Tag: Artificial Intelligence

Artificial intelligence and energy together could revolutionise the energy sector. How? What are the predictions for the future? 

Strategically integrated, artificial intelligence and energy could revolutionise the energy sector in every way, from optimising existing structures to innovating in crucial technological areas. In this article, we will analyse the current situation, experts’ predictions for the future and the challenges that this interaction will inevitably face. 

Artificial intelligence and energy: why is reflection necessary? 

Artificial intelligence and energy must be considered together, as two sides of the same coin, due to their dual and symbiotic relationship: AI needs energy, and therefore the energy sector needs the potential of AI to evolve and innovate in a context of constantly increasing demand.  

The relevance of the topic is such that the IEA (International Energy Agency), an intergovernmental organisation working for global energy security and the promotion of sustainable energy policies, published a report in April 2025 entitled ‘Energy and AI‘. In these 304 pages, the aim is to demonstrate to the world a very clear thesis: the revolutionary potential of artificial intelligence must be exploited to maximise innovation and efficiency in a strategic sector such as energy. This integration, says the IEA, is essential to optimise, rethink and renew a system that, day after day, must meet the growing needs of the population, industry and services.

Now that the reasons are clear, it is time to delve deeper to answer specific questions: how much do AI data centres consume – and will they consume in the future? How will demand be met? Furthermore, how can AI help the energy sector? What will be the main challenges? Let’s see how the IEA experts responded.

Why does artificial intelligence need the energy sector?

The answer to this question, as you might guess, is simple: because it consumes – a lot – and will consume more and more as it becomes more widespread in various areas of daily life. To put it another way, AI could represent a revolution comparable to the discovery of electricity, precisely because of its status as a general-purpose technology. Apparently, Wall Street is well aware of this, given that between the launch of ChatGPT in November 2022 and the end of 2024, approximately 65% of the growth in the market cap of the S&P 500 is attributable to companies linked to artificial intelligence. This percentage is roughly equivalent to $12 trillion (twelve thousand billion) – also worth noting is the interest in the Crypto AI category, as in the case of Grayscale. 

As in the most classic of circular dynamics, such a massive injection of capital has triggered an investment rush, with major tech companies planning to spend up to $300 billion on artificial intelligence-related assets, facilities and equipment in 2025 alone. Of course, much of this funding is absorbed by data centres, which are essential for the training and implementation of AI, but are extremely energy-intensive. 

How much do data centres consume?

Data centres, defined as a complex of servers and storage systems for data processing and storage, currently account for around 1.5% of global electricity consumption, or 415 TWh (terawatt hours): a data centre designed for AI, for example, can require the same amount of electricity as 100,000 average households, while those under construction – significantly larger – could be up to 20 times that amount. 

Looking ahead, from 2017 to today, data centres have increased their electricity consumption by 12%, which is four times faster than total global consumption. This means that if the planet Earth has increased its electricity demand by 3% since 2017, data centres have required four times that rate of growth. Needless to say, the most important driver of this increase is artificial intelligence, followed by digital services, which are also in high demand. In all this, the IEA reports that, in 2024, the top three global consumers will be the United States (with 45% of the total), followed by China (25%) and the European Union (15%).

So, if data centre consumption currently stands at 415 TWh, the IEA report estimates that this figure will double by 2030, reaching around 945 TWh, slightly more than the total consumption of Japan. As for projections for 2035, the report refers to a ‘scissors effect’, as it includes variables related to the development of efficient energy-saving solutions in its calculations. In any case, the range is from a minimum of 700 TWh to a maximum of 1,700 TWh. 

This incredible increase is linked both to the greater ‘physical presence’ of data centres around the world and to their intensified use, assuming that, in the future, AI will spread to every corner of the cities in which we live. In fact, in terms of consumption, the most significant impact is during the operating phase rather than during production or configuration: a latest-generation 3-nanometre chip requires approximately 2.3 MWh (megawatt hours) per wafer – the circular slice of silicon on which the circuits are manufactured – to be produced, 10 MWh to be configured and 80 MWh to operate during a five-year life cycle.  

How can this demand be met in the future?

The report answers in the only way possible, namely with a diversified range of energy sources. In particular, in the baseline scenario – obtained from an analysis of current conditions, without including optimistic or pessimistic variables – renewables and natural gas should drive this energy mix, with the former covering about half of demand (450 TWh) and the latter accounting for almost a quarter (175 TWh). Next comes nuclear energy, which, with the implementation of small modular reactors (SMRs), could contribute slightly less than natural gas. 

Let’s now shift our focus to the energy sector. 

Why does the energy sector need artificial intelligence?

Because, as is evident, artificial intelligence is capable of optimising every aspect of the energy sector: exploration, production, maintenance, safety and distribution. In short, applying AI to the energy sector, as we mentioned at the beginning of this article, could revolutionise it. Let’s look at some specific cases: 

AI and energy together in the oil and gas industry

The report informs us that in this area, the adoption of the winning combination of artificial intelligence and energy has occurred ahead of the average. The main uses relate to the optimisation of reservoir exploration and identification processes, the automation of hydrocarbon extraction activities – well management, flow control and fluid separation – but also everything related to safety and maintenance: leak detection, preventive maintenance and emission reduction. In the future, the IEA reports, this integration could translate into a 10% saving in operating costs in deep waters. 

Artificial intelligence in the electricity sector

In the field of electricity, the IEA report predicts that AI will play a key role in balancing networks, which are becoming increasingly digitised and decentralised – as is the case with rooftop solar panels. Specifically, AI could improve the forecasting and integration of renewable energy generation by reducing curtailment – forced reduction – and, therefore, emissions. In simple terms, this means that artificial intelligence, thanks to its ability to analyse endless series of data, would be able to make more accurate predictions about renewable energy production (which is influenced by the weather) and average demand. This would make it possible to integrate renewable energy with other energy sources in a more precise and intelligent way, avoiding unnecessary waste associated with the arbitrary blocking of excess electricity (curtailment). 

There is also an interesting issue related to increasing the efficiency of existing networks. In a nutshell, integrating AI would unlock up to 175 GW (gigawatts). How? Through the use of remote sensors and management tools capable of reading and processing huge amounts of data in real time. Currently, electricity grids – or transmission lines – carry a maximum amount of electricity based on static and conservative conditions, calculated with a very wide safety margin: during the summer, for example, air temperature and wind are measured conservatively to prevent excessive electrical flow from causing cables to melt or similar problems. The result is that, most of the time, networks operate at low capacity. With AI-based management, these conditions would change from static to dynamic – Dynamic Line Rating, DLR – and allow real-time control of the load capacity of the networks themselves, with positive effects on the amount of energy circulating.   

Finally, artificial intelligence applied to the electricity sector could make a concrete contribution to network fault detection and preventive maintenance of power plants. In the first case, by speeding up problem localisation operations, with a 30-50% reduction in outage duration. In the second, by optimising the identification of potential damage, giving advance warning of the need to replace crucial components, with estimated savings of $110 billion by 2035.

AI in industry, transport and building heating

To conclude this section, the report briefly touches on the three areas belonging to the macro-category of ‘end uses’, i.e. the uses to which energy is put after distribution to end users. With regard to industry, the IEA quantifies the benefits of implementing AI applications as savings equal to Mexico’s total consumption today. Then, in transport, it talks about cuts equivalent to the energy used by 120 million cars, thanks to traffic and route optimisation. Finally, AI could improve the management of heating systems in civil and non-civil buildings, with an expected reduction in electricity use of around 300 TWh – the amount produced by Australia and New Zealand in a year. 

Artificial intelligence and energy: innovations

Artificial intelligence can contribute significantly to energy innovation as it is capable of rapidly searching for molecules that can improve existing tools. Thanks to the combination of predictive and generative models and endless academic literature, AI exponentially accelerates the process of selecting candidates and creating suitable prototypes. In particular, four key areas would benefit from the potential of AI:

• Cement production, making the research and development of new mixtures more efficient and reducing the use of clinker, a highly polluting component that forms the basis of cement itself.
• The search for CO2 capture materials, such as MOFs (Metal Organic Frameworks), reduces energy consumption and costs associated with CCUS (Carbon Capture, Utilisation and Storage, the process of capturing CO2 for reuse or storage. 
• The design of catalysts for synthetic fuels, i.e. substances that accelerate chemical reactions to produce low-emission fuels. The difficulty in designing this type of catalyst lies in the infinite number of possible combinations between molecules, a process that AI can greatly accelerate. 
• Battery research and development, facilitating material testing, performance prediction, production optimisation and end-of-life management processes. 

What are the challenges of integrating AI and the energy sector?

The report concludes by presenting, as it should, the obstacles that this ambitious project will face. First, the IEA warns us that increasing digitalisation, while having positive implications for energy security, inevitably also brings with it specific risks, such as vulnerability to cyberattacks. A fundamental problem also concerns the security of energy supply chains: chips, as is well known, require large volumes of rare earths and critical minerals, which are concentrated in a few areas of the world – China controls 98% of gallium refining. A third issue relates to the decoupling of investment in data centres and investment in energy infrastructure, which is vital for the functioning of the system. Finally, there is the issue of the lack of digital skills and qualified personnel, coupled with poor dialogue between institutions, the tech sector and the energy sector. 

I don’t know about you, but after reading and analysing this report, we are fairly convinced that artificial intelligence will also rule in this sector: burdens and honours, risks and opportunities. But then again, nothing ventured, nothing gained.  

How to create images using artificial intelligence: Where do we stand? Discover all the steps in this comprehensive guide.

If you, too, have seen the images created by artificial intelligence – and if you haven’t, who knows where you live – your crevello will have ventured an argument like this. There was a time, not so long ago, when creating an image required pencils, brushes, cameras or, for the more modern, graphics tablets and hours of painstaking patience. Then, almost out of nowhere, generative artificial intelligence exploded. Suddenly, our social feeds, company presentations and even group chats were filled with dreamy, hyper-realistic and bizarre images, all spawned by an algorithm. “You want a Van Gogh-style astronaut cat eating ice cream on Mars? Give me two minutes.”

This new frontier of digital creativity has triggered a mixture of wonder and apprehension. On the one hand, the promise of democratising art, of giving anyone the power to visualise the impossible; on the other, the fear of a future where real artists, those in the flesh, end up begging robots. But before we panic or exclaim, let us try to understand how artificial intelligence creates images.

Creating images with artificial intelligence: what’s behind the magic?

Behind the apparent wizardry of an image that comes from a simple sentence, there is a concentration of technology that, until a few years ago, was the stuff of science fiction films. We are talking about machine learning and neural networks, i.e. software that attempts to imitate the functioning of the human brain. These systems are ‘trained’ on endless databases containing billions of existing images, each accompanied by a textual description.

The models most in vogue today, such as those based on ‘Diffusion’ architectures (such as Stable Diffusion, DALL-E 3, Midjourney), learn to associate words with visual concepts. In practice, they start from a digital ‘noise’, a kind of indistinct fog, and, guided by our textual input (the famous ‘prompt’), begin to ‘sculpt’ this noise, one small step at a time, until the required image emerges. Imagine a sculptor pulling a statue out of a shapeless block of marble, only the marble is digital, and the chisel is an algorithm that has seen more works of art than any living critic. The result? Sometimes a masterpiece, other times something that looks like something out of a Dali nightmare after a heavy dinner.

How to generate images with AI: instructions for use

If you think it is enough to type ‘cat’ to make artificial intelligence create the image of a purring feline from the screen, you will be disappointed. The art of dialoguing with these AIs, known by the somewhat pretentious Anglophone term prompt engineering, is a subtle discipline, somewhere between poetry and programming.

You have to be specific, almost pedantic. You want a ‘dog’? Fine, but what breed? What is it doing? Where is it? In what light? In what pictorial style? “A golden retriever puppy sleeping blissfully in a red velvet armchair, illuminated by warm afternoon light, Renaissance oil painting style”. There, now we’re getting somewhere. 

Then there are the negative prompts, or instructions on what NOT to do: “no double tails, please”, “avoid that plastic effect”, “I beg you, no more than five fingers on each hand!”. The process is iterative: you generate, observe the result, refine the prompt, regenerate, and so on, in a loop that can lead to the perfect image or to deciding that, perhaps, a hand-drawn picture was better. At first, it is easy to get digital abominations: that ‘cat on a bike’ might turn into a Lovecraftian tangle of fur and pedal metal. But with a little practice (and a lot of patience), you can begin to tame the algorithmic beast and start creating quality artificial intelligence (AI) images.

 Lights and shadows: the pros and cons of AI-generated images

Like any self-respecting technology, image-generative AI also brings with it a wealth of opportunities and a few skeletons in the cupboard. Here is a brief summary of what, at least in our opinion, are the pros and cons of this technological breakthrough.

Pros:

• Democratisation of creativity: anyone, even someone who draws like a three-year-old, can give visual form to their ideas. Need a logo on the fly? An illustration for a post? An inspiration for a tattoo? Ask and (maybe) you’ll get it;
• Speed and efficiency: for designers, creatives and marketers, it is a crazy tool for brainstorming, creating moodboards, concept art, and rapid prototypes. Hours of work condensed into a few minutes;
• New aesthetic horizons: AI can mix styles, invent perspectives, create images that a human might not conceive, opening up unprecedented art forms;
• Pure fun: let’s face it, asking the AI to draw absurd things is often hilarious;

Cons:

• The six-finger nightmare (and other amenities): the infamous ‘uncanny valley’ is always lurking. Hands with too many or too few fingers, faces that melt like wax, seasick perspectives, objects that defy the laws of physics. Sometimes, the results are so surreal that they themselves become an unintentional art form.
• The fair of the generic: with the ease of use, the risk is a rising tide of images that are aesthetically pleasing but devoid of soul, all a bit the same, a bit ‘Midjourney effect’. The world is now invaded by cyberpunk kittens with a variable (but hardly ever correct) number of legs.
• The crisis of originality: if everyone uses the same tools and maybe even similar prompts, don’t we risk a stylistic flattening?
• But is this art?: the debate is open and heated. If a machine ‘makes’ the work, is it still art? Who is the artist? Who writes the prompt, or the algorithm? My cousin, who until yesterday was only making memes of dubious quality, now calls himself ‘an international prompt artist’, complete with a portfolio on LinkedIn.

And from a philosophical point of view?

And here the matter gets serious, because the implications go far beyond the number of fingers. The first problem, which has long been central to the debate on artificial intelligence, not only when it is used to create images, is related to copyright and the question: whose image is generated? Of the user who wrote the prompt? Of the company that created the AI? Or is it a derivative of the myriad images used for training, many of which may be copyrighted? At the moment, it’s a legal Wild West. And what about the prompting ‘in the style of [famous living artist]’? Is it homage or theft?

Then there is the work-related issue. Will artificial intelligence destroy the market for illustrators, photographers, graphic designers, or just make it more productive? We like to be optimistic, imagining a world where AI is a powerful ‘creative assistant’, freeing humans from superficial tasks and allowing us to focus on the most valuable tasks.

Let us close with the two main ethical dilemmas. The first is frightening and concerns the ease with which false but realistic images can be created with intelligence. Photos of events that never happened, faces of people stuck on the bodies of others. The implications in terms of disinformation, manipulation of public opinion, and trust in sources are enormous. Distinguishing the true from the plausible will become an increasingly challenging task.

Finally, it must be emphasised that AIs are trained on data created by human beings. If this data contains prejudices (gender, ethnic, cultural), the AI will learn and replicate them, which may lead to the creation of stereotypical images or the exclusion of certain representations. The algorithm, in short, can be as racist or sexist as the societies that nurtured it.

In short, the possibility of creating images with artificial intelligence is certainly as revolutionary as the invention of photography or digital photo editing. As we are increasingly realising, AI is an incredibly powerful tool, capable of democratising creativity, accelerating production processes, but also raising profound questions about the nature of art, work and truth itself. Like any tool, its impact – beneficial or maleficent – will depend on how we choose to use it, adjust it and integrate it into our lives. It is neither a demon to be exorcised nor a magic wand that will solve every problem. It is, more prosaically, a powerful new set of digital crayons available to humanity. Get ready for a future where, in order to understand whether your friend’s holiday photo is real or ‘prompt’, you will need a trained eye, a second coffee and, perhaps, an honorary degree in the philosophy of perception. The good (and the bad) has just begun.

What are the five most popular crypto AI agents? Decentralised ChatGPT variants are also capable of handling money.

What are the most popular crypto AI agents? You may be familiar with ChatGPT, Gemini, Claude, and other artificial intelligence systems that we interact with daily. Now, imagine if these digital brains could not only write poetry or solve complex problems but also manage real money, invest, earn, and even spend cryptocurrencies. Sounds like science fiction? Not at all! Welcome to the world of crypto AI agents, an exciting new frontier that emerges from the convergence of two revolutionary technologies: cryptocurrencies and artificial intelligence.

In simple terms, we are discussing digital entities that can operate autonomously in decentralised financial markets, providing analyses and price forecasts. The most remarkable aspect is that these are not just bots following a fixed algorithm; they are designed to learn from their mistakes and adapt to changing market conditions, much like a human would.

At first glance, this might seem like an extreme simplification, and to some extent, it is. However, there’s no need to worry! In this article, we won’t dive into the theoretical explanations of what crypto AI agents are or how they function in detail—we’ve already covered that elsewhere. Today, we aim to get straight to the point: we will review the five most popular and interesting crypto AI agents, exploring what they do and why they have garnered so much attention.

The 5 most popular crypto AI agents

Virtual Protocol: the ‘factory’ of AI agents

Let’s start with an exciting introduction! Virtual Protocol is not just a single AI agent; it is a comprehensive platform, or as it refers to itself, an “AI agent company”—that allows users to create customised AI agents. Thanks to Virtual Protocol, once configured, these agents “come to life” and can begin to operate autonomously in the digital world. What does this mean? Imagine having the ability to “program” your digital assistant that can process cryptocurrency transactions, make decisions based on its past experiences or analysed data, and interact with its surroundings, whether it’s the blockchain or other platforms like social networks.

Most of the agents created through Virtual Protocol fall under the category of IP (Intellectual Property) agents, which can be described as true virtual personalities or digital influencers. A striking example is Luna, an agent who has gained immense popularity on TikTok, accumulating nearly one million followers through her engaging content. Additionally, there are functional agents, which are less focused on the social aspect and more oriented toward performing specific tasks to enhance the user experience on various platforms or services.

AIXBT: the oracle of X

If you are a crypto enthusiast and spend time in the community, you have likely encountered AIXBT. This platform stands out as one of the most popular and widely followed crypto AI agents. Built on the Virtual Protocol ‘agent factory’, AIXBT is described as a sentient agent with a clear primary purpose: to keep holders of its associated token informed by sharing market analysis, insights, and forecasts related to the crypto world.

These analyses are not arbitrary; they are the result of an ongoing process involving data collection, analysis, and interpretation. AIXBT has successfully amassed a substantial following, currently totalling around 500,000 followers. This success can be attributed to its ability to identify emerging market narratives and provide valuable information—referred to as alpha—that gives investors a competitive edge. The quality of AIXBT’s content is so high that even CoinGecko, a leading and trusted data analysis platform in the crypto sector, has chosen to integrate AIXBT’s analyses.

One small detail is not insignificant: the token linked to this agent has experienced moments of glory, reaching a market capitalisation of no less than $745 million at its peak.

Eliza OS: the first Venture Capital managed by AI

The concept behind Eliza OS, previously known as ai16z, is quite fascinating: envision a world where your investments not only work for you passively but do so intelligently, proactively, and completely automatically. This concept extends beyond traditional notions of compound interest or standard financial formulas. Instead, we are discussing a tokenised artificial intelligence built on the Solana blockchain, designed to generate returns through sophisticated and continuous trading activities.

In simple terms, Eliza OS can be described as a fully decentralised and automated venture capital fund that leverages AI to make informed financial decisions. It operates like a tireless financial advisor, constantly active and staying updated on the latest market trends. The Eliza OS-linked token saw extraordinary success, exceeding a remarkable $2.5 billion in capitalisation within just four months of its launch. However, it is important to note that the token’s price has since dropped significantly.

Hey Anon: GPT Chat for DeFi

The penultimate project in our roundup features a prominent figure in the Italian DeFi scene: Daniele Sesta. Hey Anon is a protocol created with a simple yet powerful objective: to significantly simplify interactions with the complex world of Decentralised Finance (DeFi).

It is a chatbot similar to ChatGPT, but specifically designed to interact directly with DeFi. You can give it instructions in natural language, connect your crypto wallet, and it will handle all the technical aspects for you. 

For example, if you have a certain amount of ETH and want to use it as collateral to secure a loan on Aave but aren’t sure where to start or find the process cumbersome, you can simply ask ‘Hey Anon’ to do it for you. However, there is a caveat: to utilise the services of this platform and issue commands to the chatbot, you need to hold a certain amount of the project’s native token, ANON.

Kaito: A Search Engine for Web3?

We conclude our list with Kaito, a platform designed to simplify access to and understanding of the vast amount of data within the Web3 universe. Staying informed about the ever-evolving crypto world can be challenging, given the constant influx of news, social media trends, discussions on Discord and Telegram, on-chain data, and the rapid emergence of new projects. Kaito aims to address this issue.

Utilising AI, Kaito collects, analyses, and presents essential information from a variety of sources, assisting users, investors, and developers in navigating this expansive landscape and making more informed decisions. It functions like an enhanced version of ‘Google Search,’ focused explicitly on cryptocurrencies and Web3. This tool promises to streamline the search for quality information, making it faster and more efficient.

That’s just a glimpse into the current state of the crypto AI agent landscape, which is rapidly evolving with new ideas and projects emerging daily. While it’s still early in this development phase—and, like all emerging technologies, it comes with challenges, risks, and a great deal of experimentation—one thing is clear: the combination of artificial intelligence and blockchain has the potential to create possibilities that, until recently, seemed like they belonged in science fiction novels.

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Grayscale has just announced its crypto AI fund. Find out what this innovative financial instrument consists of.

Grayscale has just announced its Decentralised AI Fund LLC. This brand-new investment fund will allow those who purchase it to gain exposure to the most important crypto protocols aiming to establish themselves in the artificial intelligence sector. 

What cryptos does this innovative fund consist of? What is Grayscale’s main goal, and what artificial intelligence problems could blockchain solve? Find out in this article.

Discover Crypto AI

Grayscale’s new crypto AI fund

Practically everyone knows Grayscale, mainly because it is the largest native crypto investment fund, the first to launch financial instruments on Ethereum and Bitcoin. For this reason, the news released in the past few hours is essential, given the ability of the team of this cutting-edge financial player to intercept new trends. 

The main problem with artificial intelligence, at least according to Grayscale, concerns the centralisation of the companies that control it. 

Few and far between are those who can offer products that can reach the masses, mainly due to the enormous amount of data they hold. As a solution to this problem, various decentralised AI protocols have emerged, aiming to make their processes even more innovative and intelligent. In particular, blockchain technology makes it possible to distribute the ownership and governance of AI services, thereby increasing transparency.

The cryptocurrencies that make up

For now, the information at our disposal tells us that The Grayscale Decentralised AI Fund will self-rebalance every quarter and will accommodate the following basket of cryptocurrencies:

• Near (NEAR): 32.99%.
• Render (RNDR): 24.86%.
• Filecoin (FIL): 30.59%;
• Bittensor (TAO): 2.92%.
• Livepeer (LPT): 8.64%.

Buy NEAR, RNDR and FIL

The team has yet to comment on possible future additions, but other cryptos will likely be added over time. Why did Grayscale choose these? Well, because they represent the three main categories of crypto AI around today:

• Protocols that are building decentralised artificial intelligence services;
• Projects that seek to solve the main problems encountered by AI platforms;
• Infrastructure networks and resources required for technology development. For example, decentralised marketplaces for data storage, or those for exchanging GPU computing power and graphics rendering.

To conclude, we can quote the words of Rayhaneh Sharif-Askary, Head of Product & Research at Grayscale, who was mentioned in the press release through which the announcement was made. “The rise of these disruptive technologies has created exciting opportunities for investors, and we believe our crypto AI fund is a great way to invest in this emerging sector. Blockchain-based AI protocols embody the principles of decentralisation, accessibility and transparency and can potentially mitigate the fundamental risks emerging from the proliferation of this technology.”

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