After the AI binge, companies balk at soaring bills
ByAFP
May 30, 2026
Prices are rising across the board, and one big reason is AI agents. — © GETTY IMAGES NORTH AMERICA/AFP Michael M. Santiago
Thomas URBAIN
Artificial intelligence is getting expensive — and companies are starting to rethink their embrace of the disruptive technology.
Playing by a well-worn Silicon Valley playbook, AI companies charged rock-bottom prices to hook customers after ChatGPT burst onto the scene.
Kevin Simback of startup incubator Delphi Labs calls it the era of “subsidized intelligence” — meaning investors were basically footing the bill so companies could offer AI on the cheap.
“But the tides are beginning to turn,” Simback warned and an era where the big AI companies actually need to make money has begun — with leaders OpenAI and Anthropic looking to go public and attract main street investors later this year.
Prices are rising across the board, and one big reason is AI agents.
Unlike a chatbot that just answers questions, agents actually do things — book appointments, write code, manage files. And they’re expensive to run, because one task can spin up dozens of agents all working at once, each racking up charges.
Those charges are measured in tokens — the basic unit AI companies use to bill customers. A single agent-powered task can burn through dozens of times’ more tokens than a simple chat message.
Meanwhile, the computer chips and data centers needed to power all this AI can’t keep up with demand, creating computing shortages and adding further uncertainty to the nascent industry.
“Especially in developer circles, the cost to use AI for things like coding has grown exponentially,” said Mark Barton of tech consultancy Omniux. “All the costs are really starting to skyrocket.”
Some companies have been so eager to use AI that they’ve gone overboard in a usage binge called “tokenmaxxing.”
“In some cases people are seeing the cost of tokens exceed the cost of the employee within a month or two of use, just because they’re using it too much,” says analyst Jack Gold of J.Gold Associates.
– Smarter spending –
Even Meta — which earlier this year encouraged employees to use as many tokens as possible as a measure of productivity — has had second thoughts.
“Nobody should be using AI tools just for the sake of using them,” chief technology officer Andrew Bosworth wrote in a memo to staff, reported by the Wall Street Journal.
Uber’s chief operating officer this week went a step further, raising eyebrows by saying all this AI spending was showing no noticeable increase in productivity.
To cut costs, some companies are switching to free, open-source AI models that anyone can download — not as powerful as ChatGPT or Anthropic’s Claude, but good enough for many tasks.
Others are moving to smaller, more specialized models built for specific industries like real estate or finance, rather than giant general-purpose ones.
And some are simply breaking big AI tasks into smaller steps, handing each piece to the cheapest model that can handle it.
The price difference can be dramatic.
“The big large monolithic model, it’s $15 per million tokens, but you can get that down to like five cents if you use the smaller mini model,” says Adrian Balfour of consultancy Enverso.
All of this points to AI becoming more like a commodity — where the specific model matters less than finding the right one at the right price.
But don’t count out the big players and their state-of-the-art models just yet.
“The most advanced users” will always be willing to pay for the best, says John Belton, a portfolio manager at Gabelli Funds.
“It’s a growing pie.”
Global AI boom comes with a power bill: Inside ChatGPT’s explosive growth
By Dr. Tim Sandle
DIGITAL JOURNAL
May 30, 2026
Image: — © AFP/File SEBASTIEN BOZON
ChatGPT has moved from novelty to infrastructure. With an estimated 900 million weekly active users and roughly 1.17 trillion prompts processed annually, OpenAI’s flagship chatbot now operates at a scale comparable to the largest digital platforms. But as adoption accelerates, so does a less visible metric: energy consumption.
A new analysis from the firm BestBrokers, reviewed by Digital Journal, offers a striking snapshot of how global demand for AI is distributed—and what it costs in computational terms. The findings highlight two key trends shaping the AI era: the rapid rise of emerging markets and the growing energy footprint of large-scale AI systems.
A Global Shift: Emerging Markets Take the Lead
One of the most notable developments is the geographical redistribution of AI usage. While the United States remains a dominant force in the tech ecosystem, it is no longer the largest user of ChatGPT.India now leads globally, generating approximately 13.2 billion prompts per month
The U.S. follows with 11.9 billion
Emerging economies such as Brazil, Indonesia and the Philippines rank prominently
This shift reflects broader digital trends. Large, mobile-first populations are adopting AI tools rapidly, often leapfrogging traditional desktop computing. In contrast to earlier waves of internet growth—where Western markets dominated—AI adoption is proving far more evenly distributed.
The implications are significant: the “centre of gravity” for AI usage is moving toward the Global South, reshaping where infrastructure investment, regulation and innovation pressures will concentrate.
Europe and the UK: High Adoption, High Intensity
Within Europe, usage remains strong but more concentrated. The United Kingdom stands out as a high-intensity market:37.1 million monthly visits
2.44 billion prompts per month
Around 35 prompts per person each month
This translates to roughly one prompt per person per day, a level of engagement that suggests AI is becoming embedded in everyday workflows. The UK ranks behind France, Germany and Spain in total traffic, but per-capita usage remains among the highest.
This pattern reflects the UK’s position as a mature digital economy, where AI is being rapidly integrated into sectors such as finance, media, education and professional services. The trend is particularly pronounced among knowledge workers, who increasingly rely on AI for drafting, coding, summarisation and research.
The Energy Equation: AI as a Power-Hungry Platform
Behind this growth lies a critical constraint: energy. AI systems—especially large language models—require vast computational resources to process queries in real time. The energy consumption is, as of March 2026:
Wh kWh MWh GWh Per day 60,685,714,286 60,685,714 60,686 60.7 Per week 424,800,000,000 424,800,000 424,800 424.8 Per month 1,907,958,857,143 1,907,958,857 1,907,959 1,908.00 Per year 22,150,285,714,286 22,150,285,714 22,150,286 22,150
The survey analysis estimates:~18.9 watts of energy per prompt
Over 22 billion kWh annually to run ChatGPT globally
To put this in perspective, that places AI infrastructure firmly in the category of large-scale industrial energy consumers.
For the UK alone:Monthly demand exceeds 46,000 megawatt-hours
Annual usage surpasses 550 gigawatt-hours
This is equivalent to the output of a large nuclear power plant running continuously for weeks.
At global scale, the cost is equally striking. If powered entirely from U.S. grid electricity, operating ChatGPT at current levels would exceed $8 million per day in energy costs.
Scaling Pressures: Efficiency vs Capability
This raises a central tension in AI development: the trade-off between performance and efficiency.
Modern AI models are becoming:Larger (more parameters)
More capable (reasoning, multimodal inputs)
More widely used (consumer and enterprise integration)
All three trends increase computational demand. At the same time, industry efforts are underway to reduce energy intensity through:More efficient model architectures
Specialised AI chips (GPUs, TPUs, custom silicon)
Data centre optimisation and cooling innovations
However, efficiency gains are often offset by rising demand—a classic “rebound effect” seen in other technology sectors.
AI in the Workplace: Productivity and Pressure
The data also reflects a broader shift in how work is being performed. In countries like the UK, AI is now deeply embedded in professional environments, where it is used to increase output and streamline tasks.
This has clear productivity benefits. But it also introduces new dynamics:Workers are expected to produce more in less time
Routine cognitive tasks are increasingly automated
The boundary between human and machine-generated work is becoming blurred
The result is a form of “AI augmentation” that may enhance efficiency while raising concerns about overwork, job displacement and skill erosion.
A Platform at Scale
ChatGPT’s growth marks a turning point in the digital economy. Unlike earlier platforms—social media, search or streaming—AI is not just distributing content, but actively generating it. This makes it both more powerful and more resource-intensive.
The emerging picture is one of global adoption, rising energy demand and shifting economic geography. Emerging markets are driving usage growth, developed economies are integrating AI deeply into workflows, and infrastructure providers are racing to keep up with demand.
The key question is whether the underlying systems—technical, economic and environmental—can scale sustainably.
As AI becomes a permanent layer of digital infrastructure, its success will depend not only on what it can do, but on how efficiently it can do it.
SoftBank to spend $87.5bn on AI centres in France: Son
ByAFPMay 30, 2026
Japanese tech investment titan SoftBank. - © AFP Kazuhiro NOGI
Paul Ricard and Djallal Malti
Japanese tech investor SoftBank will spend 75 billion euros ($87.5 billion) on artificial intelligence infrastructure in France, its founder Masayoshi Son told a French newspaper in an interview released Saturday.
“This will be the largest investment in Europe in infrastructure related to artificial intelligence: 75 billion euros in total,” Son told La Tribune Dimanche weekly ahead of a French investment conference hosted by President Emmanuel Macron.
He said it included 45 billion euros to be spent by 2031 on data centres in the Hauts-de-France region of northern France.
French electrics giant Schneider will be a partner in the huge project, its chief executive Olivier Blum told AFP.
“This is a significant partnership, a major project, the largest ever undertaken in France” in the sector, said Blum.
“Up to now, there is roughly 1.5 gigawatts of installed data centre capacity in France at the end of 2025, and what’s being announced now is that there will be an initial phase of 3.0 gigawatts followed by a second phase that could reach up to 5.0,” he added.
The announcement is a major boost to Macron’s efforts to attract hi-tech industries to France, in competition with other European nations.
– Energy exporter –
Macron is to host an international investment conference at Versailles palace from Monday.
Son, 68, said his decision was made after meeting Macron during a visit to Tokyo in April and that France’s status as an energy exporter had played a key role. Data centres are huge consumers of energy.
“The fact that the country is an energy producer and exporter is absolutely crucial for infrastructure investments in artificial intelligence, especially for data centres,” said Son, whose company has an 11-percent stake in the OpenAI giant that runs the ChatGPT chatbot.
The Japanese tycoon said he had also been impressed by Macron’s “strong personal commitment to ensuring France’s economic success, even though our investments have so far been concentrated primarily in the United States, and Japan and Asia”.
Blum said that Schneider would take part in the design and supply of all the equipment with a factory to be built at the channel port of Dunkirk.
The first three data centres would be at Dunkirk and near the northern cities of Cambrai and Amiens, he added.
France says it has 35 venues ready to provide enough energy and other infrastructure for data centres. Macron has repeatedly said that Europe must not let the United States and China take an insurmountable lead in AI.
Son said that “catching up with the United States, currently the global centre of gravity for innovation, is a challenge for most other countries”.
Europe must, he added in the interview with Tribune, “find the right path” to reach a “balance” between innovation and regulation.
A Global Shift: Emerging Markets Take the Lead
One of the most notable developments is the geographical redistribution of AI usage. While the United States remains a dominant force in the tech ecosystem, it is no longer the largest user of ChatGPT.India now leads globally, generating approximately 13.2 billion prompts per month
The U.S. follows with 11.9 billion
Emerging economies such as Brazil, Indonesia and the Philippines rank prominently
This shift reflects broader digital trends. Large, mobile-first populations are adopting AI tools rapidly, often leapfrogging traditional desktop computing. In contrast to earlier waves of internet growth—where Western markets dominated—AI adoption is proving far more evenly distributed.
The implications are significant: the “centre of gravity” for AI usage is moving toward the Global South, reshaping where infrastructure investment, regulation and innovation pressures will concentrate.
Europe and the UK: High Adoption, High Intensity
Within Europe, usage remains strong but more concentrated. The United Kingdom stands out as a high-intensity market:37.1 million monthly visits
2.44 billion prompts per month
Around 35 prompts per person each month
This translates to roughly one prompt per person per day, a level of engagement that suggests AI is becoming embedded in everyday workflows. The UK ranks behind France, Germany and Spain in total traffic, but per-capita usage remains among the highest.
This pattern reflects the UK’s position as a mature digital economy, where AI is being rapidly integrated into sectors such as finance, media, education and professional services. The trend is particularly pronounced among knowledge workers, who increasingly rely on AI for drafting, coding, summarisation and research.
The Energy Equation: AI as a Power-Hungry Platform
Behind this growth lies a critical constraint: energy. AI systems—especially large language models—require vast computational resources to process queries in real time. The energy consumption is, as of March 2026:
| Wh | kWh | MWh | GWh | |
| Per day | 60,685,714,286 | 60,685,714 | 60,686 | 60.7 |
| Per week | 424,800,000,000 | 424,800,000 | 424,800 | 424.8 |
| Per month | 1,907,958,857,143 | 1,907,958,857 | 1,907,959 | 1,908.00 |
| Per year | 22,150,285,714,286 | 22,150,285,714 | 22,150,286 | 22,150 |
Over 22 billion kWh annually to run ChatGPT globally
To put this in perspective, that places AI infrastructure firmly in the category of large-scale industrial energy consumers.
For the UK alone:Monthly demand exceeds 46,000 megawatt-hours
Annual usage surpasses 550 gigawatt-hours
This is equivalent to the output of a large nuclear power plant running continuously for weeks.
At global scale, the cost is equally striking. If powered entirely from U.S. grid electricity, operating ChatGPT at current levels would exceed $8 million per day in energy costs.
Scaling Pressures: Efficiency vs Capability
This raises a central tension in AI development: the trade-off between performance and efficiency.
Modern AI models are becoming:Larger (more parameters)
More capable (reasoning, multimodal inputs)
More widely used (consumer and enterprise integration)
All three trends increase computational demand. At the same time, industry efforts are underway to reduce energy intensity through:More efficient model architectures
Specialised AI chips (GPUs, TPUs, custom silicon)
Data centre optimisation and cooling innovations
However, efficiency gains are often offset by rising demand—a classic “rebound effect” seen in other technology sectors.
AI in the Workplace: Productivity and Pressure
The data also reflects a broader shift in how work is being performed. In countries like the UK, AI is now deeply embedded in professional environments, where it is used to increase output and streamline tasks.
This has clear productivity benefits. But it also introduces new dynamics:Workers are expected to produce more in less time
Routine cognitive tasks are increasingly automated
The boundary between human and machine-generated work is becoming blurred
The result is a form of “AI augmentation” that may enhance efficiency while raising concerns about overwork, job displacement and skill erosion.
A Platform at Scale
ChatGPT’s growth marks a turning point in the digital economy. Unlike earlier platforms—social media, search or streaming—AI is not just distributing content, but actively generating it. This makes it both more powerful and more resource-intensive.
The emerging picture is one of global adoption, rising energy demand and shifting economic geography. Emerging markets are driving usage growth, developed economies are integrating AI deeply into workflows, and infrastructure providers are racing to keep up with demand.
The key question is whether the underlying systems—technical, economic and environmental—can scale sustainably.
As AI becomes a permanent layer of digital infrastructure, its success will depend not only on what it can do, but on how efficiently it can do it.
ByAFP
Japanese tech investment titan SoftBank. - © AFP Kazuhiro NOGI
Paul Ricard and Djallal Malti
Japanese tech investor SoftBank will spend 75 billion euros ($87.5 billion) on artificial intelligence infrastructure in France, its founder Masayoshi Son told a French newspaper in an interview released Saturday.
“This will be the largest investment in Europe in infrastructure related to artificial intelligence: 75 billion euros in total,” Son told La Tribune Dimanche weekly ahead of a French investment conference hosted by President Emmanuel Macron.
He said it included 45 billion euros to be spent by 2031 on data centres in the Hauts-de-France region of northern France.
French electrics giant Schneider will be a partner in the huge project, its chief executive Olivier Blum told AFP.
“This is a significant partnership, a major project, the largest ever undertaken in France” in the sector, said Blum.
“Up to now, there is roughly 1.5 gigawatts of installed data centre capacity in France at the end of 2025, and what’s being announced now is that there will be an initial phase of 3.0 gigawatts followed by a second phase that could reach up to 5.0,” he added.
The announcement is a major boost to Macron’s efforts to attract hi-tech industries to France, in competition with other European nations.
– Energy exporter –
Macron is to host an international investment conference at Versailles palace from Monday.
Son, 68, said his decision was made after meeting Macron during a visit to Tokyo in April and that France’s status as an energy exporter had played a key role. Data centres are huge consumers of energy.
“The fact that the country is an energy producer and exporter is absolutely crucial for infrastructure investments in artificial intelligence, especially for data centres,” said Son, whose company has an 11-percent stake in the OpenAI giant that runs the ChatGPT chatbot.
The Japanese tycoon said he had also been impressed by Macron’s “strong personal commitment to ensuring France’s economic success, even though our investments have so far been concentrated primarily in the United States, and Japan and Asia”.
Blum said that Schneider would take part in the design and supply of all the equipment with a factory to be built at the channel port of Dunkirk.
The first three data centres would be at Dunkirk and near the northern cities of Cambrai and Amiens, he added.
France says it has 35 venues ready to provide enough energy and other infrastructure for data centres. Macron has repeatedly said that Europe must not let the United States and China take an insurmountable lead in AI.
Son said that “catching up with the United States, currently the global centre of gravity for innovation, is a challenge for most other countries”.
Europe must, he added in the interview with Tribune, “find the right path” to reach a “balance” between innovation and regulation.
Columbia-led team develops open-source framework to accelerate health AI research
NEW YORK, NY -- A research team led by Columbia University has developed an open-source framework designed to streamline and accelerate artificial intelligence research using health data, addressing longstanding challenges in data standardization, reproducibility, and collaboration across institutions.
The framework, called MEDS, introduces both a standardized data format and a growing ecosystem of interoperable tools intended to support the development and evaluation of machine learning models using clinical data.
A study describing the framework was published in NEJM AI.
The researchers say the framework could help reduce technical barriers that currently slow health AI research and make it difficult for scientists to reproduce findings or compare models across studies and institutions.
“MEDS is a simple way to make all different sources of electronic health record (EHR) data look the same to your code, regardless of what hospital or clinic or EHR software system the data came from,” says Matthew McDermott, PhD, assistant professor of biomedical informatics at Columbia University and study leader. “MEDS lets us share code that we can use to train models on many different sites of care without needing to share sensitive patient data — and often without needing to even do the more challenging step of fully ‘harmonizing’ the data into a consistent clinical vocabulary. This infrastructure will allow researchers to spend less time rebuilding pipelines and more time answering clinically meaningful questions.”
Standardizing health data for clinical AI research
Electronic health record data are often stored in institution-specific formats that require extensive preprocessing before they can be used for AI development. According to the study authors, these inconsistencies can create significant duplication of effort, limit collaboration, and hinder reproducibility.
MEDS addresses these issues by providing a lightweight, extensible standard for representing longitudinal clinical data in machine learning workflows. The framework also includes open-source tooling that supports data transformation, preprocessing, benchmarking, and model development.
The authors emphasize that MEDS was designed specifically for AI and machine learning applications, complementing rather than replacing existing clinical data standards.
The framework is intended to support a broad range of use cases in biomedical AI research, including predictive modeling, representation learning, multimodal modeling, and large-scale benchmarking studies. Because the ecosystem is open source, researchers across academia, healthcare, and industry can contribute tools and extensions.
“The big successes in AI have always been driven by the community coming together and being able to collaborate, often in a decentralized, open-source manner, on tools, model parts, and ultimately ecosystems that let us build larger models that scale to massive datasets,” McDermott said. “These impressive results in MEDS are just reflecting the benefits you get when the community can share tools or abstract common parts of their pipelines out into a shared library and use them across everyone's data.”
The study also highlights the importance of reproducibility and transparency in health AI development as machine learning models increasingly move toward clinical deployment.
The researchers say they hope MEDS will foster broader collaboration across institutions and accelerate innovation in clinical AI while promoting more transparent and reproducible science. Already, MEDS has been adopted across 21 institutions spanning 12 countries.
###
Columbia University Irving Medical Center (CUIMC) is a clinical, research, and educational campus located in New York City. Founded in 1928, CUIMC was one of the first academic medical centers established in the United States of America. CUIMC is home to four professional colleges and schools that provide global leadership in scientific research, health and medical education, and patient care including the Vagelos College of Physicians and Surgeons, the Mailman School of Public Health, the College of Dental Medicine, the School of Nursing. For more information, please visit cuimc.columbia.edu.
Journal
NEJM AI
Method of Research
Computational simulation/modeling
Article Title
MEDS — An Emerging Data Standard and Ecosystem for Health AI Research
Article Publication Date
28-May-2026
Insilico Medicine to showcase AI-driven innovation at BIO 2026 International Convention
Company to deliver three presentations spanning AI drug discovery, quantum computing, and next-generation pipeline strategy
image:
BIO 2026
view moreCredit: BIO 2026
Cambridge, MA, May 29, 2025 — Insilico Medicine ( “Insilico”, HKEX:3696 ), a clinical-stage, generative AI–driven drug discovery company, today announced the company will showcase its latest advances in AI-driven drug discovery, quantum-enabled research, and clinical development through three featured speaking sessions at the BIO 2026 International Convention on June 22-25 at the San Diego Convention Center.
Led by Alex Zhavoronkov, Ph.D, founder , co-CEO and CBO of Insilico Medicine, the Insilico team will be meeting with biopharma partners, investors, and researchers to explore collaboration opportunities at Booth # 4021. At the event, Insilico will showcase the capabilities of its end-to-end Pharma.AI platform and latest pipeline.
As a recognized leader in generative AI–driven drug discovery, Insilico Medicine will present the following featured BIO 2026 speaking sessions:
ADCs, GLP-1s, and Beyond: How China is Impacting the 2026 BD Landscape Jun 22 3:00 PM - 4:00PM at 32AB
Quantum Computing in Drug Discovery June 22 4:15PM - 5:15PM at 28ABCDE
Strategic Innovation: Building Smarter Pipelines for Challenging Targets June 25 9:00 AM - 10:00 AM at 29AB
The synergy among AI-driven drug design (AIDD), quantum algorithms, and automation labs are becoming the next-gen engine redefining target identification and compound optimization. At the same time, de-risking early science and investments hinges on meticulous program design, early validation strategies, and milestone-driven execution. As a pioneer deeply embedding AI into drug discovery, Insilico Medicine will share unique insights from the intersection of cutting-edge tech and clinical translation.
“BIO 2026 International Convention is a premier event for fostering the collaborations that drive biotech innovation” said Alex Zhavoronkov, PhD, Founder, co-CEO and CBO of Insilico Medicine. “We look forward to engaging with industry leaders and demonstrating how our Pharma.AI platform, LifeStar 2 laboratory and MMAI Gym for Science are accelerating the discovery of novel therapeutics. Our goal is to forge partnerships that will help enable longer, healthier lives for people around the world.”
Since pioneering next-generation AI in drug discovery, Insilico Medicine has built an extensive therapeutic portfolio across a variety of therapeutic areas, rapidly advancing its internal R&D pipeline and setting a new standard for efficiency. While traditional early-stage drug discovery can take 2.5 to 4 years, Insilico has nominated 30 preclinical candidates at an average pace of just 12 to 18 months per program, synthesizing and testing only 60 to 200 molecules each—highlighting the exceptional capabilities of its AI-driven platform.
Among the company's clinical-stage programs, Rentosertib, the world's first AI-discovered novel-mechanism anti-fibrotic candidate, has completed Phase 2a proof-of-concept clinical trial, demonstrating promising efficacy trends and a favorable safety profile. ISM5411, the PHD1/2 inhibitor with best-in-class potential for treating inflammatory bowel disease (IBD) has completed 2 Phase 1 trials, showing good safety and gut-restrictedPK profile. Additionally, three of Insilico's anti-tumor programs have now reached the first-in-patient dosing stage, and interim results are expected to be shared in the near future.
Since founding in 2014, Insilico has published over 200 peer-reviewed papers. Leveraging sustained scientific breakthroughs at the intersection of biotechnology, artificial intelligence, and automation, Insilico ranked Top 100 global corporate institutions in Nature Index's "2025 Research Leaders: global corporate institutions for biological sciences and natural sciences publications".
About Insilico Medicine
Insilico Medicine is a pioneering global biotechnology company dedicated to integrating artificial intelligence and automation technologies to accelerate drug discovery, drive innovation in the life sciences, and extend healthy longevity to people on the planet. The company was listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025, under the stock code 03696.HK.
By integrating AI and automation technologies and deep in-house drug discovery capabilities, Insilico is delivering innovative drug solutions for unmet needs including fibrosis, oncology, immunology, pain, and obesity and metabolic disorders. Additionally, Insilico extends the reach of Pharma.AI across diverse industries, such as advanced materials, agriculture, nutritional products and veterinary medicine. For more information, please visit www.insilico.com
Frontiers wins two awards at the 2026 EPIC Awards for pioneering work on AI in publishing and research integrity
Frontiers wins a gold award and a silver award at the 2026 EPIC Awards of the Society for Scholarly Publishing – for a landmark whitepaper on AI in research and publishing, and a digital campaign making research integrity visible.
Frontiers
Frontiers has won a gold award and a silver award across two categories at the 2026 Excellence in Publishing, Information Technology & Communications (EPIC) Awards, presented by the Society for Scholarly Publishing (SSP) during its 48th Annual Meeting in Chula Vista, California, US, on 28 May 2026.
A roadmap for responsible AI use in research and publishing
Frontiers’ whitepaper Unlocking AI’s untapped potential: responsible innovation in research and publishing received the gold award in the Reports category. Published in December 2025, it is the first large-scale study to examine AI adoption, trust, training, and governance across authoring, reviewing, and editorial workflows.
Drawing on a global survey of 1,645 active researchers, the whitepaper found that 53% of peer reviewers already use AI tools. AI adoption is rising rapidly across the wider research community, reaching 87% among early-career researchers. Frontiers translates these findings into evidence-based policy recommendations for publishers, institutions, funders, and tool developers – a practical roadmap to align publishing policy with how researchers are already using AI, and to unlock AI’s full potential to strengthen scientific rigor, reproducibility, and trust.
Elena Vicario, Director of Research Integrity at Frontiers, said:
“The whitepaper shows that AI use in research is already happening, at scale, across every region and career stage. The question is whether our policies are keeping pace, and how we translate this momentum into stronger, more transparent, and more equitable research practices. Winning the gold award signals that the industry is ready to address that question now, and Frontiers is proud to be leading that conversation.”
Guardians of Science: making research integrity visible
AI-generated content, misinformation, and papermills are reshaping trust in science. Frontiers’ digital campaign, Guardians of Science: unveiling the human force behind research integrity, answers the question: who protects the scientific record, and how?
Led by Frontiers Brand team, the campaign received the silver award in the Narrative / Multidisciplinary Digital Projects category. Through an integrated multimedia experience combining video, editorial storytelling, and social-first distribution, the campaign spotlights our Research Integrity team and the advanced technology they use. Their expertise, judgment, and ethical oversight guide every review. A first-of-its-kind podcast-style interview with Frontiers AI Review Assistant, AIRA, makes complex detection technology accessible, human, and engaging. The “Day in the life” interview series brings our Research Integrity and Auditing teams into focus, affirming transparent, expert-led, and responsibly AI-enabled quality control as the bedrock of trust and credibility.
Gilbert De Gregorio, Director of Communications at Frontiers, said:
“The Guardians of Science campaign transformed the abstract concept of research integrity into compelling, human-centered stories. Winning the silver award demonstrates that trust has to be earned in plain sight. Giving a voice to our Research Integrity team – and even to our AI Review Assistant, AIRA – helped reframe conversations about research integrity, quality assurance, and the role of AI in publishing.”
Now in its second year, the EPIC Awards celebrate outstanding achievements in scholarly publishing and have drawn more than 98 entries in 2026. Frontiers' two awards recognize our work on two fronts: setting a roadmap for responsible AI use in research and publishing, and upholding research integrity. Together, they reinforce our commitment to making science open and trusted.
By Jennifer Kervin
May 29, 2026
Johnny Than, CEO and founder of Appficiency and founder of AskCipher (Photo courtesy of Monia Khan)
Gareth Doherty asked the room a question most leaders are sitting with but rarely say out loud.
What does it really mean to trust something you can’t fully explain?
“I don’t know exactly how all the neurons in your brain work,” said Doherty, executive leader at Think/able Solutions, at a Toronto Tech Week panel discussion hosted by Appficiency, SnapOn Software, and AskCipher.
“But if I can reliably predict based on some ask of you that you’re going to behave in the exact same way, which is every person in your organization. We don’t get so tripped up about what’s going on in your thought process. What I care about is that you behave in the right ways in the right context.”
Unsurprisingly, Doherty was talking about AI. Specifically, what’s paralyzing enterprise adoption right now.
You don’t need to understand how something works to trust it. (Bold, but let’s hear it out.)
You just need it to behave predictably. (Of course.)
With most enterprise AI, at this stage, it doesn’t. (Ding ding ding!)
Moderated by Johnny Than, CEO and founder of IT consulting firm Appficiency and founder of AskCipher, the discussion brought together Doherty, Alex Miles, partner at 180 Systems, and Shayan Rastgou, co-founder of AskCipher, all practitioners with enough deployment chops to know what is and isn’t working, and why most organizations haven’t moved beyond a few haphazard tools.
Think of this as a field report from the front lines of enterprise AI, built from the yet-to-be-answered questions many decision makers are carrying into their next meeting.
What nobody talks about in the press release
Than opened the session by walking through the AI-assisted hiring process step by step, from job description to final candidate selection, as an exercise in showing where enterprise AI breaks or brings friction.
AI has been involved in hiring for a while now, and each stage surfaces a different kind of failure. Off the bat, there’s technical friction when the model doesn’t know what a job really requires at your company, so it takes a guess.
Human friction comes along when a recruiter can’t trust what the screening agent lets through.
Bringing up the rear, organizational friction comes calling when the scheduling tool books the first available slot for an interview. And nobody wants their first available slot of the day to be an interview.
Than named these failures systematically, before coming in with the big guns.
AI reveals risk at scale.
“Everyone’s using it,” he said, “but nobody’s trusting it. That’s really where we’re going.”
The trust problem shows up in practice faster than most organizations expect.
In an interview after the panel, Than explained how a client tried to build a live executive dashboard by connecting an AI directly to their Enterprise Resource Planning (ERP) system using Model Context Protocol (MCP), an open-source standard that lets AI models pull live data from external applications.
While simple enough on the surface (budget versus actual, year to date), the data they needed lived across multiple systems, not just the one the AI was connected to.
What started as a dashboard project became an integration project, then a data rationalization project. Like many teams that end up too deep, the project was abandoned.
A second client ran into a different wall. Their AI was consolidating purchase orders to build a bulk vendor order, the kind of exercise that can unlock better pricing at scale. The model hallucinated the order total, and while to an outsider it seems like a small error, one or two per cent, the trust was gone and the brakes were pumped.
In the panel discussion, Rastgou offered what it looks like when an organization works through the friction instead of abandoning the project.
AskCipher, an AI-powered interface layer, was rolled out to their major client, Appficiency, where it sees about 200 to 300 active daily users. These employees use the AI across three main areas: ERP implementation, code development, and communication.
Rastgou explained that humans expect AI to act like traditional, deterministic software that flawlessly does the same thing every time. Because AI is probabilistic and inherently hallucinates, users get frustrated when it makes mistakes.
“When you put humans in front of an AI, they go into two categories: they either expect it to do everything very well every time, or they expect it to be able to reason like a human,” Rastgou said. “They become angry, and then they get into their seats, and they yell at the AI, and the AI won’t do anything good for them”
By testing the tool with a diverse group to find all the ways it fails, though, the team was able to adjust expectations and build around the failure points.
What they did see was a 25% improvement in run rates, yielding roughly $5 million a year in efficiency gains against an investment of over $2 million over two years.
“If we take the time to actually provide it with the right context, both the AIs and the humans interacting with it,” Rastgou advised, “then it would be a lot less of a friction implementation.”
Doherty pointed to one organization he’s working with, where a team of PhD-level qualitative analysts resisted using AI to help analyze unstructured customer feedback.
Their concern is, of course, whether AI tools could put them out of a job.
But he argued the larger cost is what the organization loses while that resistance continues. These analysts could be spending more time on deeper research and analysis, with AI speeding up the first pass. He estimated the opportunity cost of not redeploying that expertise at roughly $600,000 to $800,000 a year.
The adoption problem, he said, is that while these tools are in the organizations, and employees are getting creative, we aren’t quite there “in terms of creating the culture and the know-how to be able to engage.”
“If you’re going to start small, you want to start with your more successful teams and try to make them better. It’s the best way to pilot. You’re going to learn a lot more,” Doherty said.
“You’re going to have engaged people, they will know their business processes better than poor performing parts of your organization as well. They’ll understand their workflows much better and to be more engaged at being able to get successful and learn as you grow. Because I think, honestly, adoption really is a learn-as-you-grow.”
The knowledge economy just changed
For most of our professional lives, knowledge was the moat.
For example, knowing how a specific application worked or being able to compare CRM platforms. Generative AI, however, has commoditized a significant portion of it.
“What used to be ‘I know all the stats of every restaurant in the world’ is now instantly available with one prompt,” Than said. “That concept of knowledge being your moat has changed.”
Instead, judgment is taking its place. Than used a hiring scorecard as an example.
Train an AI on your scorecard and it will apply it consistently at scale. That’s a knowledge economy problem, solved. Ask the AI to build you the scorecard and you’re in different territory.
The output might be better than anything you could’ve made yourself, for all you know. Now, however, someone has to read it, interrogate it, reverse-engineer how it made its choices, and decide whether those choices were right.
Ta-da! Judgment economy.
Every enterprise Than works with has built a series of checks and balances around AI output. Spending time evaluating what the model produces is, for now, the normal state.
Than said the people you need to hire are the ones who can evaluate, challenge, and apply what the models produce.
Miles made the same point through a client example. A company heading into a private equity sale needed a full ERP implementation completed in under two months, a timeline that would historically have been impossible.
Using AI for data migration mapping and automated report generation, the team compressed what is typically a nine-month process, freeing the team to better focus on value for their clients.
“If we didn’t hit that date,” Miles said, “it would have been catastrophic for the organization.”
The practical part
The panel closed with a question Than put to the room: if you walked out from the session today and wanted to start training yourself on AI tools, what should you do?
“The short answer is use it,” Miles said. Pick one business process, something that takes two hours, and see what you can automate.
Doherty quoted Walid Hejazi, a program lead at the Rotman School of Management, who said “When we focus on technology rather than business objectives, the strategy becomes about inputs instead of outputs.” Start with the outcome, he advised, and work backwards, making sure there’s a foundation of AI literacy in your organization.
Rastgou focused on communication.
“The biggest element of getting what we want out of the LLMs is how well we communicate with them,” he said.
Sign up for several models, he suggested, learn each one’s character, and build from there.
Than’s answer to his own question was a different kind of practical. The clearest signal that a company is going to get real value from AI, he said, is that they assigned a human to figure it out. Not a software purchase. A person with a mandate and a question.
In our interview after the panel, he described an eight-person accounts payable team struggling to find efficiency. The company assigned one person to find out where the time was going and what might help.
They interviewed their teammates, looked at available tools, picked one, and ran with it. The tool handled optical character recognition on incoming vendor invoices, routed approved vendors directly into the system, and generated the bank payment files. Two people had been doing that work. One of them was freed up for analysis.
The process that remained ran with AI assistance.
“What would you normally hire someone to do to solve the problem?” Than said. “Take that, and see how much of it you can do with AI first before you hire the person. Because maybe that person can come in and do something way more interesting for you.”
In the judgment economy, the hire still happens, but they just arrive at a different job.
At the risk of sounding like a kindergarten teacher, it’s easier to maintain patience when you remember how new all of this is.
“Widely available LLMs were 2024, and we’re halfway through 2026,” said Doherty, “so the idea that there’s a robust database of experiences to be able to share and help you with your adoptions just isn’t there. We’re all kind of learning together.”
It’s easy to feel left behind, but everyone is somewhere on the curve.
“The things that are really challenging on the AI side [are] less about technology, more about people,” he added, “so one of the main barriers is really about digital literacy and AI literacy specifically.”
Your data has more layers than you think
On data governance, Than offered a framework that could roughly be compared to a traffic light.
Operational data (for example, customer records, contacts, raw transactional data) is generally seen as AI-friendly for organizations, with some error tolerance and auditability after the fact.
It’s a green light, if you will.
Financial data like payables or customer invoices are more proceed-with-caution for organizations. They’d largely want a human to review before anything moves.
You just said “yellow light!” you smart cookie, didn’t you?
Regulatory data like GDPR compliance, licensing, or anything where a single violation can trigger a contractual or legal consequence, is still largely “do not pass Go” territory.
Most organizations, Than said, are afraid to touch this layer with AI at all, but the frameworks that would make teams comfortable don’t fully exist yet.
You probably don’t need me to tell you, but, yes, the red light.
Than also raised the topic of data domains that haven’t been named yet, an issue the panel hadn’t covered.
Beyond public, private, and process data, he sees financial data, judgment data, and identification data as emerging concerns.
“I think that there’s still more domains that we haven’t even learned to describe yet,” he explained, “but it makes us distinctly human and able to execute many complex things without as many checks and balances.”
Intuitive data, he argued, is what CIOs should be thinking about protecting now, before AI finds ways to surface it.
“How many invoices did you cut last month?” asked Than. “Probably lost to the ether already, like it or not. But why is this customer valuable to you? What is your profit with this customer? Those are the intuitive pieces of data that we need to protect, and the CIO needs to think about protecting those now, long before AI comes along and sneakily grabs it from you.”
Final shots
The lack of trust does come with a dollar figure. Than’s clients stopped using AI because it got something wrong once, and that was enough.
The data governance conversation is coming whether organizations are ready or not. The best-prepared CIOs will have already mapped what they’re willing to let AI touch, and what they aren’t.
Written ByJennifer Kervin
Jennifer Kervin is a Digital Journal staff writer and editor based in Toronto.
