AI Action Summit Preview

The February 10-11 AI Action Summit in Paris, will address, among other things, how to equitably distribute AI’s benefits globally, concerns about control of AI by a few dominant players, the role of opensource and building responsible and trustworthy AI.

Attendees at the summit, which will gather nearly 100 nations and be co-hosted by the French and Indian governments, will include Chinese Vice Premier Zhang Guoqing and U.S. Vice-President J.D. Vance.

The Summit hopes to move the conversation on from fear about the harm the technology might do, the focus of the inaugural AI Safety Summit hosted by the UK government at Bletchley Park in November 2023 in the UK, and instead emphasize how general-purpose AI has immense potential for education, medical applications, research advances in fields such as chemistry, biology, or physics, and generally increased prosperity.

But AI’s dark side will nonetheless be top of mind.

Governments, leading AI companies, civil society groups and experts gathered for the AI Action Summit will be presented with an International AI Safety Report 2025 report spearheaded by Turing prize winner Yoshio Bengio and compiled with the help of expert representatives nominated by 30 countries, the OECD, the EU, and the UN, as well as several other world-leading experts. The goal of the report is to provide a shared scientific, evidence-based foundation for discussions about the risks.

The document, which was commissioned after the 2023 global AI Safety Summit and became publicly available last week, covers numerous threats ranging from already established harms such as bias, scams, extortion, psychological manipulation, generation of non-consensual intimate imagery and child sexual abuse material, deepfakes and targeted sabotage of individuals and organizations, to future threats such as large-scale labor market impacts, AI-enabled biological attacks, and society losing control over Artificial General Intelligence (AGI).

Managing this toxic brew is complicated by conflicting approaches to risk management. While France, on February 3, announced the creation of the equivalent of an AI Safety Institute (INESIA), one of U.S. President Donald J. Trump’s first acts in office was to rescind an Executive Order issued by the previous administration on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.

The danger is that even if Europe, and the rest of the world, opt for leveraging AI in a “responsible and sustainable manner” the U.S. government and U.S. companies will see a recent breakthrough by China’s DeepSeek, which has demonstrated AI reasoning models that appear to be on par with U.S. companies OpenAI and Anthropic, at significantly lower cost, as a threat that needs to be countered in a no-holds barred race.

As model capabilities continue to advance amid mounting global instability, fueling the race between the U.S. and China, but also opening new opportunities for startups around the world working on alternative opensource models, the need for international collaboration on a series of other risks including the global AI R&D and compute divide, market concentration, environmental risks, privacy, copyright violations and safeguarding intellectual property (IP), has never been higher.

Global AI R&D And Compute Divide

General-purpose AI R&D is currently concentrated in a few Western countries and China and this ‘AI divide’ has the potential to increase much of the world’s dependence on this small set of countries, says the International AI Safety 2025 report. Some experts also expect it to contribute to global inequality. Among other things it stems from differing levels of access to the very expensive compute needed to develop general-purpose AI: most low- and middle-income countries have significantly less access to compute than high-income countries/

Compute is, in fact, slated to become “the foundation of next-generation economic growth and influence, shaping economic developments, as well as the future of sovereign power and international influence,” according to a recent Tony Blair Institute for Global Change report. It notes that compute infrastructure – and the difference in its availability from country to country – risks becoming the basis of a new digital divide.”

Last year TBI published a report that measured countries’ capabilities across the entire compute ecosystem, including talent, energy, governance initiatives, available training and technical partnerships, to help governments understand their compute capacity. The 2024 report, released November 18, underscores how the gap between nations has widened

The U.S. has some well publicized major advantages: U.S. tech companies dominate large language models globally and U.S. AI chipmaker Nvidia has become the leading beneficiary, with its Graphics Processing Units (GPUs) widely used by tech giants like Microsoft, Alphabet, Amazon, Meta, and OpenAI for AI applications. In 2024 Nvidia’s market cap reached $3.314 Trillion USD, making it the world’s second most valuable company.

The latest TBI report found that in addition to these advantages the U.S. built more data-center capacity in 2023 than the rest of the world combined, excluding China.

Market Concentration

“We are living in a world where the value of AI is very concentrated in the hands of a few players that have the data, the infrastructure, the energy and the talent,” Clara Chappaz, France’s Minister Delegate for Artificial Intelligence and Digital Technology, cautioned during a panel in Davos moderated by The Innovator’s Editor-in-Chief. “

The real power struggle isn’t geopolitical – it’s architectural, argues Sangeet Paul Choudary, a best-selling business book author and advisor to Fortune 500 companies, says in the latest edition of his newsletter. It’s about who gets to write the standards and control the infrastructure everyone else builds on, he says.

He calls it sandwich economics: one ‘player’ specifies an economic framework and imposes it on the rest of the industry, or in this case, the rest of the global economy.“Over the past couple of decades, the most significant innovation by Big Tech hasn’t merely been the creation of the world’s leading search engine, social network, e-commerce platform, or cloud computing service,” he says, “it is the ‘sandwich’ they create and impose on entire industries, forcing these industries to re-organize within it. And, with that, they sandwich the rest of the economy in between – squeezing power and profits from them, he says.

“The ones in the lead will rewrite the rules of global competition and collaboration,” Choudary says in his post. “The other will be left to adopt AI on someone else’s terms, inheriting its priorities and policies along the way.’’

In an interview with The Innovator AI scale-up iGenius founder and CEO Uljan Sharka, an AI Action Summit attendee, says entrepreneurs and governments need to prevent that from happening. “AI is the most powerful technology ever built,” Sharka says. “If we centralize it, we risk subjecting ourselves to modern dictatorship. The few tech companies that own it will become the government of the future. They will be the creators and everyone else a user, or a slave. I am motivated by building tech that enables digital equality, allowing everyone to compete, with the goal of building things beyond our imagination.”

Europe is pinning its hopes on European entrepreneurs like Italy-based iGenuis that are working on opensource and trustworthy AI.

iGenius.ai, which is valued at more than $1 billion, develops open source large language models that respect strict data security rules to ensure that corporate clients can safeguard their intellectual property. The company’s AI platform is used by some of the world’s largest organizations including financial services company Allianz, electric utility company Enel, Intesa Sanpaolo, one of the top banking groups in Europe, global shipbuilding group Fincantieri and several governments, including Italy’s.

“We are attracting customers in the U.S.,” says Sharka, co-founder and CEO of the Italian scale-up. “We tell them if you want the best models go to Silicon Valley. If you want good performance and large language models you can trust you can talk to us.” [See The Innovator’s Startup Of The Week story about iGenius).

“Europe is well-poised to win a GenAI-led digital Renaissance by offering an alternative to American and Chinese models that embeds European values and builds trust,” says Sharka.” It is a different way of building tech, and it is an advantage,” he says.

Like Sharka, Arthur Mensch, Mistral AI’s co-founder, is highlighting a need for a European alternative to Chinese and American offerings, telling Reuters that his goal is to make AI “more open and more accessible to everyone”.

Mistral AI this week rolled out its open source Le Chat assistant on the app store, claiming that it is powered by the world’s fastest inference engines, responding with up to 1,000 words per second. It also announced a strategic partnership with French transnational company, aimed at transforming the management and monitoring of industrial sites for water management, waste recycling and local energy production. to the ecological transformation. “This partnership marks a major step forward in industrial management,” Veolia said in a press release. “Thanks to the integration of Mistral’s LLM with Veolia’s data and knowledge base, it will now be possible to have a conversation with the plant, a world first.” By integrating the power of generative artificial intelligence, Veolia and Mistral AI are enabling employees and stakeholders to co-pilot water, waste and energy plants through interactive discussions. The companies said this represents a further step towards the realization of Industry 5.0 and the emergence of augmented employees, where technology directly supports human expertise.

Mistral AI also announced this week that it is expanding its partnership with Stellantis, the world’s fourth-largest carmaker; that it has formed a partnership with the French jobs agency; and that has more deals in the pipeline with other European authorities.”

French government official Philippe Huberdeau, Secretary General of Scale-Up Europe, sees recent market developments as reason for optimism. “There is clearly lot of space between Stargate [a U.S. high-profile artificial intelligence infrastructure project, being backed by OpenAI, SoftBank and Oracle, which aims to spend $500 billion on new compute infrastructure] and DeepSeek for a third European way to leverage this industrial revolution in a responsible and sustainable manner,” he said in a LinkedIn post in the run-up to the summit.

Environmental Risks

Growing compute use in general-purpose AI development and deployment has rapidly increased the amounts of energy, water, and raw material consumed in building and operating the necessary compute infrastructure, posing environmental risks, says the International AI Safety Report 2025. Indeed, a recent research paper from a Capgemini R&D team highlights that large generative AI models consume 4,600 times more energy than traditional models, with AI-related electricity usage potentially increasing 24.4 times in the most extreme scenario by 2030. Mitigating this environmental impact in the coming years will require a coordinated effort from all stakeholders across the AI value chain. In the run-up to the AI Action Summit the AI and Society Institute, the Ecole Normal Superierie (ENS-PLS) and the ENS Foundation, with the support of Cap Gemini, launched an Observatory dedicated to analyzing and mitigating the environmental impacts of AI at all stages of its lifecycle (training, adjustment, inference and end-of-life). The new Observatory aims to establish a solid, shared methodology to encourage sustainable AI usage.

Privacy Risks


The International AI Safety Report 2025 lists violation of privacy as one of the general-purpose AI systemic risks. For example, sensitive information that was in the training data can leak unintentionally when a user interacts with the system. In addition, when users share sensitive information with the system, this information can also leak. But general-purpose AI can also facilitate deliberate violations of privacy, for example if malicious actors use AI to infer sensitive information about specific individuals from large amounts of data.

Copyright Infringements and IP Risks

Copyright infringements also pose systemic risks, according to the International AI Safety Report 2025. General-purpose AI both learns from and creates works of creative expression, challenging traditional systems of data consent, compensation, and control and threatening the livelihoods of content creators ranging from journalists to authors, artists and musicians. Data collection and content generation can implicate a variety of data rights laws, which vary across jurisdictions Given the legal uncertainty around data collection practices, AI companies are sharing less information about the data they use, complicating efforts to resolve the way copyright will be handled going forward.

There are additionally significant intellectual property risks for large corporations. Approximately 80% of the most valuable enterprise data -including personal information, financial transactions, trade secrets, and intellectual property -cannot be exported and fine-tuned to centralized AI models and/or open models with a limited license, explains iGenius’ Sharka, This is especially true of generative AI, which merges data and intellectual property irreversible. For example, financial institutions sharing sensitive data with centralized LLMs can lead to potential data breaches or misuse, which can expose proprietary trading strategies, signal market intentions, and enable market manipulation, he says.

Choosing Between OpenAI And AI That Is Open

Will companies like OpenAI control the future or can an AI that is open enable trustworthy, innovative, and equitable outcomes? That is a question that the participants in the AI Action Summit will ultimately have to address.

“Embracing openness in AI is non-negotiable if we are to build trust and safety; it fosters transparency, accountability, and inclusive collaboration,” said a statement issued Feb. 4 by Mozilla. The statement followed a meeting in Paris in the lead up to the AI Action Summit, organized by Mozilla, Foundation Abeona, École Normale Supérieure (ENS) and the Columbia Institute of Global Politics whicih brought together a diverse group of AI experts, academics, civil society, regulators and business leaders in Paris to discuss openness, a topic it says is increasingly central to the future of AI.

“ Openness must extend beyond software to broader access to the full AI stack, including data and infrastructure, with a governance that safeguards public interest and prevents monopolization,” says the Mozilla statement. “ If AI is to advance competition, innovation, language, research, culture and creativity for the global majority of people, then an evidence-based approach to the benefits of openness, particularly when it comes to proven economic benefits, is essential for driving this agenda forward.”

Mozilla said the group that met in Paris would push the following recommendations for policymakers at the AI Action Summit:Diversify AI Development: Policymakers should seek to diversify the AI ecosystem, ensuring that it is not dominated by a few large corporations in order to foster more equitable access to AI technologies and reduce monopolistic control. This should be approached holistically, looking at everything from procurement to compute strategies.
Support Infrastructure and Data Accessibility: There is an urgent need to invest in AI infrastructure, including access to data and compute power, in a way that does not exacerbate existing inequalities. Policymakers should prioritize distribution of resources to ensure that smaller actors, especially those outside major tech hubs, are not locked out of AI development.
Understand openness as central to achieving AI that serves the public interest. One of the official tracks of the Paris AI Action Summit is Public Interest AI. Increasingly, openness should be deployed as a main route to truly publicly interested AI.
Openness should be an explicit EU policy goal: As one of the furthest along in AI regulatory frameworks the EU will continue to be a testbed for many of the big questions in AI policy. The EU should adopt an explicit focus on promoting openness in AI as a policy goal


The Choices Ahead



The future of general-purpose AI is uncertain, with a wide range of trajectories appearing possible even in the near term including both very positive and very negative outcomes, says the International AI Safety Report 2025. But nothing about the future of general-purpose AI is inevitable. “How general-purpose AI gets developed and by whom, which problems it gets designed to solve, whether societies will be able to reap general-purpose AI’s full economic potential, who benefits from it, the types of risks we expose ourselves to, and how much we invest into research to manage risks – these and many other questions depend on the choices that societies and governments make today and in the future to shape the development of general-purpose AI, says the report. “AI does not happen to us: choices made by people determine its future.

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Cybersecurity Outlook 2024: Mind The Gaps

More than 45 countries will hold elections this year to determine who governs more than 50% of the world’s GDP. With the proliferation of new technologies like Generative AI and their use by cyber criminals becoming more widespread, safeguarding the integrity and fairness of the election process will be a top priority for cybersecurity professionals, says a new World Economic Forum report. Geopolitical instability will also be top of mind as attacks against critical infrastructure and elements of supply chains, such as ships on the Red Sea, shift the risk landscape and threaten to have a macro impact.

These threats are not the only ones impacting the cybersecurity outlook in 2024. The increasingly stark divide between cyber resilient organizations and those that are less prepared has emerged as a key risk, says the report, which was compiled by the Forum, in collaboration with Accenture.

While large organizations have made gains in cyber resilience, small and medium sized businesses have shown a notable decline. “The healthy middle has shrunk from 67% in 2022 to around 36%,” Akshay Joshi, Head of Industry and Partnerships for the Forum’s Centre for Cybersecurity, said in an interview during the Forum’s annual meeting in Davos January 15-19. “Those that do not have the resources to invest are feeling the pain even more than before.”

The drastic drop in cyber resilience of the small companies that form the backbone of many economies threatens the integrity of the entire cyber ecosystem, says the report. Indeed, 90% of cyber leaders who attended the Forum’s Annual Meeting on Cybersecurity believe that this inequity requires urgent action.

Some 41% of the organizations surveyed by the Forum that had suffered a material incident in the past 12 months said it was caused by a third party and a 2023 report from SecurityScorecard and the Cyentia Institute found that “98% of organizations have a relationship with at least one third party that has experienced a breach in the last two years.”

These breaches are unlikely to disappear anytime soon, according to a Forum survey of executives last June and November. Some 64% of executives who believe their organizations are cyber ready say they don’t have an adequate understanding of their supply chain cyber vulnerabilities, increasing their own chances of a cyberattack.

What’s more among ill-prepared small organizations– which are often unable to prevent critical operational disruption from an incident and can incur disproportionate financial loss to recover –only 25% carry cyber insurance. That’s three times less likely than the largest organizations by revenue, which report a 75% cyber-insurance adoption rate. As the prices of cyber insurance continues to rise exponentially, the expectation is that this gap will widen in parallel, leaving smaller organizations with even fewer options to reduce their risk and keep their businesses from collapsing, the report says.

The growing inequity involves not just companies but countries and mirrors other global development indicators, says the report. Latin America and Africa reported the highest number of insufficiently cyber-resilient organizations, while North America and Europe reported the lowest number, the report says.

The technological divide between organizations and nations that can adequately handle cyberattacks poses both a threat to the entire ecosystem and outsized risks to those that are already vulnerable, says the report.

What is a needed, says an executive at financial services firm Sun Life who is quoted in the report, is to “design risk-appropriate, affordable and fit-for-use cyber-resilience architectures for large multinational and SMES alike.”

The Technology Gap

However, the report notes that technologies used for cybersecurity are becoming so sophisticated that even if they could afford them SMEs might not be able to operate them.

Emerging technology is becoming available more widely and far faster than in the past but is not distributed evenly. For example, large corporates are rapidly embracing the use of generative AI.

Nearly half of leaders surveyed by the Forum say they believe generative AI will have the most significant impact on cyber security in the next two years, says Joshi. While 56% of leaders surveyed said that generative AI will advantage cyber attackers in the next two years, helping them to craft better phishing emails, spread disinformation and improve their malware, it is also expected to help corporates improve cybersecurity as Generative AI Large Language Models (LLMs) can be used as a way of automating or assisting analysts with threat-hunting, says Joshi. “Long term the opportunities outweigh the risks,” he says. But using sophisticated new technologies requires the right people with the right skill sets and they are in short supply.

The Skills Gap

There is a massive shortage of cybersecurity professionals, says Joshi. More than 600,000 cybersecurity jobs remain unfilled world-wide and the vacancies are increasing.

In 2022 6% of leaders reported that they were missing the skills and people they need to respond to a cyber incident. In 2023, this doubled to 12%. This year, when asked whether their organizations had the skills needed to accomplish cyber objectives 20% said they do not, according to the Forum report.

Here, too ,the inequity gap is widening: 31% of leaders from the smallest organizations by revenue reported they are missing critical people and skills; yet only 11% of leaders from the largest organizations said the same, according to the Forum report.

Forum research indicates that by 2027 44% of workers’ core skills will be disrupted because technology is moving faster than companies can design and scale their training. This is true in cybersecurity, where the talent gap continues to pose very real challenges across public and private industries. To address this organizations must tap into new talent pools and provide employees with training.

To that end the Forum has launched an initiative called “Bridging The Cyber Skills Gap.” “Over 50 organizations are working on this,” says Joshi. The challenge for organizations is to identify employees that can be upskilled and begin careers in cybersecurity. “We need to find the right incentives and create the right narrative around cybersecurity that inspires people to join,” he says.

Building Ecosystem Resilience

Building systemic cyber resilience will depend on the ability of countries and companies to scale their upskilling efforts, the quantity and quality of industry collaborations, the effectiveness and clarity of regulations, the maturity and accessibility of the cyber insurance market and the extent to which organizations understand cyber risk coming from their own supply chains and third-party relationships, says the Forum report. However, only 23% of leaders surveyed are optimistic that industry and ecosystem collaboration will significantly improve in the next two years.


Action is needed to change the current trajectory, says the report, as the struggle to maintain high-quality or even adequate cyber resilience is fast becoming a zero-sum game. If companies and governments don’t mind the gaps, says the report, the interconnection of the digital economy makes it inevitable that the negative effects will compound, affecting everyone.

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Robots With Physical Intelligence

If Physical Intelligence, an AI start-up seeking to create “brains” for a wide variety of robots, has its way, people will be able to ask robots to perform any task they want.

The company, which is joining the race to bring general-purpose AI into the physical world, announced November 4 that it had raised $400 million, in a financing round led by Jeff Bezos, Amazon’s executive chairman, and the venture capital firms Thrive Capital and Lux Capital. Other investors include OpenAI, Redpoint Ventures and Bond. The fund-raising valued the company, which was launched this year, at $2.4 billion, not including the new investment.

It is the latest example of how the prospects for robots that help with everything from household chores to handling hazardous waste have improved as progress in artificial intelligence accelerates and investment in the sector grows faster than anticipated. The total addressable market for humanoid robots is projected to reach $38 billion by 2035, up more than sixfold from a previous projection of $6 billion, according to a Goldman Sachs report.

Over the past eight months, Physical Intelligence has developed a general-purpose robot foundation model that it calls π0 (pi-zero). “We believe this is a first step toward our long-term goal of developing artificial physical intelligence, so that users can simply ask robots to perform any task they want, just like they can ask large language models (LLMs) and chatbot assistants,” the company said in a blog posting. “Like LLMs our model is trained on broad and diverse data and can follow various text instructions. Unlike LLMs, it spans images, text, and actions and acquires physical intelligence by training on embodied experience from robots, learning to directly output low-level motor commands via a novel architecture. It can control a variety of different robots and can either be prompted to carry out the desired task or fine-tuned to specialize it to challenging application scenarios.”

Building such a model requires a huge amount of data on how to operate in the real world. Those information sets largely do not exist, compelling the company to compile its own. Its work has been aided by big leaps in A.I. models that can interpret visual data.

Among the company’s co-founders are Karol Hausman, a former robotics scientist at Google; Sergey Levine, a professor at the University of California, Berkeley; and Lachy Groom, an investor and former executive at the payments giant Stripe.

In its blog posting Physical Intelligence explained the challenge and its vision: “The past decade witnessed practically useful AI assistants, AI systems that can generate photorealistic images and videos, and even models that can predict the structure of proteins,” it said in its blog. “ To paraphrase Moravec’s paradox, winning a game of chess or discovering a new drug represent “easy” problems for AI to solve, but folding a shirt or cleaning up a table requires solving some of the most difficult engineering problems ever conceived. To build AI systems that have the kind of physically situated versatility that people possess, we need a new approach — we need to make AI systems embodied so that they can acquire physical intelligence.”

Embedded in its blog posting are videos of robots folding laundry, clearing a table and more.

Industry investors say they are not surprised OpenAI backed Physical Intelligence. While most of OpenAI’s investments go to enterprise software players, some of its biggest investments have been in physical-world applications for AI, says market research firm CBInsights. OpenAI has invested in humanoid robotics developers Figure (which raised a $675 million Series B in February) and 1X (which raised a $23.5M Series A extension in March 2023). Both have partnered with OpenAI to integrate AI into their robots to enable reasoning, learning, and even language. Meanwhile, OpenAI just hired the former head of Meta’s AR glasses initiative Orion, Caitlin Kalinowski, to lead robotics and consumer hardware at OpenAI, with an initial focus on robotics.

Tesla, robotics company Boston Dynamics and Sanctuary AI are among others vying to build human-like intelligence in general-purpose robots. That’s not all. Earlier this year Nvidia, the U.S. maker of advanced AI chips, systems, and software, announced GROOT, a foundation model for humanoid robots.

Robots powered by GROOT (short for “Generalist Robot 00 Technology) will be designed to understand natural language and emulate movements by observing human actions—quickly learning coordination, dexterity, and other skills to navigate, adapt and interact with the real world, Nvidia said in its March 18 announcement. The new platform consists of a computer system that will power the robot and AI, plus a package of software including GenAI and other tools needed to build robots that are human-like, the company said at its annual developer conference.

“Building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today,” Jensen Huang, founder and CEO of Nvidia said in a statement at the time. “The enabling technologies are coming together for leading roboticists around the world to take giant leaps towards artificial general robotics.”

IN OTHER NEWS THIS WEEK

FINANCIAL SERVICES

UBS Pilots Blockchain-Based Payment System



Finextra reports that UBS has successfully piloted a blockchain-based payment system, UBS Digital Cash, designed to improve the speed and efficiency of cross-border transactions for corporate and institutional clients. In the pilot, transactions with multinational clients and banks were successfully carried out, including domestic transactions within Switzerland and cross-border payments in US dollars, Swiss francs, Euros and Chinese yuan. In addition, the pilot also included the transfer of liquidity between various UBS companies. A key aim of the program is to improve the view of liquidity positions for corporate clients, which can be obscured due to delays in settlement timeframes. With UBS Digital Cash, the bank says companies should be able to manage intraday-liquidity and adjust liquidity buffers on their accounts more easily in the future, thanks to greater visibility of their total cash positions.

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Creatives And The News Media Escalate Their Copyright Battle With AI Companies

Creatives and the news media escalated their battles with tech companies this week over the use of copyrighted content to build and operate AI systems.

At press time 20,000 creatives, including some famous people in the fields of literature, music, film, theater and television, had signed a statement issued this week that expresses growing concerns over the unauthorized use of copyrighted works to train generative AI models.

“The unlicensed use of creative works for training generative AI is a major, unjust threat to the livelihoods of the people behind those works, and must not be permitted,” said the one-sentence statement published by Fairly Trained, an advocacy group founded by Ed Newton-Rex, a British composer and a former head of audio at tech firm Stability AI.

“There are three key resources that generative AI companies need to build AI models: people, compute, and data, Newton-Rex told The Guardian. “They spend vast sums on the first two – sometimes a million dollars per engineer, and up to a billion dollars per model. But they expect to take the third – training data – for free.”

The news media is making a similar argument. Wall Street Journal parent Dow Jones and the New York Post filed a lawsuit in the southern district of New York against Perplexity AI on October 21, claiming the artificial intelligence startup engages in a “massive amount of illegal copying” of their copyrighted work without authorization or compensation.

Perplexity, a scale-up. uses generative artificial intelligence to power a search engine that aims to compete with Google’s. The news organizations allege Perplexity’s AI-generated “answer machine” has ingested its copyrighted news stories, analysis and opinion in an internal database used to generate responses to users’ questions. Perplexity formulates its responses in a way that, at times, reproduces the content verbatim, the news organizations claim. The suit alleges these actions constitute an unlawful copyright infringement.

“This suit is brought by news publishers who seek redress for Perplexity’s brazen scheme to compete for readers while simultaneously freeriding on the valuable content the publishers produce,” according to the lawsuit.

With its lawsuit, News Corp is joining the ranks of multiple publishers that have sued AI companies for copyright infringement over their use of content without authorization, both to train algorithms and to generate summaries of real-time information.

Earlier this month, The New York Times sent Perplexity a “cease and desist” notice demanding it to stop using the newspaper’s content for generative AI purposes.

Perplexity has also faced accusations from media organizations such as Forbes and Wired for plagiarizing their content but has since launched a revenue-sharing program to address some concerns put forward by publishers.

On October 23, two days after the Dow Jones and the Post filed their law suit, Perplexity CEO and founder Aravind Srinivas appeared at a Wall Street Journal tech conference . He rejected a licensing deal but said Perplexity was ready to share advertising revenue with publishers, a pool of money he said would grow over time. (Perplexity is expected to launchits advertising program later this month). Srinivas compared it to the revenue splits that music-streaming service Spotify offers artists. Perplexity at the end of July announced partnerships along those lines with a handful of publishers, including Time and Fortune.

Some publishers are signing licensing agreements with AI companies open to paying for content, although the sides often disagree over the value of the materials. Many AI developers argue they have broken no laws in accessing them for free.

In May, News Corp announced it had struck a multi-year partnership with OpenAI. Robert Thomson, Chief Executive of News Corp, applauded the tech company for understanding “that integrity and creativity are essential” to realize the potential of artificial intelligence.

Every time there is a technological disruption there is pressure to rewrite the rules and rebalance the intellectual property system, Antony Taubman, the former Director, Intellectual Property, Government Procurement & Competition Division of the World Trade Organization (WTO) told The Innovator in an interview last month about AI and copyright. “It was the case with the introduction of photography, sound recording, the Internet and digital music, he says. “The reality is that the rule makers will never catch up and anticipate the changes. With AI we are seeing that in spades now.”


It is a natural cyclical upheaval of the IP system, he says, “but change never happened spontaneously. It always results in hard core litigation that tests the boundaries of the existing system.

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Agentic AI: Artificial Intelligence’s Next Wave

Earlier this month Open AI made headlines when it introduced “Swarm,” an experimental framework designed to coordinate networks of AI agents. Though not an official product, Swarm provides developers with a blueprint for creating AI systems capable of autonomous collaboration on complex tasks.

What most people don’t realize is that teams of AI agents- known as Agentic AI- already have the ability to take over entire departments, replacing – and outpacing -white collar workers, says Simon Torrance, the London-based founder and CEO of two advisory firms: ‘AI Risk’ – a research, strategy and innovation network focused on developing and deploying AI effectively and safely -and ‘Embedded Finance & Insurance Strategies’ , which helps leaders across multiple sectors leverage fintech and insurtech to create new value and growth.

Torrance cites the following example: A year ago the entire operations team of a small European insurance brokerage left the company after being poached by a competitor. The CEO asked a friend, a successful insurtech entrepreneur and computer scientist who had just sold his predictive analytics business for advice. The friend- who prefers to remain anonymous for now – had some spare time to help and decided to try something radically new: re-designing the whole operations function using AI alone, with tools that already existed and were readily available.

While the company used some freelancers and other staff as stop gap measures the friend analyzed all the job tasks and workflows and, within three months, created a new ‘team’ comprised exclusively of AI agents that took on the roles of a commercial manager, an actuarial and underwriting function, an accountant, customer care managers and IT staff for the insurance brokerage. The brokerage’s data was not prepared for AI but many of these roles and processes are quite generic and Large Language Models (LLMs) could replicate them quite easily, says Torrance. That said, the friend in charge of the project had to very carefully map out the processes and then use other human colleagues and LLMs to enhance and develop them.

Once they were unleashed the Agentic AI outperformed its claims ratio objective by a factor of two, says Torrance.

Operating expenses plus claims costs typically determine net profit in the insurance industry, and human salaries, benefits and payroll taxes often make up around 65% of total operating expenses for a broker. By replacing humans and improving the claims ratio the AI team reduced those costs for the European brokerage to zero, says Torrance.

Typically, insurance underwriting generates a net profit (premiums minus claims costs and operating expenses) of around 5% in a good year. The Agentic AI team helped to generate insurance net profits of roughly 45% which was “completely unheard of and also unethical,” says Torrance, so tweaks had to be made.

Ethical, legal and safety guardrails clearly need to be put into place, but the European brokerage’s results give a glimpse of what is possible now, he says. The potential upside for enterprise is so great that Torrance and the computer scientist that built the Agentic agents for the insurance brokerage are in the process of creating a company that will offer corporates a no code product to create and manage teams of digital workers that are capable of autonomous decision-making, executing complex tasks with minimal human intervention, collaborating with other systems and people and dynamically adapting to changing conditions.

“Companies will be able to increase profitability by hiring an almost infinite number of ‘assistants’ and even workers at relatively zero cost” says Torrance.

For example, recently, Nvidia CEO Jensen Huang shared his vision of a future where, by roughly 2030, his company employs 50,000 human workers (up from 30,000 today) aided by 100 million ‘AI assistants’, notes Torrance. “In terms of ‘AI agents or ‘digital co-workers’ undertaking knowledge worker tasks end-to-end either independently or as parts of hybrid human-digital teams I would say that Nvidia could increase its ‘workforce’ (humans + digital) by 50%. So, by 2030, it might have 50,000 human workers, 25,000 digital workers (autonomous agentic workers) and 100 million AI assistants. That’s for a super tech company. Normal companies could perhaps increase their ‘headcount’ by 10 or 20%, without incurring the costs associated with human employees.”

“The digital Agentic workers are often better than human workers because they are not constrained by human biases about what’s possible or not, and when you give them a task they can keep going, they don’t need to take time to take lunch, drive home or sleep,” says Torrance. “What is key here is that we are not talking about single agents doing one thing. These workers are not assistants for humans. Agentic AI is made up of a diverse set of workers that can collaborate to get things done.”

Agentic AI will have a big impact on enterprise because it directly impacts three of the foundational pillars of a corporate’s competitive advantage: operational efficiency, scalability, and agility in decision-making, says Torrance.

Gartner predicts by 2028, 33% of enterprise software applications will include Agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. The firm has named it a top strategic technology trend in 2025.

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Green Energy Needs To Develop A Compelling Business Case

When Saudi Aramco CEO Amin Nasser said last March that gas and oil are here to stay, he was stating an inconvenient truth.

“We should abandon the fantasy of phasing out oil and gas and instead invest in them adequately, reflecting realistic demand assumptions,” the CEO said to applause from the audience during a global energy conference in Houston.

Despite the world pumping more than $9.5 trillion into the energy transition over the past two decades, green alternatives have been unable to displace hydrocarbons at scale.

Taking a broader perspective, the energy transition is, in fact, stalling, according to the 14th annual edition of the World Economic Forum’s report, Fostering Effective Energy Transition 2024,. The report, published in collaboration with Accenture uses the Energy Transition Index (ETI) to benchmark 120 countries on the performance of their current energy systems that uses 46 indicators covering energy sustainability, security and equity.

While 107 out of 120 countries have progressed on the ETI over the last decade, there has been a slowdown in the transition due to economic and geopolitical volatilities in recent years that have impacted interconnected global energy systems and value chains. The higher energy prices in many markets have hit vulnerable consumers and households and the higher costs of capital have hampered investments especially in emerging and developing markets.

The problem is not the technology readiness of key enablers like energy storage clean hydrogen, sustainable aviation fuels, carbon capture and many other clean technologies, says Maciej Kolaczkowski, who oversees the World Economic Forum’s Advanced Energy Solutions community. The real issue is an absence of compelling business cases.

Take the case of Aramco, which is also a producer of green hydrogen. There are few takers for this alternative product.

Nasser told delegates that, in energy terms, the cost of green H2 amounted to the equivalent of $400 per barrel of oil — roughly five times the current price. He added that renewable hydrogen would only be affordable with a “significant amount of [government] incentives and offtake agreements of at least 15 years” — but that companies are wary of starting businesses that are only profitable with subsidies and would then be unable to make money if those were taken away.

It is little wonder then that Aramco, which has a market cap of $1.8 trillion and boasted $121 billion in profit last year, is not about to shut down its main line of business.

The State Of The Global Energy Transition

The slowdown in the pace of the global energy transition, first identified in 2022, has intensified in the past year, according to the Forum’s 2024 report. It shows that the three-year improvement in global ETI scores between 2021-2024 is almost four times less than the upswing over the 2018-2021 period. Furthermore, the report indicates that 83% of countries achieved lower scores than last year on at least one of the primary performance dimensions of the energy transition – sustainability, equity and security. Economic volatility, heightened geopolitical tensions and technological shifts have all had an impact, complicating its speed and trajectory.

Europe continues to lead the ETI rankings, with the top 10 list for 2024 fully composed of countries from that region. Sweden (1) and Denmark (2) top the rankings, having both placed in the top three countries each year for the past decade. They are followed by Finland (3), Switzerland (4) and France (5). These countries benefit from high political commitment, strong investments in research and development, expanded clean energy adoption – accelerated by the regional geopolitical situation, energy-efficiency policies and carbon pricing. France is a new entrant in the top five, with recent energy-efficiency measures reducing energy intensity in the past year.

Among G20 economies, Germany (11), Brazil (12), the United Kingdom (13), China (17) and the United States (19) join France in the ETI top 20, along with new entrants Latvia (15) and Chile (20), which were buoyed by increases in renewable energy capacity.

China and Brazil have progressed significantly in recent years, primarily driven by long-term efforts to increase the share of clean energy and enhance their grid reliability. Brazil’s ongoing commitment to hydropower and biofuels, recent strides in solar energy, along with initiatives tailored to create new opportunities have been key in attracting investments. In 2023, China also significantly scaled up its renewable energy capacity and continued to grow and invest in its manufacturing capability in clean technologies such as batteries for electric vehicles, solar panels, wind turbines and other critical technologies. China, together with the U.S. and India, is also leading in developing new energy solutions and technologies, the report says.

The Role Of New Technologies

Young, innovative companies, are playing a pivotal role in creating these new energy solutions– such as small modular reactors (SMRs) which can be built more quickly and cheaply than traditional nuclear power plants, peer-to-peer energy generation, green hydrogen, innovations in fusion, electric car charging, energy storage and carbon management technologies. Among the 2024 cohort of Technology Pioneers announced on June 6 were 14 companies working on both established and emerging green technologies in areas such as carbon-negative and circular materials, carbon capture, regenerative agriculture, alternative proteins, nuclear fusion as well as carbon-negative and circular materials.

Two of the 2024 Tech Pioneers – India’s Amperehour Solar and the U.S.’s Fourth Power – provide innovate ways of storing renewable energy while three others: Germany’s Marvel Fusion and Proxima Fusion and the U.S.’s Thea Energy are all working on fusion energy. (See The Innovator’s in-depth story about Proxima Fusion and its story about Marvel Fusion). Fusion energy has great potential as a safe, abundant, zero-carbon source of reliable electricity and is recognized as a potential game-changer in addressing climate change and rising global energy demand.

The Forum’s Advanced Energy Solutions community, which is comprised of technology companies, project developers, financial institutions, investors, and the large corporates who will buy the green solutions, aims to help speed up from decades to years deployment of these types of advanced energy solutions.

The key, says Kolaczkowski, will be building the right business cases.

Building Viable Business Cases

There is an economic case for moving to green energy. California’s Carbon Neutrality by 2045 plan, for example, estimates that, alongside neutralizing CO2 emissions, implementation would produce $200 billion of healthcare savings due to reduced air pollution and create four million new jobs to develop infrastructure and capacity.

One of the important things about this program is that the benefits are valued and priced into the investments. This is not often the case, meaning innovators, utilities, energy users and investors don’t get rewarded for generating these benefits, Kolaczkowski says.

At the same time, there is an additional cost gap – the so-called green premium. While some costs are expected to decrease, many solutions will still incur long-term additional costs that need to be paid. For example, the cost of clean hydrogen could reach as little as $0.12 per kilowatt hour (kWh) by 2035, but this would still not be in line with natural gas, the cost of which is expected to be as low as $0.09 per kWh in the same year. Sustainable aviation fuel (SAF) will still be up to two times more expensive than kerosene by 2050. And carbon capture will always be a pure additional cost to the emission producer that does not really have an alternative, says Kolaczkowski.

When combined, the green premium and the lack of accounting for the benefits of new technologies pose significant challenges to create viable business cases for the private sector to invest in advanced energy solutions, he says.

In 2023, around $60 billion was allocated to advanced energy solutions, but this needs to grow almost 10-fold over the next few years, according to Forum figures. Investment in storage, clean fuels, carbon capture, SAF and advanced nuclear need to exceed $500 billion per year by 2030 to align with global net-zero pathways.

Economic, health and climate outcomes need to be valued and priced into investments to address cost disparity. At the same time, the resulting increased cost of the energy transition – the price to pay for wider benefits – will need to be met by governments, and eventually consumers, while companies must also accept some margin compression, says Kolaczkowski.

Government funding, which exists in various forms such as subsidies, incentives, and guarantees to help minimize risk and cost, could enable more investment. California, for example, has been able to inject almost $50 billion from state funds, in addition to the Inflation Reduction Act at the Federal level, to make its Carbon Neutrality Plan by 2045 more attractive to investors.

Another way to cover the green premium cost is by passing it directly to end users and consumers. For instance, decarbonizing aviation by 50% would result in the tripling of fuel costs for airlines as they switch to SAF. If airlines were to preserve margin and pass the entire cost onto passengers, it would result in an increased ticket premium of around 18%.

Last month, Lufthansa became one of the first airlines to announce a surcharge on tickets to fund cleaner fuels and decarbonization. The Frankfurt-based group, which operates Eurowings, Swiss and Austrian Airlines as well as the German flag-carrier, said it would charge a fee of between €1 and €72 per ticket from next year.

A study suggests that decarbonizing Europe’s power grid by as much as 95% using advanced energy solutions and renewables would increase bills by roughly €14 per month for the average EU household.

Public acceptance will depend on whether consumers can be convinced that paying more is a viable path to a better future, says Kolaczkowski. “Asking people to pay a premium for an extended period of time is difficult,” he says, “so the role of government will become essential.”

Giving the energy transition new momentum will require addressing the green premium and accounting for the full costs and the benefits of advanced energy solutions, he says. “Supporting partnerships among innovators, large energy companies, energy users and investors is also key,” says Kolaczkowski. The Forum’s Advanced Energy Solutions community is trying to do just that, he says, “ by helping to increase public confidence in advanced energy solutions, boost technology readiness and demand, and bolster the business case for these much-needed greener energy solutions.

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How A Dutch Venture Builder Is Accelerating European Deep Tech

When researchers at the European Organization for Nuclear Research (CERN), developed a cooling system for particles traveling at light speed through the world’s largest and highest-energy collider, they never imagined that the technology would one day be used to make corporate data centers more sustainable.

Data centers currently use 1% to 1.5% of global electricity but as demand grows for new digital services and technologies, such as AI, by some estimates they could use as much as 20% of all electricity available worldwide, resulting in massive CO2 impact equal to that of the aviation and shipping industries combined. Much of the energy is used for air conditioning or fans to cool the servers but it’s the chips that are the source of the heat. A startup called Incooling, one of 52 new deep-tech ventures created by Dutch Deep Tech venture builder HighTechXL, found a way to use CERN’s phase technology to cool the chips directly, significantly cutting electricity use – and related CO2 emissions – while radically improving the performance of semiconductors. In June Incooling announced test results with chip maker AMD that established a new world record in central processing unit (CPU) performance, an example, the startup says, of the potential that cooling solutions can unlock when integrated with next-generation CPUs.

That’s the kind of impact HighTechXL seeks, says CEO John Bell, a former executive at both Philips and at Johnson & Johnson’s, where he collaborated closely with startups to co-create products. The venture builder scouts advanced technologies from research institutions and tech companies, assesses them based on their patent position, novelty, manufacturability, and knowledge transfer, then explores ways of to use them to tackle grand societal challenges and begins the venture-building process. Its shareholders include Dutch chip equipment maker ASML and Dutch healthcare company Philips. Together they created DeepTechXL, a €100 million investment fund to help ensure the young companies get the follow-up funding they need. One of the large Dutch pension funds and local government also agreed to invest in DeepTechXL.

“Deep Tech is needed to help solve the world’s most pressing problems but is complicated, highly risky and it takes time – it can take eight to ten years for companies to start earning revenues,” says Bell. “We believe that by putting together the right IP with the right people and mobilizing the Deep Tech ecosystem we are well positioned for success.”

The venture builder’s approach is an example of how young companies, large corporates, government, and research organizations can work together to help solve the U.N.’s Sustainable Development Goals.

Combining Knowledge, Finance And Talent

Incooling’s journey illustrates how the model works. Through its scouting efforts CERN’s phase change technology found its way onto HighTechXL’s radar. It licensed the technology and then asked two entrepreneurs from its program- Helena Samodurova and Rudie Verweij – to see what they could do with the technology. Verweij has an IT background while Samodurova is focused on sustainability. They were tasked with finding other applications for the technology and determining whether it could be grown into a business, says Incooling co-founder Samodurova, the company’s COO.

Everything from solar panels to chips can benefit from cooling, so it was difficult at first to decide what to focus on. Then Samodurova and Verweij discovered that chips were the source of the heating problem in data centers and that phase change – the heart of the technology developed by CERN- could be used to cool them. “If a computer becomes hot performance goes down,” says Bell. “By cooling down the chips Incooling can improve performance up to 65%. This is very attractive for all kinds of companies that work with chips because it means you can significantly increase the performance of a server without having to buy a new one.”

Six months in, Incooling raised funding. “Knowledge, financing and talent are all very important ingredients to success,” says Samodurova. “At HighTechXL, we are surrounded by amazing people who helped us get where we are today. I can’t imagine a business like Incooling being built anywhere but here.”

HighTechXL backer ASML supported Incooling from its inception, helping with everything from engineering, to the patent process and commercialization and market research, she says.

The chip equipment maker’s collaboration with Incooling culminated in a pilot project that involved the startup’s solution being tested in an ASML data center. “ASML views the primary benefits of this relationship as accruing to Incooling, while potential value for ASML could emerge over time if Incooling’s solution becomes integrated into the ASML infrastructure,” Rob van der Werf, ASML’s Government and External Affairs Director Startup Partnership, said in a written response to questions.

Having ASML as the startup’s pilot customer “was a way to support our entry into the market. It helped us grow in all kinds of ways and proved we could work together.” says Samodurova.

Indeed, ASML and Incooling won an award for the most successful and impactful innovative collaboration between a corporate company and a startup in a 2020 Dutch contest.

A Two-Way Street

Van der Werf says that ASML gives a lot but also a gets a lot from the relationship with HighTechXL.

“This is not just about funding,” says van der Werf. “From our own history we have learned that having access to a network of experts that are willing to help you solve complex problems is extremely valuable.” For example, he says another of the venture builder’s startups faced a design challenge for a highly energy efficient complex machine. Following sessions with ASML experts, a design was developed that surpassed the initial requirements in terms of efficiency, output and cost-effectiveness.

ASML also benefits in multiple ways from the relationship with the venture builder, says Van der Werf. “Collaborating with HighTechXL provides talented ASML employees with an opportunity to participate in a customized personal development program that allows them to share their expertise with startups and be part of a different entrepreneurial experience,” he says. “ASML employees learn a lot by being involved with startups.”

The venture builder’s corporate partners are collaborators and potential customers of the startups in the program. It is also possible for corporates to launch strategic innovation challenges that lead to the creation of new companies. They get early access to the technology but not exclusivity, says Bell.

Next Up? Advanced MedTech

In September, a new venture building program for startups in the healthcare and MedTech sector, called HealthTechX, was launched. The partners involved are Philips, HighTechXL, local government agency Brabantse Ontwikkelings Maatschappij (BOM), DeepTechXL and Invest-NL. The new program aims to give an extra boost to the creation, building and development of startups in advanced medical technology in the Brainport Eindhoven region, which includes over 5000 high tech and IT companies.

HealthTechXL officially started at the beginning of September with 50 participants. In the first half of 2024, the teams will pitch their proposal to investors, potential partners and the broader Brainport startup community. The program is led and implemented by HighTechXL and the BOM. The BOM, DeepTechXL and Invest-NL intend to invest in companies that emerge from the program.

One of the aims of the new ventures is to enable talents leaving Philips to learn and get them involved in creating new companies locally. At the same time, the set-up will strengthen the innovative ecosystem, says Bell.

ASML – the most valuable deep tech company in Europe, with a market cap of about €220 billion- spun-out of Philips some 40 years ago, notes Bell. It will be interesting to see what local talent comes up with next.

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How To Curb AI’s Environmental Footprint

AI can help tackle the climate crisis but unless measures are put in place AI’s environmental footprint risks to be a part of the climate problem.

Hyperscalers, which include Google, Microsoft and Amazon, are driving the swift proliferation of electricity-guzzling data centers to expand their artificial intelligence and cloud computing technologies. As a result, the International Energy Agency (IEA), says that by 2026 data centers could use twice as much energy as two years ago—an amount roughly equivalent to adding “at least one Sweden or at most one Germany.”

The increasing demand is not just putting a strain on the grid. The boom in data centers is expected to produce about 2.5 billion metric tons of carbon dioxide-equivalent emissions globally through the end of the decade, according to Morgan Stanley research published in early September.

In addition, data centers, which house thousands of servers, require substantial amounts of water for cooling. Training large-scale AI models can lead to a 10-fold increase in water usage compared to traditional computing tasks, according to a September 12 briefing paper published by bluegain, a German company that helps leaders in their digital, business model, and sustainability shifts. These systems can use between 1.8 to 2.5 million gallons of water annually per megawatt of power consumed, depending on the technology and location. And, as data centers expand to support larger and more complex AI models, their power consumption and subsequent cooling requirements are expected to increase proportionally, further straining water resources.

But there is another side to the story. The energy grids of the future will require more powerful analytical tools and AI has a critical role to play.

In addition to better forecasting of energy supply and demand and predictive maintenance of physical assets, the IEA says AI applications in the energy sector could include managing and controlling grids, facilitating demand response and providing improved or expanded consumer services.(. Firms such as Octopus Energy and Oracle Utilities are already exploring this).

Young companies like BrainBox AI, iGenius, and Crusoe are using AI to help companies become more sustainable and others are using the technology to help protect the environment by doing everything from helping predict forest fires to preventing illegal overfishing.

This raises thorny questions, says an article published on the World Economic Forum’s website. “Do the economic and societal benefits of AI outweigh the environmental cost of using it? And more specifically, do the benefits of AI for the energy transition outweigh its increased energy consumption?”

The Forum’s Artificial Intelligence Governance Alliance is applying a cross-industry and industry-specific lens to understand how AI can be leveraged to transform sectors and drive impact on innovation, sustainability and growth.

As part of this initiative, the Forum’s Centre for Energy and Materials and Centre for the Fourth Industrial Revolution have launched a dedicated workstream on AI and energy usage and strategies to manage it.

“We need to understand the advantages and the disadvantages,” says Roberto Bocca, the Forum’s Head, Centre for Energy and Materials and a member of the Executive Committee.” We are talking about more efficiency but also more consumption.

The Forum will publish its initial findings at its annual meeting in Davos in January 2025.

“We are currently in the process of building a multi-stakeholder community, “says Thapelo Tladi, the Forum’s Lead, Energy Initiatives. “It involves ICT companies that are leading AI adoption but also a number of other companies across various industries like telecommunications, the chemical industry, health, etc. We have about 20 companies and we are looking to grow the group further.”

The Forum is one of a number of players tackling AI climate issues. France’s GenAI Impact is creating tools to measure and reduce the environmental impacts of using and deploying AI, Cloud providers such as Scaleway and evroc are using new approaches to significantly lower the footprint of data centers and consultants like Germany’s bluegain are advising companies on specific ways to reduce AI-related water consumption.

The topic will also be front and center at the XYZ Generative AI for business conference in Paris September 27. “Assessing The Environmental Impact of Generative AI: The Challenges & Opportunities” is the title of a panel which will be moderated by The Innovator’s Editor-in-Chief. Interviews with the panelists and with the Forum shed light on AI’s climate conundrum and what companies can do now to lower AI’s environmental impact.

Sizing Up The Situation

The IEA estimates that data centers, cryptocurrencies, and AI consume about 460 TWh of electricity worldwide in 2022, almost 2% of total global electricity demand. The ever-growing quantity of digital data requires an expansion and evolution of data centers to process and store it. Electricity demand in data centers is mainly from two processes, with computing accounting for 40% of electricity demand of a data center. Cooling requirements to achieve stable processing efficiency

similarly makes up about another 40%, says the IEA. The remaining 20% comes from other associated IT equipment.

Depending on the pace of deployment, range of efficiency improvements as well as artificial intelligence and cryptocurrency trends, the IEA expect global electricity consumption of data centers, cryptocurrencies and artificial intelligence to range between 620-1 050 TWh in 2026, with the base case for demand at just over 800 TWh – up from 460 TWh in 2022. This corresponds to an additional 160 TWh up to 590 TWh of electricity demand in 2026 compared to 2022.

It is difficult to get accurate estimates on the impact of GenAI in part due to machine learning models being incredibly variable, because they can be configured in ways that can dramatically impact their power consumption, but also due to organizations like Meta, Microsoft and OpenAI not openly sharing relevant information. What’s more there is no standard way of measuring energy and water consumption. Data is not systematically collected on AI’s energy use and environmental impact and there is a need for greater transparency and tracking – especially as models grow and GenAI use becomes more prevalent, says Samuel Rince, another speaker at XYZ Generative AI’s impact on business conference.

“Transparency on the models’ architecture, training process, inference energy consumption, etc. should be reported by providers, just like the PUE [Power Usage Effectiveness] is for data centers,” says Rince.

Rince is the co-founder and President of GenAI Impact, a non-profit organization dedicated to assessing and highlighting the environmental footprint of generative AI technologies. The French organization’s team, consisting of researchers, developers, and freelancers, collaborates on developing open-source methodologies and tools for environmental impact assessment.

Based on available information, GenAI Impact has developed tools for developers to measure the emissions of the third-party AI they are using so they can keep track of how much their company’s own usage of those technologies is impacting the environment.

For its part the Forum is seeking to get “an objective view on the information that is out there,” says Tladi, the Forum’s Lead, Energy Initiatives. “We want to find out where the energy is being consumed and we want to find out how they are measuring this and how industry can move together to improve transparency,” he says. “Then in the next phase we will drill down on the more practical elements of what companies can do in terms of strategies. It is great that we can do certain things to manage the energy consumption of AI but at the end of the day we will still need more energy so it is important that we also look at the clean power supply that will be needed.”

Reducing Data Center Resource Consumption and Emissions

A growing number of data centers are using green energy and that is expected to increase. Indeed, Morgan Stanley points out in its September report that an upside in the build out of energy guzzling data centers is that it will create a large market for decarbonization solutions. The build-out of the giant computer warehouses will increase investments in clean power development; energy efficient equipment and so-called green building materials, Morgan Stanley said. Carbon capture, utilization, and sequestration technology and carbon dioxide removal processes are also expected to get a boost as tech companies try to keep their climate promises, the report said.

Scaleway, a Cloud provider which is owned by Iliad Group and markets itself as an alternative to the U.S.-based hyperscalers, are reducing their energy consumption in other ways.,

Centers operated by Iliad Group’s OpCore in which Scaleway servers are housed, use no air conditioning and minimal water. “To our knowledge our GPU cluster is the only one in the world not cooled by air conditioning, which typically accounts for 30% to 40% of energy use,” says James Martin, Scaleway’s head of content and sustainability communications, a speaker at the XYZ Paris conference.

While hyperscalers are not transparent about their water usage based on available information Martin says he believes Scaleway uses significantly less water than American and Chinese Cloud providers.

In its annual sustainability report, Microsoft, a multibillion-dollar investor in Open AI, divulged that its data centers in Iowa and other areas used nearly 1.7 billion gallons of water in 2022. That’s 34% more than it used in 2021. While Microsoft hasn’t specifically said what led to the unusual surge, experts say it’s no coincidence it occurred while the company’s data scientists were believed to be training the large language models (LLMs) that power Chat GPT’s intelligence, according to a September 12 bluegain briefing document.

That conclusion about AI water consumption would seem to make sense since Google also reportedly used more than 5.6 billion gallons of water in 2022, or 20% more than the previous year, while training LLMs for its generative AI tool, Bard, bluegain says.

Data centers use huge volumes of water for cooling towers. Scaleway’s sister company OpCore uses a different approach in its DC5 data center: It uses air from outside to pass over the servers most of the year. During the summer months when the air gets hot it uses an adiabatic cooling system – mimicking how the human body sweats to cool down. By evaporating a few grams of water into the air, a few hours per year, the air coming from the outside can be cooled by nearly 10°C.

Scaleway’s system has 2,200 sensors and measurement points, allowing the system to adapt, self-regulate, and optimize its processes every 17 milliseconds so that every sensor gets exactly the right energy and cooling needed to function at maximum efficiency, saving both energy and water.

Scaleway is also taking steps to cut energy consumption by making adjustments to the hardware and software it uses.

Generative AI requires considerably more computing power than standard calculations. A key reason for this is that generative AI model training calls for GPUs (Graphic Processing Units] rather than CPUs, (Computer Processing Units), the hardware components that are the core computational unit in a server. GPUs generally require around four times more energy than CPUs. (Case in point: Ampere’s CPUs for AI consume 3-5 times less energy than the equivalent NVIDIA machines.

GPUs tend to generate 2.5x more heat than CPUs. Standard CPUs used in cloud computing are in the range of 250-350W TDP, whereas GPUs are in the 750-800W range and they require that much extra cooling power, says Scaleway. So, the processors needed for generative AI training and inference are considerably more power-hungry than pre-generative AI models.

The emissions from training, the process required to ‘educate’ a generative AI model by feeding it as much data as possible, vary widely depending on the model. A white paper published by French tech association Data For Good estimates GPT 3.5’s training emissions at c. 500 tCO2eq -.

Inference, or the everyday usage of a model, has its own impact, which has been estimated at 200 times higher than that of training. According to Data for Good, considering ChatGPT has 100m weekly users, that’s 100,000 trillion CO2 e/year for GPT-3.5.

Inference also uses a lot of water. It’s been established that one conversation with ChatGPT uses half a liter of water in terms of the data center cooling resources required. This is on top of GPT-3’s training, which required 5.4 million liters of water . That’s a bit more than one liter per training hour (training GPT-3 took 4.6 million GPU hours, according to ChatGPT)

Given these elements, it’s not surprising that AI energy demand is set to outpace supply.

One academic, Alex de Vries, a PhD candidate at Vrije Universiteit Amsterdam, has suggested that if every Google search for a year used AI, it would use the equivalent amount of electricity used to power a small country like Ireland.

One of the ways to reduce GenAI’s water and energy demands is to use alternative hardware for inference. “We are working with Ampere, they provide CPUs that are capable of handling many inference workloads and they can do it using 3x to 5x less energy an equivalent NVIDIA GPUs,” says Scaleway’s Martin.

Scaleway is currently rolling out its own “managed inference” service. Corporate customers will be able to choose from a number of opensource GenAI models that serve as alternatives to U.S. large language models, generate less emissions and use less water and energy, says Martin. “We are providing an alternative that is more sustainable and also sovereign because the training and inference data stays in Europe,” he says.

Suggested Best Practices For Corporates

Scaleway’s Martin, GenAI Impact’s Rince, Dr. Shirin Dora, an assistant professor in the Computer Science Department at the UK’s Loughborough University, and bluegain have practical suggestions for what companies can do to decrease AI’s environmental footprint:

*Don’t train your own LLM model from scratch, says Martin. iF a model already exists finetune it for your needs. The cost to your company and to the environment will be much lower

*Use GenAI only when it is relevant rather than for simple tasks like writing emails or for translation or as a calculator, says Martin.

*Reduce inference energy consumption by replacing GPUs with CPUs, says Martin.

*When using GenAI don’t always rely on the biggest LLMs. Use small models of traditional deep learning for smaller tasks, says Rince.

*When possible, use opensource models and model compression optimization to conserve consumption, says Rince.

*Be more open to switching to neuromorphic or quantum computing systems that are faster, more efficient and consume less energy, says Loughborough University’’s Dr. Dora.

*Seize the benefits of Life Long Learning Machines when they become available, urges Dr. Dora. Current large language model systems are limited to performing only those tasks for which they have been specifically programmed and trained. If you try to train them with new material the risk is that everything previously learned will be erased so when you want to add something new you need to start the energy guzzling task of training the model from scratch. Lifelong learning machines develop systems that can learn continuously during execution and become increasingly expert while performing tasks and apply previous skills and knowledge to new situations – without forgetting previous learning and without the need to retrain from scratch.

*Bluegain says companies who want to address the water footprint of digital technologies should consider:a) Investing in Advanced Cooling Technologies:Adopt cooling solutions that reduce water use.( Google says it is developing new climate-conscious cooling tech that can reduce a data center’s water use by as much as 50% while preserving energy efficiency.
b) Optimizing Cooling Processes: IoT-enabled building automation systems monitor and control cooling systems to enhance water efficiency without compromising performance.
Promoting Sustainable AI Development:Support the creation of AI models that are designed with water and energy efficiency in mind, working closely with technology providers to drive sustainable innovation.
Encouraging Best Practice Exchange: Participate in collective efforts to share best practices and conduct joint research aimed at reducing water consumption across sectors.
Setting Sustainable Targets for Water: Integrate the reduction of water consumption into the company’s sustainability goals and ensure transparent reporting and accountability against the targets set.

By adopting proactive measures to reduce energy and water consumption, organizations can not only advance their sustainability agendas but also enhance their operational efficiency, balancing technological innovation with responsible resource management, says bluegain.Visit: https://innovatorawards.org/

How Platform Business Models Are Changing Healthcare

U.K. clinicians knew that patients could benefit from remote kidney function monitoring at home and that these devices were being developed, but it was unclear how to best design one with patient needs in mind.

That is where Thiscovery came in. The University of Cambridge spin-out, which is backed financially by not-for-profit companies, uses a platform business model to connect innovators, device manufacturers, patients and clinicians on a platform network to crowdsource design ideas.

“We are changing the way healthcare is delivered to end-users by using the platform model as a kind of conceptional model as well as a business model,” says Ruth Cousens, Thiscovery CEO and Co-founder Ruth Cousens.

The platform runs online projects which gather the knowledge and experience of people who use, deliver and influence health and care services. Insights gathered allow its clients to solve real problems by shaping innovative evidence-based solutions. For example, it was used to work with maternity staff and families across England to develop new approaches for identifying and managing situations where babies are experiencing difficulties during labor. “This is being piloted and we hope will eventually be rolled out across the UK,” says Cousens, who will be a speaker at the Platform Leaders Conference in London on November 13. “There is a huge potential for platform models to influence public health policy and the delivery of health services,” she says.

Around the world, healthcare has struggled with uneven access and quality for decades. To try and change that dynamic healthcare professionals, such as the President and CEO of the world-renowned Mayo Clinic, have publicly urged a move from traditional, linear pipeline-service-model thinking to a platform approach that fundamentally changes how healthcare and advance cures are provided.

Andreessen Horowitz, a top-tier Silicon Valley venture capital firm, supports the shift. “We strongly believe there is a trillion dollar opportunity in becoming the front door to healthcare—the marketplace where consumers go to find and book appointments across multiple conditions and specialties, buy the lowest cost drugs, and even shop for insurance,” the VC firm wrote in an article published last year entitled “Healthcare Marketplaces Where Art Thou?”

The need is there. “There is a big gap between consumer expectations and what health care systems offer today,” says Laure Claire Reillier, Co-founder and CEO of Launchworks & Co, the organizer of the Platform Leaders conference. “They are used to choice, convenience and speed when they book a taxi, or a holiday and the platform economy plays a huge part in that. If you compare that with healthcare services, it is like day and night.”

Healthcare systems are huge, run in silos, are fragmented and full of inefficiencies and complexities. “It is really hard to navigate these systems,” she says. “Platforms can bring a lot of value in terms of coordination and better use of resources at scale by bridging the gap where there are pockets of unmet demand.”

For example, Florence, a U.K. marketplace that will participate in a panel at the Platform Leaders conference, matches nurses with healthcare providers that need them, says Reillier. “There are so many gaps and unmet needs.”

Helping Mental Health Patients

In the U.S. mental healthcare platforms are one of the first successful use cases of healthcare platforms, according to Andreessen Horowitz. The VC firm cites U.S. platforms Headway, Alma, Sondermind, and Path as examples. All four are platforms which are helping mental health professionals take insurance and connect them to consumers who need their services.

Most mental health providers historically have not accepted insurance. They’re often solo practitioners who don’t have the bandwidth to contract with each insurance company, and basic insurance contracts pay less than the provider could make by charging consumers directly. New mental health marketplace companies propose to handle the complex logistics around insurance contracting and billing and negotiate better payment rates than providers could get on their own, notes Andreessen Horowitz. On the backend, these marketplaces are structured as MSOs (Managed Service Organizations) that deal with the logistics of insurance and other backoffice support services.

In mental health, the drivers of successful growth have been:Very high consumer demand driven by increased incidence of mental health issues and the cultural destigmatization of therapy
Widespread adoption of telehealth, propelled by both necessity during the pandemic, and favorable regulatory changes
A historical dearth of therapists who take insurance, which has resulted in…
…insurance companies that are desperate to expand their mental health provider networks, and are therefore willing to pay competitive rates to platforms that can expand their network coverage

The key element that makes these marketplaces work is the fact that they offer deep supply-side services that benefit both the provider (by diversifying their revenue stream beyond cash pay clients) and consumer (reduces out-of-pocket costs per appointment), according to the venture capital firm.

U.S.-based Resilience Lab, which is using a platform business model to deliver mental health care at scale, is doing just that and more. It has developed its own methodology to train and certify therapists, to manage the quality of the care and ensure quality across all its certified providers. Then, it connects clients with mental health issues to the certified clinicians. Like other companies in the space the platform makes treatment assessable and affordable through insurance partnerships. But now Resilience Lab is taking its offer one step further. On October 8 the U.S. company announced the acquisition of Options MD, a leader in online psychiatric medication management for severe and treatment-resistant depression, a serious and often overlooked mental health condition that contributes to high costs of care and high level of patient disability.

The acquisition means that Resilience Lab can now offer integrated medication management and psychotherapy for moderate to severe mental illness, including mood and anxiety disorder, post-traumatic stress disorder (PTSD), and attention deficit hyperactivity disorder (ADHD).

Resilience Lab has also gained Option MD’s clinical intake and AI decision support platform which offers intelligent differential diagnosis and treatment recommendations that aim to improve diagnosis and help clinicians create the most effective care plan, according to the company. Clients of Resilient Lab who suffer from moderate to severe mental illness can now undergo an assessment and be referred to a specialized clinician for medication management, while Options MD Clients can complement their treatment with a qualified therapist to accelerate their mental health recovery.

“Primary care physicians do not know how to deal with these patients,” says Resilience Lab CEO Marc Goldberg. “The problem is systemic as primary care providers usually prescribe antidepressants after five minutes of a patient interview and do not have the time, training and incentive to provide care for any mental health issue,” he says. His company is operational in five U.S. states and works with 12 insurance companies that together cover 24 million people. The next step is to expand the platform to include corporates and their employees, says Goldberg.

Using Platforms To Mind The Gaps In the U.K.’s Healthcare System

In the U.K. the National Health Service is overwhelmed. Lengthening NHS waiting lists, which hit a peak of nearly 7.8 million in England in September, have prompted more people to take out private medical insurance in recent years, despite its rising cost, dipping into savings or taking out loans to pay for routine operations, often spending thousands of pounds. Many are not sure which clinician or hospital to turn to.

That is where MeditSimple, a UK healthcare marketplace, comes in. On October 28, the platform, which was launched in 2019, announced MeditSimpleAI, an AI tool providing what the company says is “responsible, personalized” healthcare guidance to patients who find it increasingly difficult to access timely and effective care.

“MedSimpleAI helps lower the pressure on healthcare systems without compromising quality,” says Laurence Lévy, MeditSimple’s CEO. “According to the OECD 20% of healthcare costs are wasted on unnecessary tests and consultations,” she says. “We can reduce this to 5%.”

It works like this: patients enter their symptoms in MeditSimpleAI, and the platform will identify related symptoms, medications, and medical conditions that may impact their current state. Based on this assessment MeditSimpleAI provides a ranked list of the best care options, complete with clear, actionable explanations, the company says. It also flags any symptoms to monitor that might indicate a need for urgent care.

MeditSimpleAI offers recommendations based solely on medical relevance and patient preferences—free from ads or affiliations, says Lévy.

“Most of the patients we support are insured and benefit from a streamlined process that ensures they are fully prepared for treatment from an administrative standpoint – something hospitals greatly appreciate,” she says. “Hospitals also value our efforts to make their services more accessible and understandable for patients. Through our integrated search engine patients can easily explore and navigate available services, enabling immediate insurance refunds and significantly enhance both the accessibility and transparency of the care provided.”

The platform has agreements with hospitals, clinics and imaging centers. “Our mission is to make healthcare sustainable and responsible for both patients and healthcare professionals,” says Lévy. “Unlike other marketplaces we don’t encourage consumption of healthcare services but rather promote better, more efficient care while alleviating burdens on medical facilities and professionals.”

Healthcare Needs Its Own Platform Strategy

Earlier this year research published by MIT Sloan Management Review found that best practices that have proven successful for digital platforms in other business contexts are less useful in the context of healthcare. An article entitled “Healthcare Platforms Need a Strategy Overhaul” sheds light on how platform strategy must evolve as it is applied to solve new problems in new markets.

Researchers Marcus Holgersson, Joakim Björkdahl, Anna Essén, and Johan Frishammar identified the unique challenges digital health care platforms face, based on four-year-worth of study. From 2018 through 2022, the authors studied digital health platforms in Sweden, focusing on platform strategy, ecosystem management, and user behavior. They conducted 108 interviews in 14 organizations — four major digital primary care platforms, two platform technology developers, five established public and private health care providers, two health care agencies, and the Swedish Association of Local Authorities and Regions — and with individual users of digital health platforms.

What they found was that due to the special nature of the healthcare sector, network effects don’t work in the same way and that due to regulatory restraints that can differ from country to country and even region to region, it is much more difficult to scale them, Holgersson said in an interview with The Innovator.

In their article the researchers outlined a three-part approach for making health platforms grow and succeed. To avoid costly mistakes, organizations need to shift their mainstream assumptions about platform strategy and make different decisions about key domains to align with a digital health perspective:Market Entry — Unlike platforms such as Airbnb and Amazon, healthcare platforms are not a substitute but rather a complement to established services. Digital health platforms should focus on a narrow and well-defined scope and integrate with established ecosystems.
Scaling the Business — The concept of network effects is central to platform strategy. Having an increased number of users is important for platforms such as Facebook, but for health care platforms, learning from and leveraging data from users will improve adoption and retention. Digital health platforms should use data to develop operational excellence and for business model development.
Ecosystem Governance — Organizations such as Apple centralize orchestration among their ecosystem of apps and developers, but health care platforms must function under distributed orchestration among physicians, health care organizations, governments, insurance companies, and more. Digital health platforms need to build sociopolitical legitimacy and proactively participate in regulatory development.

“Digital health care platforms, if done right and implemented well, can be a large step in solving the productivity crisis in healthcare,” researcher Frishammar, a professor of entrepreneurship and innovation at Luleå University of Technology in Sweden, was quoted as saying in a press release about the study. “There is still much to be explored in what these platforms can do and should do, but ultimately they can make healthcare more accessible and more affordable.”

“The potential is huge in terms of cost savings and quality improvement,” says Holgersson.

Moving From The Periphery To The Core

The question is whether it will really be possible to transform huge healthcare systems. “I think the answer is yes,” says Launchworks’ Reillier. She cites the example of a platform in the UK called Patients Know Best which, with a patient’s permission, aggregates all their healthcare data from various practitioners in one place, giving them access to their own healthcare data and the power to share it. The service is going to be rolled out across the U.K. It is an example of how platforms can move from the periphery to the core of healthcare, she says.

There is a huge pent-up demand not just for medical care but also for preventative care, says Reillier. Daniel Ek, the founder of music sharing service Spotify, recently opened a business in London called Neko Health that offers people full body scans for 299 British pounds. Within the first month 30,000 people had signed up and there is a waiting list to be seen.

This kind of affordable prevention should be integrated into healthcare services, says Reillier. “We need to avoid a two- speed system where only those that can afford healthcare will have access,” she says. “Platforms can be part of the answer to coordinate participants and help orchestrate the emerging healthcare systems of tomorrow. Since they are smaller and nimble, they can begin by complimenting existing services and eventually transform them.

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