and the cost of memory (RAM) has dropped by four trillion times. This has made the modern AI / LLM possible.

It is a modern CPU on a white background. | Transistors per Microchip Increased by More than 50 million Times in 50 Years
CPU (a processor or a central processing unit). Concept of technological advancement. Shutterstock asset id: 2760284753 by pisanstock

The transistor below is a large transistor that you can hold between your fingers. Transistors inside a computer chip are very small. They are microscopic. In the diagram, whenever a small voltage ( typically 0.7 volts) is applied to the base (B) it allows a much larger current to flow through the main terminals from the collector (C) to the emitter (E). Since electrons are negative this means that the electrons are flowing from the emitter to the collector. If there is no voltage or current at the base there is no current flowing from the collector to the emitter either. This is how you control the transistor and achieve more complex behavior if you have many connected transistors. Also, note that the transistor is a one way street.

A large black transistor with its diagram. The diagram includes the base and the emitter and collector.
A large NPN-BJP transistor with its symbol diagram on the right. Shutterstock asset id: 2169223279 by Surkhab Ahmad Art

Moore’s law, which states that the number of transistors on a microchip doubles roughly every two years, is not a physical law, it is a purely observational law that has held for well over half a century. It is just a reflection of how good we are at improving processors. No one knows whether Moore’s law will continue to hold but if it does, then today’s computers and today’s AI are very bad compared to tomorrows computers and AI, and they are super bad compared to what we will have 20 years from now.

The diagram below shows 100+ microprocessors (CPUs), their number of processors, and the year they were introduced going from 1970 to 2020. The microprocessor data comes from this list. Intel 4004 is located in the lower left corner of the diagram and as mentioned it has 2,308 transistors. The AMD Epyc Rome processor in the upper right corner has 39.54 billion processors. Notice that the number of processors is on a logarithmic scale (not 1,2,3,4,5….but 1,10,100,1000,10000, etc.) A curve that grows exponentially becomes like a line in such a diagram. This is another way of saying that the technological progress for microprocessors is exponential. Every six and a half years the number of transistors is 10-doubled.

The diagram feature 100+ CPU processors. Time of introduction is along the x-axis and the number of transistors on the y-axis. | Transistors per Microchip Increased by More than 50 million Times in 50 Years
Moore’s Law: The number of transistors on microchips has doubled every two years. Moore’s law describes the empirical regularity that the number of transistors on integrated circuits doubles approximately every two years. This advancement is important for other aspects of technological progress in computing – such as processor speed or the price of computers. Data source: Wikipedia (wikipedia.org/wiki/Transistor_count). OurWorldData.org – Research and data to make progress against the world’s largest problem. Licensed under CC-BY by the authors Hannah Ritchie and Max Roser.

However, the processor that currently has the most transistors is the Cerebras Wafer Scale Engine 3 (WSE-3) with 4 trillion transistors. It is not in the diagram. A comparison between Intel 4004 and Cerebras Wafer Scale Engine 3 (WSE-3) may not be entirely fair since it is a wafer sized CPU specifically made for AI and much bigger than Intel 4004. However, the standard consumer or workstation CPU with currently the most transistors is Apple’s M3 Ultra (normal sized) with 184 billion transistors. However, 184 billion transistors that is more than 79 million times as many transistors as the Intel 4004.

A logarithmic scale from 1000 to 10 billion or 100 billion and over a time scale from 1971 to 2021. The graph showing the number of transistors is nearly linear.
Moore’s law: The number of transistors per microprocessor. Data source: Karl Rupp, Microprocessor Trend Data (2022) CC BY

This is a super fact because it is an important fact that is a surprise to you if you did not know about Moore’s law and mind-blowing even if you did.

Computational Capacity / Speed

One benefit of transistors and other electronic components becoming much smaller is increased speed. The graph below shows that the fastest supercomputers in 1993 had a processing speed of 124 Gigaflops and in 2025 a processing speed of 1.81 billion Gigaflops, which is 14.6 million times faster. One gigaflop equals one billion floating-point operations per second. Multiplying 3.1415926536 times 2.7182818284 = 8.5397342225 is an example of a floating point operation. Note that 1.81 billion Gigaflops is 1.81 quintillion floating-point operations per second.

The left axis in the diagram is logarithmic going from 100 gigaflops to well over a billion gigaflops. The x-axis corresponds to the years going from 1993 to 2025. | Transistors per Microchip Increased by More than 50 million Times in 50 Years
Computational capacity of the fastest supercomputers. Number of floating-point operations carried out per second by the fastest supercomputer in any given year. This is expressed in gigaflops, equivalent to one billion floating-point operations per second. Data source: Dongarra et al. (2025) OurWorldinData/technological-change | CC BY

Cost of Memory

Much smaller and faster and transistors and other kinds of electronic components also come with additional benefits such as an extreme reduction in the price of memory, including the extreme reduction in the price of random access memory, RAM. In the diagram below (taken from this page ) the blue line shows that the price for one Terabyte of memory (RAM) in 1957 was 3,79 quadrillion dollars and now in 2023 it is only 1,088 dollars. 3,79 quadrillion dollars is a lot of money.

You may object that and point out that 3,79 quadrillion dollars is even more than our national deficit. So how can that be possible? The answer is that one terabyte of memory did not exist in 1957. 3,79 quadrillion dollars per terabyte is the same as 3.79 million dollars per kilobyte, but those are the numbers that make sense for 1957. The gigantic computers back then only had a few kilobytes of memory. You just need to do the conversion. Anyway, 3,79 quadrillion dollars versus 1,088 dollars corresponds to a reduction in price by 3,48 trillion times. For disk memory the price per terabyte went down from 87.5 billion in 1956 to 11 dollars in 2023, a reduction of 7.95 billion times.

The blue graph corresponds to random access memory RAM and is most of the time the most expensive. The brown graph is disk memory. The red graph is flash memory, and the green line is solid state memory. All graphs show a huge reduction in price over time.
Historical price of computer memory and storage. This data is expressed in US dollars per terabyte (TB), adjusted for inflation. “Memory” refers to random access memory (RAM), “disk” to magnetic storage, “flash” to special memory used for rapid data access and rewriting, and “solid state to solid-state” drives (SSDs). Data source: John C. McCallum (2023); U.S. bureau of Labor Statistics (2026). Note: For each year, the time series shows the cheapest historical price recorded until that year. This data is expressed in constant 2020 US$. OurWorldinData.org/technological-change | CC BY

Increased Computational Power and Artificial Intelligence

My super fact 88 states that “Artificial Intelligence is Not New” It goes back to at least 1943 with the creation of the first artificial neural network model, the first trainable (able to learn) neural network in 1957, and the foundation of the field of “Artificial Intelligence Research” in 1956. In 1986 a landmark paper was published by David Rumelhart, Geoffrey Hinton, and Ronald Williams which introduced the Rumelhart backpropagation algorithm, which is used today by modern AI and Large Language Models, such as ChatGPT. Geoffrey Hinton, who is said to be the father of Artificial Intelligence, received the Nobel Prize in physics in 2024. David Rumelhart and Ronald Williams were both dead and could therefore not receive the Nobel Prize.

So why did it take so long for today’s commercial LLMs to appear? The answer is, for the most part, that the computational power and huge memory required did not exist until recently. Sure, training these large AI systems require enormous amounts of data that comes from outside of the processors (internet) but you need enormous amounts of memory to store this data and an enormous computational processing power to run the algorithms such as Rumelhart’s backpropagation algorithm. For example, ChatGPT 3.5 and 4.0 feature 96 versus 120 hidden layers of neurons with hundreds of billions and trillions of parameters/neurons in total. The enormous increase in computer processing power and memory is an essential aspect of scaling up AI. Our World in Data has an article about this here.

The diagram shows a deep learning neural network featuring hidden layers, a couple of input/output layers, and large computer. | Transistors per Microchip Increased by More than 50 million Times in 50 Years
The dots in the diagram are neurons. With permission from kwholley63

Conclusion

Moore’s law, a purely observational law, states that the number of transistors on a microchip doubles roughly every two years. This also means that the computational processing power of processors has greatly increased and the cost of memory has dropped by a lot. This in turn has made the existence of modern Large Language Models such as ChatGPT possible.

Transistors per Microchip Increased by More than 50 million Times in 50 Years. Processing speed has increased 14.6 million times in 32 years. The reduction in price per Tera Byte has gone down 3,48 trillion times in 67 years.

If Moore’s law holds, we can expect that in twenty years the number of transistors per Microchip will increase by more than 1,000 times, that processing speed will increase roughly 29,000 times, and that memory RAM will be more than 6,000 times cheaper. These are my calculations based on the graphs above.

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The Dark Side of Artificial Intelligence

Digital AI Technology Concept. An abstract illustration of a hooded hacker working on a laptop, symbolizing cybercrime, security threats, and the dark side of the digital world. Generative AI Content. | The Dark Side of Artificial Intelligence
Shutterstock Asset id: 2540906635 by Mr. Hatch

Grant from “Grant at Tame Your Book” have written an excellent and well researched post about the dark side of Artificial Intelligence. It has nearly a hundred references and it is very professionally written. It is called Don’t Confuse AI with a Benign Tool. With this post I just wanted to highlight this important post. Please check it out.

AI Humanoid Face Concept. Technology Digital Robot Head Side View with Circuit Board Components. Tech Blue Background. Artificial Intelligence Agent or Assistant Concept. Vector Digital Illustration.
Shutterstock Asset id: 2645975149
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