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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State Of Compute: The New Power Paradox

With good reason: in a recent podcast OpenAI CEO Sam Altman called compute “the currency of the future” and says he believes “it’ll be maybe the most precious commodity in the world.”

Countries that can “manufacture intelligence” at scale will be at the forefront of harnessing the benefits of finding solutions to key challenges, from green transition to digital biology, says a recently released report by the Tony Blair Institute For Global Change (TBI). It argues that compute is not just a source of scientific and economic progress, but the new benchmark of global power economically and geopolitically.

“Just as governments needed to enable infrastructure such as roads, railways and telecommunication networks for business to thrive, investing in shared and public compute has become equally important,” Jakob Mökander, TBI’s Director of Science & Technology Policy, said in an interview with The Innovator.

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,” says the TBI 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 in the last 12 months.

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. TBI’s data-center investment indicator shows that the U.S. has announced that 5,796.2 megawatts’ (MW) worth of data centers are to be built over the next three years. This is a 164.4% rise from 2023 and shows no sign of slowing.

What’s more, large U.S. tech companies, including Alphabet, Amazon, Meta, Microsoft, xAI and Oracle, are driving competition for the latest chips, such as Nvidia’s Blackwell GPU, launched in March 2024. This latest architecture is designed to offer four-times faster training and 30-times faster inference.

As today’s largest AI models can be four times more computationally intensive than in 2023, these chips – due to start shipping at the end of 2024 – will revolutionize the next generation of compute resources, says the report.

The Race For Compute Supremacy

With $210 billion having been spent globally to upgrade and maintain the existing server base in the past year, a new compute security dilemma is emerging as the two largest state players, the U.S. and China, seek to check the other’s power with export controls, investments and industrial espionage, says the TBI 2024 report. With China’s new “HPC iron curtain”and its refusal to declare new supercomputers and other investments to global indices such as the TOP500 Supercomputers list, the balance of compute power will be increasingly difficult to verify, the report says. It forecasts that this strategy “will lead to increasing uncertainty in the world order and intensify the race for compute supremacy.”

Where Does This Leave The Rest Of The World?

As the commoditization of computer resources intersects with the clash of great powers, questions of digital sovereignty and digital colonialism are being raised, notes the report. “Caught between allegiances to infrastructure providers on the one hand and access to critical components on the other, emerging digital economies find themselves in strategic dilemmas as they trade-off between a series of sub-optimal binary choices,” the report says. Countries may find themselves in a position of having to align with one of the supercomputer superpowers for assured compute access, increasing tech bipolarity in an era of geopolitical upheaval, says the report. Alternatively, countries seeking to accelerate their own progress may be able to take advantage of the rivalry and develop alternative ecosystems to meet the needs of non-aligned countries, creating a new multi-polarity in the world order. Leaders wishing to avoid either dynamic may seek to develop sovereign capabilities, but risk investing in expensive infrastructure that is still behind the pace of the global leaders. Or they may find that key levers of their ecosystem, in particular talent, has left to work in more developed ecosystems.

To avoid a new type of digital divide and a fractured computing landscape, the TBI report recommends three possible courses of action for governments: public compute, public-private partnerships and international development for access to compute.

Public Compute

One good option for countries to build strategic autonomy while leveraging compute for the public benefit is to invest in “public compute”, i.e. state-built components of the compute stack, including data centers and cloud infrastructure and supercomputers, says the report. This compute can then be provided to critical sectors and be used as a lever to encourage responsible use of AI. As others have argued, compute should be provided on the basis that companies meet certain safety requirements, or that end outputs can help to contribute towards the digital commons, the report says.

Public compute requires investing in the key enablers of a model compute ecosystem. Having an abundance of energy and a mature data-center ecosystem has made it easier for countries like Malaysia to build their own public-compute capacity, the report says. Countries with abundant energy resources and stable business environments, such as Dubai, are focusing on attracting and hosting large-scale data centers.

Alternatively, nations with advanced manufacturing skills and startups might invest in developing domestic chip production, as seen in China’s efforts to counter U.S. restrictions while countries with more limited capital, resources and skills but a strong research ecosystem can pursue a strategy of AI model development. For example, state-of-the-art models can be fine-tuned to be better aligned with national language, values and interests and allow for models to be built with specialized sector-specific objectives in mind. The Netherlands has begun building GPT-NL with this purpose in mind.

“By strategically leveraging compute for public benefit,” says the report, “nations can not only address immediate sectoral needs but also reinforce both their long-term technological sovereignty and their competitive position in the global digital landscape.”

Public-Private Partnerships

Since TBI’s 2023 report, new models of mobilizing private capital for the development of AI infrastructure have emerged. These provide opportunities to unlock multi-trillion-dollar long-term investment and the potential for technological advancement on a global scale.

For example, Aramco, the Saudi Arabian government majority controlled petroleum and natural gas company has partnered with Groq, an American AI company that builds an AI accelerator application-specific integrated circuit (ASIC) that they call the Language Processing Unit (LPU) and related hardware to accelerate the inference performance of AI workloads, to create the world’s largest AI inference center. The center, strategically located in Saudi Arabia, aims not only to support the Saudi government’s Vision 2030 to drive digital transformation and economic diversification but also has the potential to open access to cutting-edge AI capabilities for those nations that lack the resources to build and maintain their own AI platforms. Through creating a global hub for AI infrastructure, Saudi Arabia hopes to reinforce its leadership in the global digital economy and provide a blueprint for how public and private capital can come together to build the advanced AI infrastructure needed to drive the next wave of technological innovation, economic growth and inclusivity on the global stage.

Regional Compute Sharing

Nations with limited energy and domestic compute infrastructure could also seek to build and participate in regional compute models.

Estonia, despite being one of the world’s most digitally advanced nations, has limited compute resources. By leveraging The European High Performance Computing Joint Undertaking ( EuroHPC JU) and, in particular, access to its fastest supercomputer LUMI) it overcomes potential issues in accessing supercomputing resources and AI servers via the Cloud, notes the report.

Similarly, the ASEAN-HPC taskforce is providing opportunities across the region so that ASEAN countries can access shared high-performance computing (HPC) resources such as Japan’s Fugaku supercomputer.

“A regional approach provides a larger market for compute providers to sell into, so may provide a stronger rationale for investment,” says the report. Transnational investment will help to reduce the public-spending requirements on any one individual nation.

The key for countries without strong political integration will be to find practical and regulatory mechanisms to secure corridors so that shared compute access can be realized, says the report.

International Development for Access to Compute

If countries lack the right political environment to build shared compute, an alternative could involve looking to the international development landscape for support, says the report. For example, the U.S. Department of State, in collaboration with companies such as OpenAI and Nvidia, have launched the Partnership for Global Inclusivity on AI. The partnership has allowed the Department of State and Nvidia to offer compute credits to emerging market economies. The Gates Foundation AI Grand Challenges has adopted a similar approach, focusing on health care and the Gates Foundation has also stated curating a “Gavi for compute” to redistribute compute resources in Africa.

Other multi-stakeholder agencies have begun their quest to close the international compute divide. The United Nations, for example, recently recommended the development of an AI development fund.

“Many of these projects are in their infancy, so there are unique opportunities for countries to shape this agenda by understanding their own national demand and engaging with these new initiatives, the report says.

Turning Challenges Into Opportunities

The opportunities for countries to boost their compute position don’t stop there.

Countries with a significant number of servers and data centers are gaining an advantage by beginning to replace general-purpose servers with smaller numbers of specialized AI servers. Switzerland, the Netherlands and Ireland are leading the way, says the 2024 report, and TBI projects that this trend will continue.

Small countries that commit to building out infrastructure are attracting investment. In this year’s TBI report, Israel has risen from 38th to 24th on its Cloud-service availability indicator, as it expanded from a single cloud or colocation data center in 2022 to five additional data centers – an expansion of 20,000 % (from 4,000 to 804,000 square feet). Furthermore, Israel has shown the largest percentage growth in subsea cables and Internet-exchange points, adding a further eight. This rapid growth has been driven in part by Israel’s Project Nimbus, first conceived in 2019, to be the basis of the public Cloud infrastructure in Israel to facilitate the move of a significant amount of government services to the Cloud, according to the report.With AWS and Google having won the tender to implement this $1.2 billion project, the subsequent investments play a key part in Israel’s rise, according to the report. This includes investments to increase the electricity grid, including building a new substation to facilitate the supercomputer and additional data centers, as well as a commitment to invest in smart grids and renewable energy to power this growth. The accompanying investment into regional connectivity, such as the 254 km subsea cable between the Mediterranean and the Red Sea, is also creating new opportunities for Israel’s compute ecosystem, notes the report.

Energy infrastructure is also a key factor. Companies building the largest data centers will prioritize putting them in locations that can build out energy infrastructure fastest, says the report. If Europe can translate its greening economy into one that can rapidly scale infrastructure, it will strengthen its competitive advantage. Investment in renewables has been a priority across Europe to secure domestic energy supply and reduce heavy reliance on Russian energy. It was the only region to have demonstrated significant growth in the availability of clean energy over the past year, according to the report.

Physical geography is becoming a key factor in growth for other reasons. For instance, accelerated private sector investment in Finland’s compute partly rests on the country’s cooler climate, making it cheaper and more energy efficient to run data centers. Likewise, Malaysia has taken advantage of its geographical proximity to Singapore to supply the country – which has placed a three-year moratorium on data center building – with compute at scale.

Africa is making strides in building its compute power, but still has a way to go. TBI projects that Cote d’Ivoire will have the highest server growth rate of the 67 countries studied over the next 5 years, at 84.3%. Ethiopia, Rwanda and Kenya have seen the biggest year-on-year increase in software engineers across TBI’s data, at 40%, 39% and 29% respectively. And Rwanda, Nigeria, Kenya and Ghana have shown high growth in both the size of their developer community and its activity, while the region more broadly has the highest growth in GitHub developers, year-on-year, according to TBI. That said, the Continent is being held back by a variety of issues including electricity blackouts and the fact that many African countries heavily restrict data portability across borders. In addition, despite higher levels of STEM graduates, sub-Saharan Africa is not developing strong human-capital pipelines to compute roles, says the report. Despite a new wave of investment, the region has a similar number of servers to Spain; per capita, the UK has 20 times as many, notes the report. It says two changes are required for Africa to further shore up its position in compute: an increase in edge computing (where data processing takes place on a device or local server, rather than a data center) to help leapfrog infrastructure limitations, and more opportunities for the region’s skilled workforce within local compute ecosystems. “Without these changes, the local talent pool is likely to seek opportunities elsewhere – and this could stymie the growth of the region’s access to compute,” the report says.

No Silver Bullet

The fact that U.S. companies dominate large language models (LLMs), AI chips and data center capacity and the increased tensions between the U.S. and China do not necessarily have to lead to a new digital divide and a fractured compute environment, says TBI’s Mökander. To solve the world’s biggest problems from climate to healthcare – and unlock economic growth – countries need not only invest in compute capacity, but also implement technical, procedural and cultural controls to manage tensions related to system interoperability, data sharing, and cyber resilience. “Not all compute requires the same level of localization and security.” he says. “Countries need a plurality of approaches and to segment use cases and data in order for the world to build a resilient and flourishing compute ecosystem.

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Technology’s Role In Fighting Climate Change

Global leaders gathered in Dubai this week for COP28, the United Nations’ climate conference, a global hackathon was exploring how quantum computing can contribute to solving some of the world’s toughest challenges, including sustainability.

The winners of the hackathon, which was organized by French quantum company PASQAL and attracted more than 800 candidates with 75 proposals from 25+ countries, demonstrated how the cutting-edge technology can, among other things, be used for renewable energy forecasting and optimizing the layout of windfarms.

The application of new technologies to abate climate change is needed more than ever. A new report released this week by the World Economic Forum entitled The Net-Zero Industry Tracker 2023, published in collaboration with Accenture, takes stock of progress towards net-zero emissions for eight industries – steel, cement, aluminum, ammonia, excluding other chemicals, oil and gas, aviation, shipping and trucking – which depend on fossil fuels for 90% of their energy demand and pose some of the most technological and capital-intensive decarbonization challenges.

These emission-intensive sectors, which account for more than 40% of global greenhouse gas emissions, are not aligned with the trajectory to reach net zero by 2050, says the report. Over the past three years, absolute emissions have grown on average by 8% due to increased activity and demand and all sectors in scope depend on fossil fuels, most with over 90% reliance.

Transitioning these industries to a Net Zero future will require a collective investment of approximately $13.5 trillion, prioritizing the electrification of low to medium temperature industrial processes, says the report. That amount is needed to scale up the essential technologies and sustainable infrastructure, but investments aren’t enough, says the report. They must be complemented by policies and incentives that can help the industries make the switch while ensuring access to affordable and reliable resources that are critical for economic growth.

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Putting AI Into Production

Like many large corporates Bosch, a German multinational engineering and technology company, is interested in putting AI into production to make its factories more productive and competitive. Although the machines and production line in its plants are already well-equipped, integrating new sensors and deploying AI technology could further enhance efficiency. While the company has an open innovation department and regularly works with startups and scale ups, its Maklar, Hungary plant had little experience in working with young tech companies.



Through the DeepTech Alliance, a private non-profit association of leading European entrepreneurship hubs that specializes in connecting deep tech companies with large corporates, the innovation manager of Bosch’s plant in Maklar connected with IPercept, a Swedish startup that serves as a kind of fitness tracker for industrial machines, leveraging AI to track mechanical movements to do root cause analysis and predictive maintenance.

Bosch tested the technology on a welding machine used in the automotive steering systems it produces. The objective was not just to tackle unplanned downtime, but to preempt it, leading to reduced costs, improvement in product quality, and elevated customer satisfaction. The pilot was a success, and the Maklar factory is now looking at how to apply the technology to other production lines, says Gabriel Gudra, the factory’s innovation manager. He and IPercent CEO Karoly Szipka appeared on stage together to talk about their collaboration at an October 9-10 DeepTech Alliance advanced manufacturing meeting in Munich which brought corporates together with startups targeting the manufacturing sector.

Bosch’s story is just one example of how manufacturers are starting to unlock AI’s potential. The same week the DeepTech Alliance was hosting the advanced manufacturing event in Munich the World Economic Forum announced the latest additions to its Lighthouse network, a community of 172 industry leaders pioneering the use of cutting-edge technologies in manufacturing. Nineteen manufacturing sites received designations as Fourth Industrial Revolution Lighthouses for achieving step-change impact in performance through technology-enabled transformation. Three others were designed as “Sustainability Lighthouses” for their use of advanced technologies to reduce their environmental impact.

The Forum’s latest cohort gained an average 50% boost in labor productivity, attributed to various digital solutions such as interactive training programs, smart devices and wearables, and automated systems that combine robotics, AI and machine vision. Process modelling and root-cause analytics unlocked efficiency gains across Lighthouses’ end-to-end supply chains, on average reducing energy consumption by 22%, inventory by 27%, and scrap or waste by 55%, according to the Forum.

“There are not many examples of successful applications of AI in industry,” says Federico Torti, the Forum’s Initiatives Lead, Advanced Manufacturing and Value Chains. “The lighthouses are demonstrating what can be achieved and serve as an inspiration for other companies.”

What sets the lighthouse companies apart is that they have already made critical investments in their tech stacks, and they design AI use cases for scale, creating an easily replicable package across their production network, says Torti. They start by fundamentally redesigning processes, reducing variability and waste, and organize technology deployment around the user, focusing on people skills and user experience / how users interact.

The lighthouse companies, which hail from 10 different countries, are also open to learning from their peers and across sector, he says. “They are really pushing the boundaries of how they can get value from cutting-edge technologies like AI,” says Torti. “It is not about piloting AI, it is about creating the right ecosystem that will allow their company to transform.”

In general, these companies are taking a long-term oriented approach, have a vision and are investing in “the required foundational aspects – such as clean, reliable data platforms, a good governance structure and training their work forces with a people-oriented approach –rather than trying to quickly adopt the shiny aspects of AI,” he says.

Global pharmaceutical company AstraZeneca’s Södertälje plant in Sweden – one of the Lighthouse factories in the Forum’s new cohort – is a case in point. The plant has implemented 50+ advanced technology solutions and a significant number incorporate AI or GenAI, Jim Fox, VP Sweden Operations, AstraZeneca, said in written responses to questions from The Innovator.

In drug development, AI predictive modeling is optimizing the physical and chemical properties of Active Pharmaceutical Ingredients (API) and predicting the performance of formulated products during manufacturing. What’s more GenAI, machine learning, and large language models (LLMs) are significantly reducing development lead times and the use of API in experiments, says Fox. AI-powered process digital twins are optimizing conditions for yield and productivity in the manufacturing process, reducing the use of raw materials, and minimizing tech transfer requirements. AI-powered tools are also aiding AstraZeneca in achieving its Net-Zero carbon footprint goal by pinpointing its environmental emission “hotspots” and the carbon footprint of itsd products across the entire supply chain.

An important factor in its success to date was preparing “findable, accessible, interoperable, reuseable and clean data” to power its AI algorithms and ensuring it is managed with good governance and data standards, says Fox.

Upskilling 3,000 employees to deal with VR/AR, AI computer vision, IoT, sensors, integration of cobots, drones and digital twins was also key. AstraZeneca has a strategic workforce plan that includes both outsourcing/offshoring AI services and building critical internal capabilities to cover the range of its needs, says Fox.

Specific tailored training for employees at the Swedish site was introduced through an extensive online training platform. “We also launched a Digital Academy together with a digital innovation zone for experimentation to sustain our digital capability uplift,” says Fox. In addition, the plant has also launched a comprehensive and accredited self-serving online program for basic to advanced AI and Gen AI training.

“The experience at the Södertälje plant has provided valuable insights into scaling AI and upskilling employees effectively,” he says. “If we had not done [the upskilling] we would not have seen the results that have led to a 56% increase in production and a 67% reduction in development lead times for launching new products.”

The Human Factor

The human factor must be considered in any successful adoption of new technologies, says Antti Rantanen, who runs the industry 4.0 and Industrial AI practice of the Nordics division of EFESO, an international consulting group specialized in helping manufacturers to use technology to advance operations strategy and performance improvement.

He cites a brewery’s manufacturing plant in northern Europe as an example. Most of the employees have been there for 30 to 35 years. “We were walking around on a factory tour when a bottle machine got stuck and the 60-year-old operator knew exactly where to kick it to get it started again,” says Rantanen, a speaker at the Deep Tech Alliance Munich event. An estimated 30% of the work force in Nordic factories will retire in next 10 years, he says, and that knowledge will disappear. Top-down technology solutions imposed by management are not the right fix.

“Factory operators hate it when headquarters comes and visits,” he says. “They have their own way of doing things.” Take the case of one packaging company Rantanen visited. The company’s leadership installed new SAP software. When leadership visited shop floor operators would open laptops to something that looked like the German tech company’s product and repeatedly push their shift buttons to make it look like they were using it. As soon as management left, they would go back to business as usual. “They explained to us that using a complicated IT system does not help them do what they need to do and if they adopted the technology it would ruin their P&L,” says Rantanen, who has 25 years of experience in digital transformation. “This is the situation in most manufacturing plants.”

When headquarters imposes AI solutions or software “they rarely ask the machine operators ‘what do you see as the inefficiencies and how will this connect with the overall value chain?’,” says Rantanen.

EFESO helps companies take a different approach. “We walk through a production plant before we start any process to understand the culture of that plant,” he says. “We usually meet with the CEO, plant manager, safety and maintenance – all the way down to the machine operators – to understand how people work together. We earn the trust of the machine operators, then, we go at this from an operational excellence perspective, find the inefficiencies and build the AI models with partners like SILO to be scaled out.”

Success depends not only on the input of people using the technology, with deep knowledge of operations. Cultural differences must also be considered, he says.

The French manufacturing culture is different from the German or the Swedish, says Rantanen. “Global companies think that they can roll out the same systems and same architectures in the same way but if you look at a plant in, say, Finland versus one in Brazil, the plants will be completely different in the way they work, their openness to change, in whether they adhere to processes or not,” says Rantenen. “Those things need to be taken into account, not just AI or robotics.” These issues will be ongoing even after AI is implemented and factory jobs change, he says. “AI will be essential for manufacturing companies to stay competitive globally,” says Rantanen. “But it will never work if they don’t take the human elements into account.”

Scaling Up

With all the hype around Machine Learning and AI “we get the impression that it is all changing very quickly,” says Thomas Klem Andersen, Executive Director of the DeepTech Alliance. “But a lot of manufacturers are still not there,” he says. “A lot more has to be done not just for the bigger industrial companies but also the smaller ones to help connect them to the right deep tech startups, identify use cases and implement them.

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The Case For Designing A Resilience-By-Design Cybersecurity Strategy

While there is lots of attention being given to AI and quantum computing there are an estimated 200 critical and emerging technologies shaping today’s technological landscape, each with their own unique cybersecurity implications.

“There is a Pandora’s box full of new technologies coming to market,” warns Dr. Hoda Al Khzaimi, director and founder of the Center for Emerging Technology Accelerated Research (EMARATSEC), and associate vice provost for research translation and entrepreneurship at New York University Abu Dhabi (NYUAD), United Arab Emirates. She is a co-author of a recent World Economic Forum report on Navigating Cyber Resilience in the Age of Emerging Technologies.

Indeed, the rapid growth in investments in emerging technologies– from approximately $4 billion in 2018 to more than $3.2 trillion today – demonstrates a significant surge in global interest and development, underscoring the need for a broad, inclusive approach to technology assessment and strategy development, says the report.

In the face of this complex and evolving threat landscape a traditional mindset of “security by design”, which focuses on embedding security features into new technologies from the outset, is no longer sufficient, says Al Khzaimi, who is also co-chair of the Forum’s Global Future Council for Cybersecurity and director emeritus of NYUAD’s Centre for Cybersecurity. Instead, there is a pressing need to adopt a “resilience by design” approach, which ensures that systems can withstand and recover from inevitable attacks that will occur as these technologies proliferate, she says. This approach involves embedding resilience principles into every stage of technology development and deployment. In practical terms this means enabling continuous monitoring, developing rapid response, and cultivating the ability to learn from incidents to strengthen defenses over time so companies can recover quickly with minimal impact.

Distinguishing Between Critical And Emerging Technologies

The first step in developing a resilience-by-design strategy is distinguishing between critical technologies which have already achieved a certain level of maturity and emerging technologies, says Al Khzaimi.

Critical technologies, such as smart and new material science, semiconductors and new means of energy generation, are already foundational and essential to national security and economic competitiveness, demanding immediate and sustained investments to protect them from cyber threats. In contrast, emerging technologies, such as AI, quantum computing and synthetic biology, are still at the developmental stage but have the potential to become critical as their applications expand and their strategic importance becomes more apparent. The two are not mutually exclusive. Some technologies fall into both categories. “This fluidity necessitates a flexible approach to cybersecurity that can adapt to both current and future risks, ensuring preparedness for a range of possible scenarios,” says the report.

Anticipating Worst Case Scenarios

While emerging technologies hold great promise for innovation and advancement across sectors, it is essential to consider the potential security challenges they might pose, says the report.

For example, biotechnology advances such as DNA data storage technologies raise questions about long-term data security and potential biological data breaches. Synthetic biology could potentially be used to create designer pathogens or manipulate existing organisms in unforeseen ways. If risks are not hedged within a certain framework of ethical and responsible development, the convergence of AI and biotechnology raises concerns about the potential for creating self-evolving biological systems.

Neuromorphic computing, an approach to computer engineering that designs hardware and software systems to mimic the structure and function of the human brain, may pose other risks. It can help improve efficiency and allow machines to perform more complex tasks but brain-like computing architecture could also prove vulnerable to new types of attack that exploit their learning capabilities, says the report.

Advanced 3D displays such as holograms could also be used for sophisticated phishing or social engineering attacks. Securing the data used to generate holograms is crucial in preventing unauthorized replication as there is potential for the creation of false environments that could manipulate decision-making in critical situations, the report says.

Creating Collective Cyber Resilience

Given the sheer volume of new threats how can companies and countries cope?

The Forum report contains three case studies. One illustrates how the French multinational company Schneider Electric is using generative AI (GenAI) for programmable logic controller code generation within industrial control systems. This application of AI can help to enhance operational efficiency and strengthen cybersecurity measures by automating code generation and improving code quality.

Another case study talks about how Singapore is working with multiple stakeholders on a critical information infrastructure (CII) supply chain program. “This program is a living blueprint that evolves to tackle changing risks and outlines guidelines to support stakeholders in risk management and cyber contracts,” says the report. “It prioritizes international cooperation to support cyber-risk management in supply chains with international and regional partners, working towards harmonizing cybersecurity standards across jurisdictions.”

The third case study highlights how the United Arab Emirates (UAE) is using emerging technologies at the national level to drive both technological innovation and cyber resilience. UAE government bodies are developing technologies such as AI, blockchain, quantum computing, 5G, IoT, digital assets, connected vehicles and smart cities with the goal of transforming sectors across the UAE, establishing it as a leader in technological innovation. The UAE is, for example, planning to transition all government transactions to blockchain by 2025 and has created the first official government body dedicated to the regulation of virtual assets. The Dubai Road and Transport Authority’s work on autonomous vehicles and the Dubai Electricity and Water Authority’s AI- powered operations are examples of the integration of AI into critical infrastructure while “considering safety, efficiency and decision-making,” says the report.

Relevant UAE government agencies are teaming with private-sector companies, research institutions and international partners in an integrated way to progress innovation while building in cybersecurity and anticipating future issues.

“They have an open assessment platform for all the new technologies that is being co-developed with different members of the private sector,” says Al Khzaimi. “What they are doing is creating collective resilience, by involving all of the stakeholders and not just the regulator and the government.”

The report acknowledges that this model is not applicable to all countries but endorses the approach that all nations must aim to derisk the potential threats of emerging technologies. “Understanding what types of bodies and what types of public–private collaboration lead to the most productive outcomes will ultimately serve more than just a single nation,” says the report.

Putting Resilience-By-Design In Practice

In addition to promoting cross-sector collaboration to build comprehensive cyber resilience the Forum report contains a list of practical suggestions for countries that want to put resilience-by-design into practice. They include: Focus dedicated research on fields such as quantum computing, blockchain, IoT and biotechnology to develop new technologies designed with inherent capabilities to detect, respond to and recover from cyberthreats.
Strategically integrate emerging technologies into critical infrastructure sectors such as energy, healthcare, finance and transportation. Technologies such as quantum resistant cryptography, IoT-enabled predictive maintenance and blockchain-based security protocols can enhance the resilience of these sectors.
Develop data-driven frameworks for technology and cyber governance with clear metrics for evaluating technology readiness, impact assessments and risk management.
Create training programs focused on emerging technology security, such as quantum computing, IoT and biotechnology to ensure workers have the right skills.
Implement ethical guidelines for emerging technologies
Adopt novel solutions tailored to local needs rather than existing technologies to reduce dependency on external technologies and promote local innovation ecosystems.
Establish continuous monitoring and incident response planning in cybersecurity practices.
Build trust in emerging technologies by communicating openly about cybersecurity measures, risks and responses to incidents.

“Emerging technologies require a multifaceted approach that integrates security, resilience, sustainability and quantifiable risk measurements into all aspects of technology development and deployment,” says the report. “By adopting these practical recommendations, leaders can enhance cyber resilience, promote responsible innovation and build a secure digital future. “

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New Report Weighs The Benefits And Risks Of AI Agents

The first phase of AI was predictive, the second was generative. Now the third wave is here: autonomous AI agents that can not only recommend actions but can reason and tackle multi-faceted projects without requiring human oversight at every step.

By 2027, half of companies that use Gen AI will have launched AI agents, according to Deloitte.

“This is a trend,” Cathy Li, the World Economic Forum’s Head, AI, Data and Metaverse and Deputy Head of Center for Fourth Industrial Revolution (C4IR), said in an interview with The Innovator. “It is already here so we need make sure we think about the ramifications.”

To that end, the Forum and Capgemini published a new white paper on December 16, Navigating the AI Frontier: A Primer on the Evolution and Impact of AI Agents.

AI agent’s ability to manage complex tasks with minimal human intervention offers the promise of significantly increased efficiency and productivity, says the white paper. Additionally, the application of AI agents could play a crucial role in addressing the shortfall of skills in various industries, filling the gaps in areas where human expertise is lacking or in high demand.As the technology progresses AI agents are expected to be able to tackle open-ended, real-world challenges such as helping in scientific discovery, improving the efficiency of complex systems like supply chains or electrical grids, managing rare non-routine processes that are too infrequent to justify traditional automation, or enabling physical robots that can manipulate objects and navigate physical environments.

But AI agents also pose certain risks. Technical risks include errors and malfunctions and security issues including the potential for automating cyberattacks. The autonomous nature of AI agents raises ethical questions about decision-making and accountability and there are socioeconomic risks around potential job displacement and over-reliance and disempowerment.

The white paper urges corporates to take measures to mitigate these risks. It recommends:Establishing clear ethical guidelines that prioritize human rights, privacy and accountability is an essential measure to ensure that AI agents make decisions that are aligned with human and societal values.
Prioritizing data governance and cybersecurity before deploying AI agents.
Implementing public education and awareness strategies is essential to mitigate the risksof over-reliance and disempowerment in social interactions with AI agents.
Improving the transparency of agents and implementing “human-in-the-loop” oversight, enabling agents to work autonomously while human experts review decisions after they’ve been made.

The Forum’s AI Governance Alliance, an initiative that unites industry leaders, governments, academic institutions, and civil society organizations to champion responsible global design and release of transparent and inclusive AI systems, has been working on the topic of AI agents for the last 18 months, says Li, and AI agents will be on the agenda at the Forum’s annual meeting in Davos January 20-25.

“There is more in-depth work to be done,” she says. “There are issues that need to be tackled because AI agents could exploit loopholes or act in unintended way socioeconomically. There is also concern about job displacement. If deployed in the right way it’s not about replacing humans, it’s more about augmenting what we do, but at the same time each company and organization need to deploy AI responsibility and keep the potential impact on the workforce in mind.”

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By investing and experimenting in quantum technologies now HSBC is not just preparing for the future; we’re shaping it,” Colin Bell, CEO of HSBC Bank and HSBC Europe, said in a statement.

HSBC is one of a number of banks helping pioneer tech solutions that combine AI and quantum or AQ for short. Shoring up cybersecurity is a key reason, but it is not the only one. Combining the two technologies can improve business outcomes across a broad spectrum of industries from financial services and healthcare to aerospace and manufacturing, say industry experts.

Using AQ now can also be a hedge against the day that fault-tolerant quantum computers become a reality. Since quantum computing is a step-change technology with substantial barriers to adoption industry pundits say early movers will seize a large share of the total value, while those who have not prepared may not be able to catch up and could see their businesses wiped out overnight.

That prospect is so worrying that a new acronym cropped up during the World Economic Forum’s annual meeting in Davos in January, says Clement Jeanjean, a Paris-based senior director at SandboxAQ, a company spun out from Google parent Alphabet that delivers AQ solutions that run on today’s classical computing platforms. “Instead of FOMO [Fear of Missing Out] people were talking about FOBO: Fear of Becoming Obsolete,” he says. “There is a race to make sure that historical incumbents in industry verticals are not put out of business overnight.”

There was also concern in Davos about not just companies but countries falling dramatically behind. “The growing global quantum divide between countries with established quantum technology programs and those without will lead to significant imbalances in core areas such as healthcare, finance, manufacturing and more,” according to the Quantum Economy Blueprint report published by the World Economic Forum on January 16, during the annual meeting.

Current worldwide public sector investments in quantum computing exceed $40 billion, says the report. But only 24 out of the 193 member states of the United Nations have some form of national initiative or strategy to support quantum technology development.

“We do not want to risk a quantum divide and hope the blueprint will enable discussions for different regions to participate and benefit from the quantum economy,” says Arunima Sarkar, the Forum’s Lead, Quantum Technologies. “We look forward to piloting the blueprint, working with policymakers in developing regional and national roadmaps for leveraging the potential of this technology and preparing for the transition to the post-quantum era.”

The Forum takes a much wider view of quantum technologies, including not just computing but also sensing and communications, says Sarkar. “Some of these technologies are already being deployed while for others we see rapid advancements,” she says. “Eventually these technologies are expected to permeate every sector of society, creating what we call the Quantum Economy.”

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From innovation to implementation: The new era of AI

Artificial intelligence (AI) has moved from the periphery of innovation to the heart of modern enterprises. It powers everything from customer service chatbots to complex supply chain optimizations. But deploying AI at scale brings a unique challenge: ensuring these systems are reliable, unbiased, and capable of meeting business goals. Testing such systems-quickly and effectively-is now a critical enterprise priority.

Unlike traditional software, AI applications evolve with use, learning from data and adapting their behavior. This dynamic nature makes them incredibly powerful but also challenging to manage. Missteps in AI can ripple through operations, resulting in flawed predictions, inefficiencies, or even reputational harm. The solution lies in rethinking the testing paradigm to align with AI's unique demands.

SCALABLE AUTOMATION
Traditional software testing methods lean heavily on manual processes. For AI, this is impractical. AI systems require automated testing frameworks capable of managing their scale and complexity.Automated tools accelerate the testing process, enabling organizations to identify issues quickly and refine their systems before deployment. Consider an AI tool used for fraud detection in financial transactions. It needs to process thousands of data points, identify anomalies, and flag risks-all in real time. Automated testing allows this tool to be stress-tested across countless scenarios, ensuring it performs reliably even under high-volume conditions.

Automation also enables enterprises to focus on high-value improvements. By streamlining repetitive test cases, teams can dedicate their energy to refining model accuracy or addressing edge cases, rather than getting bogged down in routine checks.

CONTEXT-AWARE TESTING
AI applications don't exist in a vacuum-they operate in dynamic, real-world conditions. Effective testing must account for this complexity by incorporating relevant context into the process.
Take the example of an AI-driven hiring platform. It needs to evaluate candidates fairly, irrespective of factors like age, gender, or ethnicity. Without context-aware testing, the system might unintentionally
replicate biases present in the training data. To avoid this, organizations must design tests that reflect the diverse environments in which the system will function.


Context-aware testing also ensures better user experiences. Whether it's a personalized shopping recommendation or an AI assistant for healthcare, the ability to adapt to user-specific needs and cultural nuances is critical. Designing context-aware simulators and test scenarios that mimic these conditions helps guarantee relevance and reliability.

REAL-WORLD VALIDATION
AI systems are continuously evolving entities, so testing doesn't stop once deployed. Instead, organizations must embrace iterative real-world validation, evaluating performance under live conditions and refining the system based on feedback.Careful determination of factors that influence the AI application's behaviour (identify context variables) like user demographics, environmental conditions, and system characteristics requires selection more precisely.

Real-world validation involves observing how AI interacts with users, data, and other systems in production. For example, an AI tool used for customer support might initially perform well, but unexpected queries or regional variations could challenge its accuracy. Monitoring these interactions allows teams to make targeted adjustments, improving long-term outcomes.
Feedback loops are essential here. By incorporating user input and live performance data into the testing cycle, organizations can ensure their AI systems adapt and improve continuously. This iterative approach not only enhances reliability but also drives ongoing innovation.

THE PATH FORWARD
Scalable automation ensures efficiency, context-aware testing delivers relevance, and real-world validation bridges the gap between development and deployment.In the race to innovate, enterprises must not ignore testing rather make it an integral part of their AI strategy. Those who can balance speed with rigor will not only build trust in their AI systems but also unlock their full potential to transform business operations. Testing at the speed of innovation isn't just a necessity; it's the foundation for lasting success in the AI era.


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Innovation Design and Entrepreneurship bootcamp from today

Nurturing innovation, design, and entrepreneurial skills of student innovators and innovation ambassadors from PM SHRI schools as the focus, the Department of School Education and Literacy, along with All India Council for Technical Education (AICTE) and Ministry of Education Innovation Cell (MIC), will organise a two-day bootcamp, starting Wednesday, at Vardhaman College of Engineering, Shamshabad.


In collaboration with Wadhwani Foundation, the camp will see the participation of school principals, teachers and secondary and senior secondary school students. Around 1,175 participants from various PM SHRI schools from across 10 States have completed the registrations for the event.


According to organisers, the programme will be virtually inaugurated by AICTE Chairman T.G. Sitharam and Additional Secretary Vipin Kumar (DoSEL) from New Delhi. Chief Innovation Officer, Ministry of Education Innovation Cell, Abhay Jere and officials from the Department of School Education will take part in the inaugural ceremony here.

Participants of the two-day bootcamp will receive training in adopting a human-centric approach for identifying opportunities, mastering design thinking tools, and enhancing skills, necessary for developing customer-centric products or services.

Additionally, participants will explore fundamental business models and conduct preliminary calculations for their ideas and start-up concepts. The bootcamp also seeks to enhance participants’ understanding of the Indian entrepreneurial landscape, available opportunities for school children, and support framework provided by the Government of India.

A series of group activities, immersive working sessions, and engaging workshops, all tailored to ignite creativity and problem-solving skills will be part of the participants’ experience.

With an emphasis on fostering collaboration and interactive learning, participants will explore the questions of “what, why and how” of innovation. One will gain valuable insights into the significance of innovation in education, uncover the practical strategies for driving change and harness the power of entrepreneurial thinking, the organisers explained.

Visit:https://innovatorawards.org/

Daily Horoscope Prediction says, Discover New Opportunities and Emotional Connections

Pisces, today brings an opportunity for exploration and connection. Use this day to seek new possibilities and deepen emotional bonds with loved ones. In your career, focus on collaboration and innovation. Financially, remain cautious and prioritize stability. Lastly, make time for self-care and pay attention to your mental and physical well-being. Approach each aspect of your life with openness and curiosity for rewarding outcomes.


Pisces Love Horoscope Today

In the realm of love, Pisces can anticipate meaningful emotional connections. Whether single or in a relationship, your empathetic nature will be especially appealing today. Engage in heart-to-heart conversations and express your feelings openly. This is an ideal time to show vulnerability and strengthen bonds with those you care about. New relationships may start on a deeper level, offering long-term potential. Trust your instincts and embrace the emotional journey with your partner or potential love interest.
Pisces Career Horoscope Today

Today, Pisces should focus on collaboration and innovation at work. You may find yourself inspired by new ideas or projects that demand teamwork. Embrace the creative energies around you and contribute your unique perspective to group efforts. Networking opportunities might arise, offering a chance to forge valuable professional connections. Keep an open mind and be willing to adapt to changes in your work environment. Your adaptability and creativity will shine, leading to potential career growth and recognition.
Pisces Money Horoscope Today

Financially, Pisces should prioritize stability and careful planning today. It's important to assess your current budget and spending habits, making adjustments where necessary. Avoid impulsive purchases or high-risk investments; instead, focus on building a secure financial foundation. If you're considering a major purchase or investment, take the time to research and consult with trusted advisors. Maintaining a balanced approach will help you navigate financial challenges and ensure long-term security.
Pisces Health Horoscope Today

When it comes to health, Pisces should focus on balance and self-care. Pay attention to both your physical and mental well-being, incorporating activities that nurture both. Consider engaging in mindfulness practices, such as meditation or yoga, to alleviate stress and maintain emotional equilibrium. Ensure that you’re getting adequate rest, nutrition, and exercise to support your body's needs. Listening to your body and prioritizing relaxation will help you maintain energy and a positive mindset throughout the day.


Pisces Sign AttributesStrength: Conscious, Aesthetic, Kind-hearted
Weakness: Sentimental, Indecisive, Unrealistic
Symbol: Fish
Element: Water
Body Part: Blood Circulation
Sign Ruler: Neptune
Lucky Day: Thursday
Lucky Color: Purple
Lucky Number: 11
Lucky Stone: Yellow Sapphire


Pisces Sign Compatibility ChartNatural affinity: Taurus, Cancer, Scorpio, Capricorn
Good compatibility: Virgo, Pisces
Fair compatibility: Aries, Leo, Libra, Aquarius
Less compatibility: Gemini, Sagittarius

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New IBV study: AI drives mainframe innovation

How AI impacts mainframes

The study shows that 78% of IT executives surveyed said their organizations are either piloting projects or operationalizing initiatives that incorporate AI capabilities into mainframe applications and transactions. The study found that organizations view the mainframe as an invaluable platform for deploying enterprise AI for a wide array of functions including driving innovation, bolstering cybersecurity, streamlining operations, and modernizing applications. For example:Propelling innovation – 79% of respondents agree that the mainframe is essential for enabling AI-driven innovation and value creation. By applying AI directly to transactional workloads on the mainframe, businesses can extract new insights and enhance workforce productivity. This maximizes the value of their core systems and drives meaningful business outcomes.Boosting security – In terms of security, 90% of surveyed executives indicated their organizations are piloting or implementing AI-powered cybersecurity projects; with 82% of those surveyed citing the importance of the mainframe for monitoring, analyzing, and responding to cyber threats, a system already known for its security prowess.Modernizing system management – 74% of respondents said that they are integrating AI into mainframe operations to enhance system management and maintenance. Meanwhile, 61% of executives said using generative AI (gen AI) for application modernization efforts on mainframes is important to their organization. Gen AI-driven application modernization tools are revolutionizing mainframe modernization strategies, accelerating time to value, and closing mainframe skills gaps by enabling developers to modernize or build applications faster and more efficiently.
Mainframes as part of the hybrid cloud

Mainframes also play a vital role in successful hybrid cloud strategies, enabling organizations to deploy application workloads in the most suitable computing environments. Critical workloads, such as financial transactions, healthcare records, and government services, demand advanced security, reliability, and scalability. By using the mainframe for these workloads, organizations can tap into its optimized architecture for unparalleled performance and efficiency, minimizing costs and risks across their IT ecosystem.

This approach has been proven to deliver significant returns on investment, with hybrid-by-design companies achieving more than 3x higher returns from their digital transformation efforts. However, to fully realize these benefits, organizations must maintain mainframe systems to ensure currency, modernize mainframe applications and integrate them with distributed data, applications, clouds and modern development practices.
Unlock the full potential of hybrid cloud and AI

As organizations advance in their digital transformation journeys, they must leverage their existing investments in mainframes to unlock the full potential of their data with hybrid cloud and AI technologies.

IT leaders looking to revitalize their mainframe strategies should consider a few critical actions now:
Be intentional about your IT foundation.

To establish a solid IT foundation, organizations should commit to a hybrid-by-design strategy, modernize mainframe applications, and develop a clear integration and data-sharing strategy. This involves optimizing business value across the technology estate, creating an application modernization strategy aligned to business objectives, and prioritizing integration between mainframe and other technologies for seamless data exchange and API connectivity. By doing so, organizations can improve overall competitiveness, reduce costs, and enhance their ability to respond to changing business requirements.
Lean into AI innovation.

Organizations should leverage AI to empower DevOps teams, enhance mainframe operations, and infuse AI into business transactions. This can be achieved by equipping developers with gen AI-assisted tools that accelerate application discovery, analysis, and modernization; improving operational functions with smart aids and next-generation chatbot assistants; and leveraging AI for in-transaction insights to enhance business use cases. By embracing AI innovation, organizations can streamline modernization, improve operational efficiency, and drive business success in a hybrid cloud and AI-driven world.
Invest in advanced mainframe capabilities and skill sets.

Organizations should leverage today’s mainframe capabilities, cultivate a diverse and skilled mainframe workforce, and upskill mainframe professionals with AI-tooling and collaborative initiatives. This involves leveraging new mainframe capabilities, such as advanced encryption and authentication, and advanced processor chips and specialized AI accelerators; addressing skills gaps through targeted skilling initiatives and mentorship programs; and empowering mainframe professionals with AI-tooling, assistants and collaborative initiatives like the Mainframe Skills Council.
Accelerate your digital transformation

As the era of hybrid cloud and AI unfolds, the mainframe’s staying power is more evident than ever. Its role as a strategic asset in helping ensure security, data privacy, and operational efficiency makes it indispensable for organizations striving to remain competitive. By embracing modernization strategies that leverage the mainframe’s strengths, organizations can accelerate their digital transformation journeys and unlock new opportunities for growth and innovation.

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Innovation unleashed: Tummers and Siemens partner to digitize potato processing

on October 8, 2024

Tummers Food Processing Solutions in the Netherlands last week announced a new strategic partnership with Siemens. This collaboration is said to mark a significant milestone in the digital transformation of the potato processing industry and underscores Tummers’ ongoing commitment to innovation, efficiency, and sustainability.

The company says in a press release that it is renowned for its innovative solutions in washing, peeling, cutting, and drying technologies, and has recently expanded its product portfolio with the ambitious E2E (Emission to Energy) programme.

Now, the company is adding a powerful digital layer to its machinery with the introduction of Tummers Connect, a groundbreaking platform that makes machine management simpler and more accessible than ever before.

With Tummers Connect, users can not only monitor the operational status of their machines via an intuitive dashboard but also consult manuals, order spare parts, view service history, and even adjust machine settings. This offers an unprecedented level of control, flexibility, and convenience for the food processing industry.

The partnership with Siemens plays a crucial role in the development of this platform. Siemens brings decades of expertise in automation and digital solutions, ensuring that the technology is safe, efficient, and future-proof.

“We have found a strong and experienced partner in Siemens,” said Lennaert van Dijk, Managing Director of Tummers. “When the opportunity for collaboration arose, we seized it with both hands. Thanks to the existing connections with Neeraj Singh from Tummers and Noorie Ahmed from Siemens, the foundation for this partnership was quickly established.”

The official announcement of the collaboration took place during Anuga FoodTec, and after a period of intensive cooperation, the partnership has now been officially confirmed by Lennaert van Dijk and Dirk de Bilde, CEO of Siemens Netherlands N.V., during a visit to Tummers. Dirk De Bilde calls it a great example of how a company gets started with digitization from a customer-oriented vision.

The first results of this partnership will be showcased at Interpom, where Tummers and Siemens will jointly present the next phase of Tummers Connect.

“We are proud to welcome Siemens as a partner in this exciting project. Together, we are taking an important step towards a smarter, digital future for the potato processing industry,” said Lennaert.

Source: Tummers Food Processing Solutions
Image: Official signing of the agreement. Credit Tummers