Aston Martin Aramco Formula One Team has assembled what it describes as one of the biggest and most diverse technology ecosystems on the grid, layering chips, cloud computing, sovereign artificial intelligence (AI) models, coding agents and cybersecurity into a single stack to increase its competitiveness on track.

The setup spans silicon design, data center compute, data infrastructure, foundation models, agentic coding, security, operational workflow and systems integration. Each partner occupies one layer.

“We live and breathe technology, and the partners that we have here today are going to give us a chance to win,” said Jefferson Slack, managing director of commercial at the team. “They can make us go faster, and that is an authentic integration that you do not normally get in sports.”

“This is not primarily a story about technology and the ecosystem of technology,” said Eric Ernst, technology ambassador of the team. “It is about unleashing intelligence and using every partner not to adapt to the future but to actually architect it.”

Intelligence can be outsourced with AI but experience cannot, Ernst said. The experience stays with the team and its people, while AI provides the cognitive scalability to handle work that would otherwise require two or three more staff members.

Having joined the team five months ago, he compared the ecosystem’s almost accidental assembly to the founding of Aston Martin itself. Lionel Martin and Robert Bamford created the brand in 1913 after Martin’s successful day at the Aston Hill Climb.

In his stack, Arm provides the silicon, CoreWeave the compute, NetApp the data infrastructure, Cohere the sovereign AI brain, Cognition the agentic coding, Zscaler the security shield, ServiceNow the unseen operational processes and Cognizant the understanding of how the layers operate together.

The team does not believe in outsourcing the way its models are weighted, he said.

“We want to control how a model is reasoning, what our values are weighted by when we ask a question to a large language model,” Ernst said.

Slack, who has worked in sports for about 30 years, said the team has never had such a critical mass of technology partners, with some on board since the rebrand in 2021 and a new group this year.

He said the partners are discovering they can do business with one another, and one partner’s valuation has jumped from $1 billion to $25 billion in 18 months. At June’s Cannes Lions festival, no other sport was represented the way Formula One was, he added.

“The sport really has become the epitome of what is happening with engineering, technology and culture,” he said.

From silicon to strategy

The comments came at the inaugural AMR Network Technology Forum on July 3 at the AMR Technology Campus in Silverstone, UK, ahead of the British Grand Prix. Aston Martin Aramco organized the forum to launch the AMR Network, a platform uniting its partners around the future of AI in Formula One and beyond.

Dozens of invited reporters took part in a pitstop challenge and watched sprint qualifying from the Mission Control viewing gallery.

In the first panel on building an AI ecosystem, Ben Richardson, head of CoreWeave International at CoreWeave, said that nine out of the 10 largest AI model builders run on the company’s cloud infrastructure.

“I often get asked where we are in a cycle. Is this a hype cycle?” he said. “I always use the US example that we are probably at the top of the second innings in a baseball analogy. This is just starting.”

He said companies must be brave, avoid waiting for perfect conditions and put guardrails in place where needed. His first question to job applicants is now about their personal and professional AI stack.

Richardson said CoreWeave’s business is all about speed and execution, a direct parallel with a team that pulls data from hundreds of sensors on a car and runs it through models.

David Ingham, digital partner in media and entertainment at Cognizant, said organizations are most often stuck with legacy systems when assembling an AI stack.

“To unpick data, to understand where the data is sitting in the organization, how do I get data from A to B, that is really where Cognizant can add value,” he said.

Ryan Lewis, head of UK and Northern Europe at Cohere, said the enterprise AI firm builds foundation models from scratch, one of about 10 companies in the world still doing so, and deploys agent platforms inside customer environments.

“Instead of having a single agent do these things sequentially, now you have each agent being an expert in this workflow, so the diagnostic agent is going to meet with the simulation agent,” he said. “They are going to synthesize those insights and create reports that give these teams faster insights and time to value.”

Simon Cox, chief transformation officer at ServiceNow, said many enterprises are paralyzed by the fear that their data is inaccurate, when the most successful ones fix it as they go.

“The real difference this time with AI is that the AI can find those gaps, the signal and the noise in that data, and help you fix as you go,” Cox said. “If you do not start, others are going to go flying past you.”

He said ServiceNow, an Aston Martin partner since 2023, calls itself the team behind the team, handling staff processes and moving into sea and freight logistics and travel.

Businesses learned from the cloud era, when they left the tap running and were hit with charges. Firms should build an AI control tower from the start or risk exhausting their budgets.

“Control and governance of it sounds dull, but it is really important,” he said.

Ernst told the panel that machine learning spots patterns while generative AI can now draft race strategies, leaving an engineer with several options and the experience to pick one. He said the data shows the situation, the AI shows the trajectory and the human decides whether to go faster or slower.

Zscaler is the newest layer of the shield. The cybersecurity firm joined in June as the team’s global cybersecurity partner in a multi-year deal, with branding debuting on the nose, seatbelts and wing mirrors of the AMR26 race car.

A single race weekend can generate more than a terabyte of telemetry from hundreds of sensors on each car.

Armies of agents

In the second panel, on building an AI-powered enterprise, Emily Cohen, senior vice president of operations and people at Cognition, said the firm’s AI software engineer Devin was adopted fastest by engineering managers because they were already used to delegating chunks of work.

“Everyone is becoming a manager of their army of agents,” she said. “I actually think a lot of people undervalue the change management that has to go into getting people there.”

Sunil Frida, chief marketing officer of Zscaler, said the firm secures more than 45% of the global Fortune 500 and handles about 750 billion transactions a day, roughly 10 million a second.

“What zero trust does is it makes your house disappear,” he said. “When your house is no longer reachable, your enterprise is not going to be breachable.”

Gabie Boko, chief marketing officer of NetApp, said the data infrastructure firm moves about 50 petabytes for the team on a race day, a volume that a healthcare client might spread across a month.

Rafael Oliveira, vice president of international marketing and partnerships at Eight Sleep, said the driver is the only element of a car not covered by sensors. The firm’s smart mattress makes hundreds of temperature adjustments a night, with clinical studies showing up to 25% more deep sleep and 45% less snoring.

He said a tired brain makes measurably worse decisions, turning sleep into a competitive advantage for the whole team.

Ernst added that humanity is undergoing a shift toward a superpower comparable to Gutenberg's printing press in the 15th century, with people becoming more valued for triage than for creating work.

The final panel turned to the issue of expanding access to careers in motorsport technology.

Janet Wessels, Chief People Officer, Aston Martin Aramco F1 team, said programs such as Make A Mark and the STEM Racing initiative with Aramco, targeting 100,000 students by 2028, have made attracting women the easy part.

“The challenge is retention,” she said. “As with most high-performing sports, we still have a lot to do around creating the right culture and the right environment for women to thrive.”

“I often compare diversity to aerodynamics. Do not tell my colleagues this, but it is something we cannot see, and yet it definitely makes the car go faster,” she added.

She urged companies to give women a fair chance to project manage AI initiatives rather than only analyze results.

Jonathan Armstrong, senior director of brand and creative at Arm, said recruitment gets people in the door but belonging keeps them, and inclusion must feel intentional. Arm is 18 months into a partnership with team ambassador driver Jessica Hawkins, who he said has taken three consecutive podium finishes this season.

Tara Mulcahy, vice president of sponsorships and experiences at NetApp, said that AI- and data-backed storytelling helps brands authentically reach female audiences.

Rebecca Adams, IT senior business analyst at the Aston Martin Aramco F1 team, said AI now handles much of her presentation work, freeing up time to dig into problems with colleagues.

Whether the ecosystem translates into lap time remains the harder question. Aston Martin Aramco races as a full works operation for the first time in 2026, with Honda power units and Adrian Newey as team principal, and its partners will be judged on how much faster they make the car.

Quantum Computing Inc (QCI), a New Jersey-based quantum photonics company, is racing to bring its entire supply chain onshore, concerned that access to foreign-sourced components could one day be restricted as geopolitical conditions deteriorate.

The strategy took concrete shape last week with the acquisition of NHanced Semiconductors, Inc., a US-based advanced packaging foundry specializing in hybrid bonding, chiplet architectures and photonics device integration. The deal formally launches Fab 2, QCI's second production facility in Tempe, Arizona, years ahead of schedule.

“As a US company, we have to obey any US rules. Right now, we are not subject to export control restrictions, but that could change,” Yuping Huang, chairman and chief executive of QCI, told TechJournal.uk in an interview. “In order to mitigate the risk imposed by geopolitics, we have been working on establishing our manufacturing capabilities in the US.”

The NHanced transaction, valued at $73.1 million in cash and stock with up to $72 million in additional performance-based earnouts, establishes Fab 2 alongside the existing Fab 1, which produces thin-film lithium niobate (TFLN) photonic chips, the core material in QCI’s quantum hardware.

The NHanced deal follows QCI’s earlier acquisition of Luminar Semiconductor Inc., which brought laser fabrication, photodetection and photonic packaging capabilities into the group. Together, the two acquisitions give QCI a vertically integrated domestic manufacturing stack from chip design through to full-system assembly.

“Today, we are delivering on our commitment to launch Fab 2 and expand our manufacturing capabilities and capacity years ahead of our original timeline,” Huang said in a statement accompanying the acquisition.

The rationale runs deeper than supply chain insurance.

Huang has long argued that quantum technology will only fulfill its promise if it can be manufactured at the scale of the semiconductor industry, and that photonic integrated circuits offer a proven path to get there, one that the industry has already spent 20 to 30 years refining.

“We are not talking about having 100 engineers spend two years to build a big machine. We are talking about making millions of products on a production line. This is the manufacturability that quantum has to take on,” he said.

Room-temperature operation is central to that case. Unlike most quantum computing approaches, which rely on superconducting qubits that must be cooled to near absolute zero, QCI’s photonic systems operate at ambient conditions. That makes them far more compatible with standard semiconductor fabrication processes and far more practical for deployment in commercial and defense environments.

On June 22, US President Donald Trump signed an Executive Order directing federal agencies to develop adequate domestic supply chains and manufacturing capabilities for quantum technologies, a policy direction that aligns directly with QCI’s strategy. The Hoboken, New Jersey-based company is listed on the Nasdaq and employs 205 people.

The domestic manufacturing push coincides with QCI’s first foray into European defense. On June 18, the company announced a framework agreement with Planck Dynamics, a Netherlands-based defense technology firm backed by NUNC Capital BV, to deploy its NeuraWave photonic reservoir computer for next-generation artificial intelligence (AI) edge applications.

The initial order covers five systems, with delivery expected during 2026, and the agreement establishes a pathway to deploy up to 100 units, representing a potential program value in excess of $10 million.

From the lab to the market

Huang was interviewed on the sidelines of Commercialising Quantum Global 2026, held on June 17 in London and organized by Economist Enterprise. He was also a fireside chat guest at the event, moderated by Jason Palmer, host of The Economist's The Intelligence podcast.

He spoke at the fireside chat about the long road from laboratory physics to commercial quantum hardware. The following draws on both conversations.

Huang graduated from the University of Science and Technology of China (USTC) in 2004 and received his PhD in quantum physics from Michigan State University. He went on to hold faculty and research leadership roles at Northwestern University and Stevens Institute of Technology. He is now a US citizen.

During that period, he led a portfolio of quantum research projects totaling approximately $40 million in funding from US government agencies, including DARPA, the National Science Foundation (NSF), the Department of Defense, and NASA, establishing himself as one of the leading researchers in quantum photonics.

He founded QPhoton, a quantum photonics innovation firm, in 2020, driven by a conviction that the inventors of a technology are always best placed to take it to market.

“We wrote quite a few patents because of the huge commercialization potential of the technologies that we developed in the lab,” he said. “We realized that the best way to commercialize a technology is by the inventors themselves. This is why we started.”

QPhoton merged with QCI in June 2022, in what Huang described as a half-and-half transaction undertaken primarily to access the resources available to a publicly listed company.

He was appointed interim chief executive in April 2025 and confirmed in the permanent role in January 2026. He holds the largest individual shareholding in QCI but does not hold a controlling stake.

Through his years of academic research, Huang said he had come to understand the precise technical barriers standing between photonic quantum physics and a commercially deployable product. At the fireside chat, he walked through those barriers one by one, explaining what had been solved and what remained.

Beyond the qubit

The technical problem Huang has spent his career solving is specific. Photonic quantum computing was widely discussed in the early 2000s, but the field stalled when researchers found that photons in a linear optical system simply do not interact with one another.

“If you shoot two laser beams against each other, they pass each other as if nothing happened,” he said. “Without interaction, there can be no logic gates, and without logic gates, there can be no computation.”

QCI spent over a decade working on the problem, ultimately identifying five technical conditions that must be met for photonic quantum computing to be viable. Four and a half of those conditions have now been satisfied. The remaining challenge is one of materials and precision manufacturing.

“The last condition outstanding is that we have to make very high-quality factor micro-resonators that can keep photons in the ring for a long time. As they circulate along the ring, they can interact with each other,” he said. “Our calculation, which has been verified by many experiments, is that we need to achieve about 10 million Q. Right now we are at 2 million Q.”

The Q-factor measures how long photons can be kept circulating inside a micro-resonator before they are lost. The higher the Q-factor, the longer photons remain in the ring and the more computation becomes possible.

Closing the gap from 2 million to 10 million Q is the central engineering challenge QCI is now racing to solve, and it is fundamentally a manufacturing problem, the same domain in which QCI has been investing most heavily.

Huang said the end goal is not a quantum computer for enterprises or governments alone.

“Quantum will change the world, but quantum will not change the world until and unless the majority of the population in the world can have easy access to it,” he said.

He drew a direct parallel with the rise of AI and social media. He said that just as those technologies only transformed society when placed in the hands of billions of ordinary people, quantum will only fulfill its potential when it escapes the laboratory and becomes accessible to anyone with curiosity.

“Our job is to manufacture millions of quantum devices per day, so that even middle schoolers can buy a quantum device and figure out what to do with it.”

On the question of competition between quantum modalities, Huang said the framing was wrong. Photonics, superconducting and trapped-ion approaches will each find the problems they are best suited to solve.

The industry should share components and capabilities rather than compete, he said.

QCI already provides its best lasers and photon detectors to collaborators working on other quantum platforms, on the basis that no single organization can build everything alone.

“Let’s cook a very big cake, and the cake can grow by itself as we really start to put quantum into the hands of people,” he said.

QCI has no plans for further fundraising. With two US fabrication facilities now operational, a European defense contract secured and Washington formally aligned behind domestic quantum manufacturing, Huang said the company is finally moving at the pace its technology has long demanded.

ORCA Computing, a London-based photonic quantum computing firm, is preparing to launch a system it says will outperform classical computers on real industrial workloads, marking what it describes as the first commercial delivery of quantum advantage in data-center conditions.

The PT-3, due for release later in 2026, is designed to surpass classical machines in two application areas: large-scale optimization problems and generative artificial intelligence (AI) acceleration. The company has already signed an agreement to install the system at a major data center operator’s innovation lab in London.

“The PT-3 will be a quantum advantage machine,” Richard Murray, Co-Founder and Chief Executive Officer of ORCA Computing, told TechJournal.uk in an interview. “It will be the first time that anyone delivers quantum advantage as a product in a way that can be installed in data centers.”

Regarding the practical significance of that threshold, Murray said the PT-3 will offer more optimal solutions with lower runtime than leading classical optimization solvers. The system will also deliver a generative AI advantage that is not achievable with classical hardware alone.

“Optimization is such a huge market, such a massive application space,” he said.

“We focus very much on the first near-term applications of quantum computers,” he said. “There are two categories where we think there is a commercial benefit: advancing generative AI, taking existing generative AI models and adding quantum as an accelerating layer, and optimization, running large up to 25,000-variable optimization problems on our current generation of quantum computers.”

Murray said ORCA believes it is at the cusp of commercial quantum advantage, the point at which a quantum system delivers a measurable commercial benefit over its classical equivalent. He said the PT-3 launch will be a landmark moment for the entire quantum industry.

ORCA Computing was founded in 2019 as a spin-out of the University of Oxford, drawing on more than 30 years of research by co-founder Professor Ian Walmsley. It is headquartered in London and maintains offices in Toronto and Austin.

By early 2025, the company had delivered 10 on-premises photonic quantum systems to customers, including the UK Ministry of Defence, the National Quantum Computing Centre, Poznan Supercomputing and Networking Center in Poland and Montana State University in the US.

First enterprise deployment

Murray was speaking on the sidelines of Commercialising Quantum Global 2026, organized by Economist Enterprise and held in London on June 16.

His comments on PT-3 came after ORCA’s first installation of a quantum system at a private-sector enterprise customer. Last month, the company deployed its current PT-2 system at a major Japanese industrial company, brokered by Toyota Tsusho Corporation, a Japanese trading company. The end customer has not been disclosed.

“Installing ORCA’s PT-2 quantum system within an enterprise environment in under one week highlights the maturity of ORCA’s photonic quantum technology,” Murray said. “Together with Toyota Tsusho, we are laying the foundation for commercial quantum advantage in industrial AI applications.”

Norihito Ohigashi, manager of the Digital Infrastructure Department at Toyota Tsusho Corporation, said in a press release that the collaboration reflects a commitment to enabling technologies that will shape the future of manufacturing and intelligent infrastructure.

The PT-2 system has been integrated into cloud services supporting the enterprise customer’s global operations, enabling hybrid quantum-classical workflows alongside existing high-performance computing (HPC) applications. The system is scheduled to be upgraded to the PT-3 later this year.

In May, ORCA signed an agreement with Digital Realty, the world’s largest cloud- and carrier-neutral data center provider, to install the PT-3 at its newly launched Digital Realty Innovation Lab (DRIL) in London. At least one additional customer site is also planned. The DRIL allows organizations to test emerging AI and quantum technologies in live conditions before full-scale deployment.

Seamus Dunne, Managing Director of Digital Realty UK and Ireland, said the collaboration gives customers direct access to one of the UK’s leading quantum innovators.

“Having ORCA integrated within the DRIL helps customers explore practical pathways to hybrid quantum-classical computing,” he said.

Central to ORCA’s commercial strategy is a data-center-native design philosophy. Its systems occupy two standard 19-inch server racks, each six feet high, requiring no specialized cooling or infrastructure, a form factor deliberately engineered to lower the barrier to enterprise adoption.

“ORCA was built around the idea that quantum computing should integrate directly into the infrastructure enterprises already rely on for AI and high-performance computing,” he said. “As a London-based quantum company, it is exciting to be part of the launch of the Digital Realty Innovation Lab and to demonstrate our systems operating inside a commercial data center environment.”

Inside the photonic machine

At the heart of ORCA’s systems is a photonic architecture that diverges sharply from the superconducting approach favored by many competitors. Rather than using qubits cooled to near absolute zero, the machine generates single photons, passes them through a switchable optical network, and measures the resulting quantum states, all without cryogenic cooling.

“Single photons are created; they pass through a big switchable network, which is the device that we use to program our systems, and then the photons are measured,” Murray said. “When they are measured, the quantum state is destroyed, and that whole process sits next to an existing classical computer and is closely integrated inside a classical computing algorithm or operation.”

The system contains three core components: a photon source, a network of three switchable modules and detectors. Computation occurs as photons pass through the system rather than in any fixed chip. A proprietary semiconductor platform developed at facilities in Texas and New York underpins the system’s performance through ultra-low optical loss.

“Our systems operate with very low levels of optical loss,” he said. “That is a defining feature of what makes them quantum and what makes them powerful.”

The photonic approach has attracted significant investment globally. In the US, PsiQuantum has secured contracts with Australia and the State of Illinois to build large-scale systems, while Xanadu recently went public.

Murray positioned ORCA as moving faster than peers toward real-world application, rather than competing purely on scale.

“Progress is happening,” he said. “What to track on the technical side is the progress of very low-loss systems, which yield more and more photons and computational ability.”

On funding, Murray said ORCA does not publicize most of its fundraising, preferring to let technical milestones drive its public profile. He described the company’s approach as capital-efficient at a time when some peers are pursuing large headline rounds.

He drew a clear distinction between the UK and US investment environments. The US has increasingly favored domestic companies, while the UK’s £2 billion quantum commitment takes a more open, internationally competitive approach.

“The UK’s target is to attract globally leading quantum companies to build their systems in the UK, as well as to support UK companies,” he said. “I like the UK because it is more of an open marketplace.”

With the PT-3 launch scheduled for later in 2026 and its first private-sector enterprise installation now operational in Japan, ORCA enters the second half of the year with more live commercial deployments in its pipeline than at any point in its seven-year history.

Make, a Prague-based artificial intelligence (AI) automation platform, is gaining traction in the enterprise market with a cost advantage it says most competitors cannot match: running rule-based automation workloads on its platform can be done at significantly lower cost than routing the same tasks through Claude-based AI agents.

The company said the bill for Claude-based AI escalates quickly at enterprise scale and that most organizations cannot afford to run all their automation through it. Make positions itself as one of the few platforms that support both approaches within a single environment, allowing customers to route each workload to whichever method is cheaper and more appropriate.

“You really cannot run everything on Claude because of how expensive it is,” Sara Maldon, Head of Business Automation & AI at Make, told TechJournal.uk in an interview on the sidelines of a recent AI event. “The second consideration is governance and observability, which come back to the management and visualization of operations. For enterprise AI, that is very important.”

Deterministic, or rule-based, automation follows fixed steps and always produces the same output from the same input, making it fast, predictable and cheap to run. AI agents, or the non-deterministic approach, use judgment to handle complex or unpredictable situations where rigid rules would fail, but at a significantly higher per-task cost.

Maldon said running rule-based workloads on Make can cost significantly less than routing the same tasks through Claude-based AI agents, a gap she described as decisive for enterprises managing large volumes of routine tasks.

“We are one of the platforms that offers both AI agents and rule-based automation. The cost of running things on agents through Claude can be up to eight times that of running them in rule-based automation on Make,” she said.

The third pillar of Make’s enterprise pitch is architectural openness. Rather than providing pre-built workflows, Make gives customers the tools to design their own operations from scratch, a deliberate contrast to platforms that impose fixed processes.

“On Make, you can build anything; you can connect anything. The bet is that we do not provide things out of the box for you right now, but you can build basically anything. It is simple to build, and simpler to build than before. We are still in the game of: it is your operations, however you want it,” she said.

Make was founded in Prague in 2015 as Integromat, acquired by Celonis in October 2020, and relaunched under its current name in February 2022.

Celonis was founded in 2011 at the Technical University of Munich, developing process mining tools to help enterprises detect inefficiencies and optimize their operations. It struck a landmark reseller agreement with SAP in 2015 and has since expanded globally, with headquarters in Munich and New York and a valuation of $13 billion.

Make operates today as an independent business unit within Celonis, with more than 300 employees and its product and engineering teams based primarily in Prague.

Doubling revenue, staying small

The AI Summit 2026, organized by Informa and held in London, brought together executives from across the technology industry to discuss the state of enterprise AI adoption. Speaking on the sidelines, Maldon outlined a broader company strategy that she said is as much about preserving culture as it is about commercial growth.

She said Make has deliberately held its headcount flat at around 300 people while pursuing aggressive revenue growth. The vehicle for this is an internal AI transformation program that Maldon leads, which is designed to demonstrate that automation and AI agents can absorb the workload that would otherwise require new hires. The program has become a live test of the platform’s own capabilities.

“One of the justifications and the missions behind launching the AI transformation program was to protect our size as a company, while continuing to grow in terms of business metrics. The mission was actually to double the revenue with the same headcount,” she said.

“The philosophy of that is not just necessarily for efficiency’s sake. As someone who has been at Make for two and a half years, I think it is an incredible company from a culture standpoint. Getting around is very easy because we are still not that many people,” she added.

Maldon described a working environment in which a deliberately small headcount creates unusually direct lines of collaboration. She said colleagues regularly approach her to demonstrate the AI agents or automation prompts they have built, a dynamic she credited to the company’s decision to stay lean.

“I go around the office and I know every single person,” she said. “The company internally is very interconnected, and it is very easy to get information, to meet people, and to collaborate, and we want to protect that at all costs.”

Make’s visual-first design underpins both its cultural argument and its product roadmap.

Maldon said the rise of vibe coding, the practice of building software by describing a vision to an AI assistant in natural language rather than writing code line by line, makes the ability to see and manage complex automation environments more valuable, not less.

“Visual communication has always been the number one way to drive communication and collaboration, and that is why being visual as a differentiator for us holds a lot of value, especially in the era of vibe coding,” she said.

“We want to be a place where businesses build their operations functions, not just a simple personal productivity workflow moving data from one place to another, but actually building processes like customer support, ticket routing, the logic after a sales call, engineering operations,” she said. “Once you build those different operational functions, you can then visualize them and see them in a holistic perspective.”

Operations, not industries

Maldon said Make does not target customers by industry. The company’s customer strategy is defined entirely by function: it serves the operations teams that build and run the internal processes of any organization, regardless of sector.

“We are not industry-specific at all, and we do not plan to be. We can serve any industry. What we are intentional about is who we serve in terms of functions. We know that we are very good at building out operations profiles in different departments,” she said.

These include revenue operations, go-to-market engineering, marketing, customer support and engineering operations.

Outside Make, Maldon has founded a side venture called Persist, focused on one of the more persistent challenges in enterprise technology: measuring whether AI investments are actually delivering results. The platform generates a single score for a company’s AI performance, benchmarked against peers and broken down by team.

“Measuring AI impact is a hard question that no one, in my opinion, has solved very well yet, and I think everyone will need to solve it at some point or another. It is my way of contributing to that topic because I deeply care about it,” she said.

She said the problem became clear over two and a half years of leading Make’s internal AI transformation, during which the same difficulty in linking AI tool adoption to measurable business outcomes recurred across organizations.

Maldon is also active in several communities focused on advancing women in technology. She participates in Women Applying AI, a global organization based primarily in Boston, and Wednesday Women, an executive network. She previously worked with European Women on Boards and She Loves Data, and speaks at conferences and industry events on the topic.

Infleqtion, a Colorado-based quantum technology company, is ramping up manufacturing and hiring engineers at pace following its New York Stock Exchange debut earlier this year.

The listing, completed in February 2026, made Infleqtion the first neutral-atom quantum company to go public and raised more than $550 million. The capital is being deployed not to fund more research, but to build, manufacture and ship.

“You start out with a team of a couple of physicists and engineers, spread across lots of things, a bit lumpy. But as you get through IPO, you start growing the team and filling those lumps with specialists,” said Ryan Hanley, UK chief technology officer of Infleqtion, who told TechJournal.uk on the sidelines of Commercialising Quantum Global 2026 in London.

The analogy comes from asteroid physics. In planetary science, the “potato radius” is the point at which a body becomes large enough for gravity to smooth its irregular surface into a sphere.

Hanley said Infleqtion has crossed that threshold, evolving from a generalist startup into a specialist organization with dedicated field-programmable gate array (FPGA) engineers, power electronics experts and photonics specialists.

Only around one-third of Infleqtion’s UK organization are physicists; the rest are engineers.

Hanley said a physicist is typically very good at making something work once, while an engineering discipline is what makes it work every time.

“For us, it’s really accelerating that productization, commercialization effort, and on the computing front, accelerating towards logical qubit utility scale. But also productionizing those systems as well,” he said.

The UK headcount stood at around 70 people and is expected to reach between 90 and 100 by the end of 2026, as Infleqtion moves to a larger Oxford facility nearby.

On May 28, Infleqtion announced the launch of a new Quantum Innovation Centre at Oxford Technology Park, tripling the size of its research, production and systems integration capabilities. The facility is scheduled to open later in 2026.

Hanley said around half of Infleqtion’s new Oxford facility is dedicated to manufacturing, validation and verification. Customers will ultimately want quantum computing systems on-premises, not just cloud access.

“A significant fraction of that facility is in production and manufacturing, because ultimately that’s how the UK succeeds as well. It’s selling products,” he said.

Hanley flagged skills in short supply across the sector: analog and FPGA engineers, control system specialists and photonics engineers capable of transitioning from fiber-based optical systems to photonic integrated circuits.

Infleqtion is headquartered in the United States with operations in Europe and Asia. Its product portfolio spans quantum computers, quantum optical clocks, radio-frequency (RF) receivers and inertial sensors, all integrated with its proprietary Superstaq software platform.

Its systems are in use by the US Department of War, NASA and the UK government, and form elements of multiple collaborations with Nvidia.

Washington signals quantum urgency

On June 22, US President Donald Trump signed an Executive Order on quantum technology, directing federal agencies to accelerate the development and deployment of quantum technologies across national security, commercial and scientific applications. Matthew Kinsella, chief executive of Infleqtion, attended the signing ceremony at the White House.

The order followed a significant funding announcement in May. The Department of Commerce confirmed that Infleqtion would receive $100 million in planned incentives under the CHIPS and Science Act, one of nine companies sharing $2.013 billion, to develop engineering systems for large-scale neutral-atom quantum computers.

The planned funding will support high-powered optical systems and novel readout and error correction systems. The Department of Commerce will take a minority, non-controlling equity stake in each recipient.

On the day of the signing, Infleqtion launched its America’s Quantum Space Initiative, focused on accelerating the development of quantum technologies for future space systems. The company had already secured a NASA collaboration, backed by more than $20 million in contracted mission funding, to fly the world’s first quantum gravity sensor to space.

Kinsella had set out Infleqtion’s commercial vision six days earlier, on June 16, during a fireside chat at Commercialising Quantum Global 2026, organized by Economist Enterprise in London and moderated by Tom Standage, deputy editor of The Economist. The session focused on quantum technologies that are already generating commercial traction beyond computing.

“The critical infrastructure of the world, both for commercial purposes as well as military purposes, relies on precision timing, and that mostly comes from the GPS networks. The GPS network, unfortunately, is becoming increasingly prone to being denied or spoofed. What we can provide is better than GPS timing locally, in an unspoofable way, to continue to synchronize and have that critical infrastructure be able to do its job,” Kinsella said.

He said Infleqtion’s quantum clocks can be deployed across energy grids, radio-frequency (RF) networks and financial markets, providing timing more precise than GPS and continuing to function during outages or jamming events.

He said these near-term sensing and timing markets represent the first profitable wave of quantum deployment.

“On the maiden voyage of the Excalibur autonomous submarine, Tiqker was on board and performed wonderfully, all the way down to around 90 meters and back up. There’s no GPS underwater, so having precise local timing is absolutely critical. It’s one of the fundamental building blocks of navigating underwater in an entirely quantum way,” he said.

The Royal Navy launched Excalibur, its first autonomous submarine, last October. Tiqker locally disciplines the vessel’s classical inertial navigation sensors, eliminating the need to surface and synchronize with GPS.

The long-term goal is a fully quantum underwater navigation system requiring no GPS contact at all.

“We’re doing work with NASA to send gravity sensors up into space, residing inside a satellite, to sense changes in gravity on the earth’s surface, or importantly, below it. You can infer some very interesting things about what’s happening both on and below the earth’s surface based upon changes in gravity: the melting of polar ice caps, the depletion of aquifers, the building of underground facilities,” he said.

Kinsella said the sensing markets are large in their own right but serve a deeper strategic purpose: every advance in clock and sensor engineering compounds directly into progress on quantum computing.

Roadmap to quantum advantage

Infleqtion’s product portfolio reflects that dual focus. On the sensing side, it ships the Tiqker optical atomic clock, Quantum Spectrum RF receivers and inertial sensors.

On the computing side, it offers Sqale, its full-stack neutral-atom quantum computer, the Superstaq software platform and ready-to-use Quantum Cores for research applications.

The computing roadmap has already delivered a landmark in the UK. At the end of 2025, Infleqtion supplied the country’s first 100-physical-qubit quantum computer to the National Quantum Computing Centre (NQC).

“We were really excited to be the company that was able to help the UK achieve its goal of having a 100-qubit computer in the UK,” Kinsella said.

Infleqtion has showcased 1,600 physical qubits in its own facilities. Academic partners at Caltech have demonstrated 6,100 physical qubits using Infleqtion’s foundational technology. Physical qubit counts, however, are not the measure of progress that matters most.

“We’ve publicly guided that we’ll be at 30 logical qubits by the end of this year, and 100 logical qubits by the end of 2028. We think at 100 logical qubits, that’s when you start to see real quantum advantage from a quantum computer,” he said.

Logical qubits incorporate error correction, making them far more reliable than raw physical qubits and the true measure of computing utility.

He said quantum computers will need to be designed for mass production, not assembled on lab benches, and that systems engineering discipline is the competitive differentiator the industry has yet to fully grasp.

“A lot of the work we’re doing on the sensing market is to compound in getting to useful quantum computing faster. The underlying technology is very similar: building a clock is much like building a quantum computer. These sensors are the building blocks of a quantum computer,” he said.

He left a career in finance and investment roughly two and a half years ago, drawn by what he described as distinct and accelerating changes in the quantum market. He said the question for large organizations is no longer whether quantum will matter, but when.

“I do really believe this has gone from a matter of if to a matter of when. Big shifts and acceleration in new technologies have given rise to new leaders across industries. Now is the time to capture that shift and take advantage of it for your businesses,” he said.

His advice to enterprise strategists is to start learning about quantum now, partner with companies focused on the technology, and consider deploying quantum sensors as a practical entry point before quantum computers reach utility scale. For Infleqtion, that moment is on a published roadmap.

Philanthropy, not government funding or venture capital, bankrolled one of the most rigorous attempts to date to determine whether quantum computing can deliver practical value in human health. After 30 months and $50 million, the results are in.

Wellcome Leap, a US nonprofit founded by the Wellcome Trust, designed its Quantum for Bio (Q4Bio) program around a question the rest of the funding landscape was unwilling to ask: could quantum algorithms deliver a provable advantage for biological problems that classical computers cannot solve?

The program has produced a prize winner, a world first in genomics and a set of computational frameworks that did not exist when it began.

“Q4Bio was designed to create new solutions that would answer that question within real biological and hardware constraints. What we now have is a much clearer understanding of where quantum can create value, where it cannot, and what needs to happen next,” said Shihan Sajeed, program director for Q4Bio at Wellcome Leap.

“When we started the Wellcome Leap program, it wasn’t clear exactly how or where quantum computing could meaningfully impact biology,” he said.

The program rested on a structural insight. The intersection of quantum computing and biology presented a class of problems that governments considered too uncertain to fund, and that venture capital found too distant in its payoff horizon to back. Problems of that kind are routinely left unaddressed. Sajeed said that the gap was precisely where Wellcome Leap was built to intervene.

To anchor the stakes, Sajeed opened his presentation with six clinical scenarios, among them a child with a rare form of myotonic dystrophy, a young woman with skin cancer, a single mother whose cancer was growing at an alarming rate and an elderly woman uncertain whether her body could tolerate her prescribed therapy.

Each scenario pointed to a quantum-enhanced solution that does not yet exist.

He asked whether the field should wait for the incremental pace of science or take deliberate steps to accelerate. Q4Bio was Wellcome Leap’s answer.

Wellcome Leap is a US nonprofit founded by the Wellcome Trust in 2020 with a mandate to run unconventional, large-scale programs and deliver breakthroughs in human health in years rather than decades. Q4Bio was its only quantum-focused program.

Genome on quantum hardware

Sajeed delivered the talk, titled “Foundations as Kingmakers: How Philanthropy is Shaping the Quantum Era,” at Commercialising Quantum Global 2026, an Economist Enterprise event held in London on June 16.

The session examined how philanthropic funders are shaping early-stage quantum research and what Q4Bio’s results reveal about where quantum computing can and cannot yet create value in healthcare.

Q4Bio launched in September 2023 with $40 million in research funding distributed across three phases and up to $10 million in prize money.

Two prize tiers were defined:

• a $5 million grand prize for teams demonstrating full quantum advantage over best-in-class classical baselines;

• a $2 million prize for each team that demonstrated an experimental realization on a quantum computer with more than 50 qubits and a clear trajectory toward advantage.

From 120 applicants, 12 teams were selected. Each received $1.5 million to identify biological problems amenable to quantum computing and develop candidate algorithms. Eight teams advanced to test those algorithms through high-performance computing (HPC) simulation, with funding of up to $750,000 each.

Six finalists then entered a 12-month hardware implementation phase, each receiving $2 million. The program formally concluded in March 2026.

The six finalist teams were led by Infleqtion, the University of Nottingham, Harvard University, Stanford University, Algorithmiq and the University of Oxford.

The team formed by Oxford, the University of Cambridge and the Wellcome Sanger Institute focused on genomics, building an end-to-end framework for mapping genome-scale problems onto quantum hardware.

Their headline result was loading the complete genome sequence of hepatitis D into a 117-qubit IBM quantum computer, the first time a complete organism’s genome had been encoded on quantum hardware. Current systems can only accommodate small molecules; as hardware scales, larger and more complex genomes become encodable.

“Wellcome Sanger Institute (previously known as The Sanger Center and Wellcome Trust Sanger Institute) was the institute where the first classical genome sequencing was done by Fred Sanger, which earned him the Nobel Prize in chemistry in the 80s,” Sajeed said. “Fifty years later, the same institute undertook the same problem, only this time in quantum.”

The tools and heuristics the team developed allow researchers to examine the HLA (human leukocyte antigen) region of human DNA, which governs immune responses and cancer cell evolution and has, until now, been among the most computationally intractable sections of the genome. Sajeed said the work opens pathways for personalized medicine, disease susceptibility analysis and cancer evolution research.

Quantum inside the pipeline

The sole prize winner of Q4Bio was announced in April 2026. The team led by quantum software company Algorithmiq, with quantum computing support from IBM and biological expertise from the Cleveland Clinic, was awarded $2 million after successfully meeting the scalability criteria. No team claimed the $5 million grand prize.

The team developed an end-to-end quantum-classical workflow to calculate the excited-state properties of TLD-1433, a photosensitizer molecule in phase two clinical trials for photodynamic cancer therapy. Using quantum-generated data, they boosted the performance of a classical DMRG (density matrix renormalization group) pipeline, marking the first time a quantum computer was integrated into a classical pipeline to improve overall output.

“We know in the near term it is more likely than not that quantum is not going to replace classical. It is actually going to coexist with classical, and we needed to know how it can happen,” Sajeed said.

The prize was awarded on the basis that the solution was scalable: its performance is expected to improve as quantum hardware advances. Sajeed described it as one of the first concrete demonstrations of a quantum computer lifting the performance of an entire classical pipeline.

Q4Bio also generated outcomes its designers had not anticipated. The constraints of running algorithms on noisy, low-resource quantum hardware forced teams to rethink and improve their classical pipelines as a side effect.

“The challenge of running a problem with those low-resource quantum hardware forced the teams to map the problem in a way so you can run it in those noisy quantum computers, and it needed them to improve the classical part of the computation, make it more efficient,” Sajeed said.

Teams discovered they had to improve every component of their computational frameworks simultaneously, because the underlying biological problem could only translate into value when all parts worked coherently. The resulting frameworks did not exist before the program launched. Sajeed said he expected them to grow stronger as hardware improves.

The experience reinforced Wellcome Leap’s core thesis: that bold, time-limited programs with competitive structures can pull forward research that would otherwise sit undone at the margins of mainstream funding.

Wellcome Leap is now designing a follow-on program and is actively seeking co-sponsors and partners.

Sajeed said the organization was still evaluating which problems it is best positioned to tackle next and had not yet settled on a name for the successor initiative. He said the pipelines and workflows built through Q4Bio are designed to adapt as more advanced quantum systems emerge.

Quantum computers are delivering measurable results in drug discovery, cancer treatment and fusion energy, with hybrid classical-quantum workflows driving breakthroughs across all three fields.

The approach is consistent: break a complex problem into components, assign classical computing to the simpler parts and quantum processors to the hardest parts, then combine the outputs. Researchers are reporting significant gains in both scope and accuracy.

“Researchers went from simulating smaller atoms, methane dimer, simple type activity into really biologically relevant enzymes and proteins,” said Katie Pizzolato, vice-president of IBM Quantum Platform.

“They put the parts that classical could do well. They took complex parts of the molecule that quantum could better simulate, and they assembled it all back together at the end.”

Pizzolato was describing a two-year collaboration between IBM and the Cleveland Clinic that ran from 2024 to 2026. The project achieved a 40-times gain in simulation scope, progressing from simple atomic models to T4 lysozyme and trypsin, enzymes central to understanding protein function and human digestion.

The team worked with high-performance computing centers, including RIKEN in Kobe, Japan, to accelerate the classical compute component, and open-sourced its methods so that others across the ecosystem could build on them.

“They didn’t do it all at once,” she said. “They did it in a methodical way, where they used the system, they found better ways to do it, which is the history of how we’ve consumed technology.”

The team began with molecules in the gaseous state before advancing to larger molecules in the liquid state, a progression she described as a model for how the industry should approach quantum application development.

“We are past the phase of abstract exploration and experimentation,” she said. “We really are in a phase for practical explorations of places that will deliver value.”

Pizzolato framed this as an industry-wide shift. Businesses, institutions and national labs are now producing work that is both commercially and scientifically relevant. Classical computers, she added, are actively extending the reach of quantum systems today, with the two technologies working in concert rather than in competition.

From cancer to fusion

Pizzolato was speaking at Commercialising Quantum Global 2026, organized by Economist Enterprise on June 16 in London. Her keynote, titled “Small steps and quantum leaps: delivering useful quantum computing now,” surveyed the progress IBM and its partners have made in translating quantum computing from experimental platforms into commercially relevant applications.

She has been part of the IBM Quantum Group since 2016. IBM Quantum Platform supports a network of more than 325 member organizations and has deployed over 90 quantum computers on the cloud and in data centers since the program’s inception.

A second use case focused on photodynamic cancer therapy, a treatment approach in which light activates compounds that destroy cancer cells. A research team applied the same hybrid classical-quantum workflow to drive algorithmic discovery in identifying treatment pathways, combining central processing units (CPUs), graphics processing units (GPUs) and quantum processing units (QPUs).

“Not only did that algorithm do a great job and win this year’s Wellcome Leap Quantum for Bio (Q4Bio) Prize, but a big part of winning it was showing that there was progress or vision into chemically relevant and biologically relevant things to do,” Pizzolato said. “This is a great example of ecosystem quantum-centric supercomputing and algorithm discovery all working together to push the bounds of the technology.”

The Q4Bio award recognizes quantum computing research with a demonstrated pathway toward biological and clinical relevance. The CPU, GPU and QPU combination unlocks applications that none of the three compute types could achieve independently.

The third use case extended the Cleveland Clinic's embedding technique into fusion energy research. Oak Ridge National Laboratory and collaborators, including Commonwealth Fusion Systems, used the method to simulate tritium production in molten salt. Tritium is a key fuel for fusion reactors; its scarcity on Earth means fusion programs must breed it from lithium inside the reactor.

Pizzolato said that tritium is scarce on Earth and that fusion programs must produce large quantities of it as part of the fuel cycle.

Oak Ridge recognized that the Cleveland Clinic’s open-sourced embedding technique could be applied to an entirely different scientific problem. The collaboration brought together enterprise partners, national laboratories and academic institutions, the same triangle she described as essential to quantum ecosystem success.

IBM has been conducting joint research with the MIT fusion group for several years.

Roadmap and power demands

Pizzolato framed the three use cases as evidence of a broader industry inflection point. BCG projects the quantum computing market will reach into the trillion-dollar range at maturity, driven by applications in pharmaceuticals, materials and finance.

She traced IBM’s journey from placing its first quantum computer in the cloud a decade ago, through a quantum utility milestone in which quantum processors outperformed brute-force classical computers on certain circuits, to where the industry stands today.

“Useful quantum computing is here today,” she said. The industry had arrived at this point, she added, because of mounting real-world evidence of what quantum systems could deliver. “We’re here because it’s real. We’re here because we’re seeing so much evidence of value that it’s bringing.”

The IBM Quantum Network’s 325-plus members span enterprise, academia, and national labs, with participants including NatWest, HSBC, the Wellcome Sanger Institute, and the Quantum Computing Center (QCC). She said enterprise partners drive the signal of what is needed, academic institutions advance the underlying research and national labs act as a connective center across the three.

IBM has put over 90 computers in the cloud and data centers since the program’s inception, with each hardware iteration generating rapid learning cycles that feed back into the next generation. The software stack, powered by Qiskit, advances in parallel with the hardware.

“We have to have scale, quality and speed across your device,” she said. “That is something that IBM has been focusing on since the very beginning.”

IBM’s public roadmap, first introduced in 2019, targets two major fault-tolerant systems: Starling in 2029 and Blue Jay in 2033. Both will intersect with known algorithms at established resource estimates. The current error-handling approach uses high sample counts with few qubits; future systems will require fewer samples and many more qubits.

“You won’t know the difference,” she said of the transition. “You will just continue to be able to execute larger and larger circuits, and you will continue to get more performance out of the system. It will all be under the hood.”

She closed with a data point she said deserved more attention than her allotted time allowed. Current IBM quantum systems, including the Nighthawk and Heron devices in System Two configurations, draw between 30 and 50 kilowatts. Starling is projected to require approximately one megawatt; Blue Jay under six megawatts.

“What are these systems going to demand, and how are they going to work in concert with our large data centers that we already have today?” she said.

Today’s data centers already consume hundreds of megawatts and are moving toward the gigawatt range. Pizzolato said the question of how quantum systems will integrate with existing large-scale data center infrastructure will grow in importance as the roadmap advances toward Starling and Blue Jay.

Business travelers are willing to let artificial intelligence (AI) simplify their journeys, but draw a clear line at allowing it to make decisions on their behalf. The question of how much to trust AI with consequential decisions has emerged as the defining challenge for the corporate travel industry.

Survey data underscores the scale of the problem. Despite 90% of IT leaders reporting satisfaction with their travel technology stacks, the experience for travelers themselves breaks down the moment complexity arises. Disruptions, policy interpretation and approval processes all force manual intervention at precisely the moments when travelers most need support.

“This feels like an important inflection point right now. So many of us are getting used to using AI to answer quick questions or analyze data, but moving to action is a completely different element of trust, and that trust will come from having the right context around it. What policies do you have in place? What guardrails and dependencies?” said Erica Antony, senior vice president, Product and Engineering at American Express Global Business Travel (Amex GBT).

Antony said travelers today are manually stitching together expenses, policy requirements, supplier receipts and approval processes.

“It is really not about a sprinkling of AI, here is a feature or let us dabble in the corners. Instead, we are looking at it as a systematic approach, a system with four key pillars,” she said.

Antony outlined Amex GBT’s four-pillar framework for deploying AI across its platform:

• Agent-to-agent architecture to connect workflows end to end

• End-to-end integration to move beyond point solutions

• A high-tech and high-touch model that deliberately embeds human support at complex moments

• Trusted intelligence grounded in data and governance

Tarun Avasthy, principal consultant at Forrester, said the central question facing the industry is not one of capability.

“It is not about what AI can do. It is about what people trust AI to do,” he said.

Forrester surveyed more than 2,000 business travelers across eight markets and 500 IT leaders for its study.

Avasthy said both groups identified three prerequisites for trusting AI: transparency in how decisions are made, clarity on why certain recommendations were surfaced, and the ability to control and override outputs.

“It is not whether it is AI versus a human. It is the coordination between the two that would work and be more harmonious,” he said.

He said AI performs well at predictable, repeatable tasks, while human oversight remains essential for decisions that require empathy and judgment.

Amex GBT moved to address those expectations on April 14, announcing a major evolution of Egencia, including agentic AI capabilities and a native integration with Concur Expense. The company said the upgraded platform delivers an average booking time of under three minutes and automated expense reconciliation, targeting the fragmented workflows that have long burdened corporate travelers.

The new Egencia AI assistant allows travelers to search, book and manage trips through natural-language conversation within tools they already use, including Microsoft Teams. Amex GBT described the Concur integration as the first of its kind for a non-SAP solution, with travel bookings and receipts flowing into expense reports in near real-time.

A joint study by Amex GBT and Ipsos found that 78% of business travelers expect AI to improve the shopping experience within five years, and 76% expect it to improve access to on-trip support.

Agentic travel at scale

The discussion took place at a panel session in London during an event organised by Amex GBT on June 23.

The session was moderated by technology journalist Jane Wakefield, who spent more than two decades at the BBC covering technology and has written about AI since 2015. Wakefield now hosts the podcast Human in the Loop, which examines how humans and AI can work together effectively.

“We launched with Egencia a conversational experience, an agentic trip management capability,” Antony said. “Through natural language, you can go ahead and search and book, have policy-compliant recommendations, and at the same time, it also knows when it has gone far enough and needs to connect to a travel counselor.”

She said the handoff to a human counselor is not a fallback but an intentional design feature that has proven effective in building traveler confidence. Antony said the integration between Egencia and Concur, the expense management platform, goes further than automation.

“With Egencia and Concur, we now have data syncing with booking and expense data to not just make that easier, but it is actually eliminating steps,” she said.

She added that Amex GBT's AI recommendations are grounded in more than a petabyte of travel data, over one million enterprise policies and transactions from hundreds of suppliers.

Avasthy said the concept of graduated autonomy offers a more sustainable path than full AI implementation. He outlined three stages:

• AI reduces complexity by offering recommendations that the traveler selects from, building trust gradually

• The traveler retains control by choosing between AI-generated options

• In well-defined scenarios only, AI operates fully autonomously

“A lot of companies fail when they fully implement AI and cut jobs. And you have seen those companies get back the workers they let go over the last couple of years,” he said. “Graduated autonomy would help, having AI that assists first, reduces complexity, provides options and gives recommendations that you still control and oversee.”

He added that trust built with IT leaders naturally extends to business travelers.

Fragmented stacks fractured journeys

The structural obstacles to AI-powered travel are significant.

Avasthy said only 3% of companies have travel technology stacks that are fully integrated and cross-functional across enterprise workflows. The remaining 97% operate with partial or low integration, meaning that complexity is amplified rather than absorbed by the system, shifting the burden onto the traveler.

Antony said the solution lies in recognizing where AI adds most value. She said routine tasks are where full automation is simply expected, while complex, high-stakes moments demand a different response.

“The human piece is not a fallback, not something we have to do because we cannot figure it out. It is exactly the opposite, a very intentional embedded design of where humans need to be part of that, because we know where it is more sensitive, critical and urgent,” she said.

Wakefield said the corporate world has been slow to reckon with both the limits of AI and the risks of deploying it without governance. She said that scaling AI and embedding it into the fabric of an organization is a far harder proposition than most businesses anticipate, and that infrastructure in most companies is not yet ready to support AI tools of any kind.

When a platform explains why a travel option has been ranked at the top, citing policy compliance, past behaviors and corporate benefits, traveler satisfaction follows quickly, she said.

Both Avasthy and Antony said the industry is moving from a phase of AI experimentation toward one of execution, with the goal of removing friction points rather than simply navigating them.

Amex GBT was spun off from American Express in 2014 as a joint venture and later became a publicly traded company.

On May 4, tech-focused investment firm Long Lake, backed by General Catalyst, agreed to acquire Amex GBT in a deal that would result in American Express Company exiting its minority equity stake. The transaction does not affect the brand licensing and commercial agreements between the two companies.

Artificial intelligence (AI) has become the most powerful accelerant the software industry has ever seen. As AI tools push individual developers to generate code at unprecedented rates, the rest of the software development lifecycle is struggling to keep pace.

The result is a fast-accumulating burden of ungoverned, untraceable code: delays, security vulnerabilities and a pipeline that generates output far faster than review, testing and security processes can absorb.

Engineers today spend up to 12 hours a week in meetings and only around 52 minutes actually writing code. Merge requests can sit in a queue for five to six days.

Around 84% of engineering time goes to maintenance rather than new feature delivery. GitLab calls this tension the AI Paradox.

“The amount of code that can be developed in this AI era has dramatically increased. In some cases, it is beyond what people could actually imagine four or five years ago,” Duncan Greenwood, Vice President EMEA of GitLab, told TechJournal.uk in an interview.

“Unless you have the right capabilities and the right platform to orchestrate and see the whole of the entire software development lifecycle, you get these downstream bottlenecks, particularly in things like code reviews, pipeline breakages and testing,” he said.

Greenwood said GitLab’s response is what it calls intelligent orchestration: a single platform that provides engineering teams and AI agents with complete end-to-end visibility into the software development lifecycle (SDLC).

“Build the code as quickly as you can, create the products, but make sure you understand what the rest of the software development lifecycle looks like. We definitely have this phrase within AI: it is creating speed, but it needs to be speed with control,” he said.

Founded in 2011 and headquartered in San Francisco, GitLab is the intelligent orchestration platform for DevSecOps (development, security and operations). The company serves more than 50 million registered users and approximately 50% of the Fortune 100.

GitLab consolidates code management, pipelines, security scanning, compliance and deployment history into a single unified data model. The company has been named a Leader in the Gartner Magic Quadrant for DevSecOps Platforms for four consecutive years. Greenwood, who is based in London, oversees all GitLab business across Europe, the Middle East and Africa.

Governance gaps widen

On June 23, GitLab published its AI Accountability Report, conducted by The Harris Poll and based on a survey of 1,528 developers and technology buyers across six countries.

The report examines whether organizations can track, govern and take responsibility for the AI-generated code entering their production systems. The findings confirm that adoption is outpacing control.

While 91% of organizations have two or more AI coding tools in active use and 78% report that developers are writing and committing code faster, 43% say they cannot reliably distinguish AI-generated code from human-written code in their own codebase.

Eighty-two percent say AI-generated code risks creating a new form of technical debt their organizations are not yet prepared to manage.

“AI coding tools have delivered on their promise of speed. But the events of the past few months, including supply chain attacks, reliability issues and regulators tightening expectations around AI traceability and provenance, are making clear that speed without control is a liability, not an advantage,” said Manav Khurana, Chief Product and Marketing Officer at GitLab.

“The teams thinking ahead are already asking the harder question: can we actually control all the code we are generating? The organizations that will ship trusted software faster are the ones building the foundations of accountability with context, traceability and governance baked into the platform, not just bolted on after the fact,” he said.

The traceability gap is particularly stark. While 87% of respondents were confident their team could determine within 24 hours whether AI-generated code contributed to a production incident, 34% of organizations that actually experienced such an incident were unable to do so.

The top structural barriers to control are difficulty distinguishing AI-generated from human-written code (43%), fragmented toolchains (40%) and systems that do not track code origin (39%). Only 28% say their SDLC tools are fully integrated with shared data and workflows.

Governance failures are near-universal. Ninety-two percent of organizations report some form of governance challenge with AI-generated code, and 80% say they adopted AI tools faster than they developed policies to govern them.

Eighty-five percent agree that the next phase of AI in software will focus less on generating code and more on governing it. With awareness rising, 91% say they are likely to invest in AI code governance tools in the next 12 months.

The governance challenge extends to regulatory compliance.

The EU AI Act is already reshaping expectations for software teams across Europe, while frameworks such as DORA (Digital Operational Resilience Act) and NIST 2 (NIST Cybersecurity Framework 2.0) impose additional obligations on how code is developed, audited and traced.

Greenwood said most organizations are struggling to keep compliance processes in step with the pace of AI-driven development, and that embedding policy requirements into the development workflow from the outset is more effective than applying them retrospectively.

He described a compliance approach built around what he called an immune system: policy requirements and audit obligations enforced during development rather than checked at the end, with vulnerabilities remediated before code reaches production.

Data sovereignty is a particular concern for European enterprises.

“In Europe, around 80 to 85% of our customers are deploying either in a dedicated environment or self-hosted,” he said.

He said the preference reflects growing unease about where AI-generated code resides and who can access it, a concern he expects to intensify as regulators sharpen their focus on traceability and provenance.

Proof at enterprise scale

Greenwood pointed to Barclays as a case study in the costs of toolchain fragmentation. The bank had built its software development operation around 12 separate tools, each handling a different stage of the pipeline. The result was a disjointed workflow in which handoffs between systems created delays, visibility gaps and mounting overhead for engineering teams.

“When we started with Barclays, they had something like 12 different tool sets within that supply chain of software development. They now save 180,000 developer hours per week,” he said.

He said the more entry points a software supply chain contains, the greater the risk that vulnerabilities will be introduced or go undetected. Consolidating onto a single platform gave Barclays’ engineers end-to-end visibility for the first time, enabling the bank to deliver software faster for the business.

Ericsson faced a comparable challenge at a different scale. As a global telecommunications company managing complex, high-stakes deployments across multiple markets, the cost of slow or insecure releases is high.

“They are 50% faster with deployment and they saved 130,000 developer hours over a six-month period,” Greenwood said.

Ericsson was also able to run 10 times as many test scenarios after consolidating its toolchain.

To explain how GitLab addresses the challenge of scaling in the agentic AI era, Greenwood described a three-part framework drawn from human biology.

“The motor system is executed at agent scale: the source code, the CI/CD (continuous integration and continuous delivery), the artifacts, and the deployments, rebuilt for thousands of concurrent agents. The nervous system is the context of the decisions, the lifecycle graphs and how the human actually interacts with the agents,” he said.

“And the immune system is the governance and the security: identity, policy and audit,” he said.

Greenwood pushed back on the narrative that replacing senior developers with junior staff and AI tools is an acceptable cost-cutting strategy, arguing that human context cannot be substituted.

“There is a gap in the developer community in the sense that, even with AI coming on board, there are still a lot of jobs that cannot be filled. The human in the loop is going to be an important factor for many years to come,” he said.

He said developer roles will evolve rather than disappear as AI scales up.

With software development volumes set to increase dramatically, human oversight will remain essential. GitLab’s EMEA team of more than 700 staff is set for growth as the business expands its customer base across the regions.

Britain is closing in on one of the most consequential technology decisions it has ever made: which quantum computing platforms deserve £1 billion in government backing, and which do not. The first contracts will be announced in October.

ProQure: Scaling UK Quantum Computing, the government’s phased procurement programme, is designed to answer that question through competitive evaluation rather than political choice. Phase 1 offers contracts of up to £14 million; later rounds scale to £75 million per company.

“We will fund 10 companies, up to about £14 million each, in that demonstrator phase to show that you have a potential route towards scalability,” said Sir Peter Knight, chair of the UK National Quantum Technology Program (NQTP)’s Strategic Advisory Board (SAB).

Knight said that companies advancing to the mega-QOPS evaluation round would see contract values rise to approximately £75 million each.

“That’s where the billion investment will be on the winning platforms,” he said.

A quantum operation per second (QOPS) is the standard unit for measuring quantum computing performance. The performance ladder runs from kilo through mega, giga and terra-scale, with one trillion QOPS (1 TQOPS) set as the UK’s national mission target by 2035.

Unlike classical bits, which hold a value of zero or one, qubits can exist in multiple states simultaneously, allowing a quantum machine to explore vast solution spaces in parallel. That makes quantum systems potentially transformative for drug discovery, cryptography, financial modeling and materials science.

At least £140 million will be committed at the Phase 1 stage. Interviews for shortlisted applicants are scheduled between July 20 and August 3, with the first contracts to be announced in October.

Knight said the programme must be customer-led, flexible and technically credible, with revenue certainty for companies.

“Who needs this stuff is a question that we should be asking all the time,” he said.

ProQure draws on the structure of the US Defense Advanced Research Projects Agency’s Quantum Benchmarking Initiative, but goes further by integrating procurement and validation, combining demand creation and performance testing into a single framework.

The NQTP operates two distinct advisory bodies to guide the sector. The SAB advises the government on overarching national strategy, while the Strategic Advisory Committee (SAC) sits directly within the National Quantum Computing Centre (NQCC) to guide its specific technical programs.

No single platform wins

Knight was speaking at Commercialising Quantum Global 2026, organised by Economist Enterprise on June 16 in London. His presentation, titled “So many platforms, so little time? When and how to start narrowing the field,” addressed the challenge of choosing between competing quantum hardware architectures at a pivotal moment in the technology’s development.

Knight chairs the UK NQTP’s SAB and has shaped the country’s quantum strategy since its inception in 2014. He is a professor of quantum optics at Imperial College London and has long advocated using government procurement as a policy lever to drive innovation to commercial scale.

ProQure must navigate a hardware landscape of unusual diversity. Six competing classes of quantum computing architecture are under active development globally: trapped ions, neutral atoms, photonics, superconducting circuits, silicon spin qubits and others.

“There are lots of pluses and minuses. There are companies in every single space,” Knight said.

The NQCC hosts test beds for each platform, having invested £30 million in an initial evaluation phase to allow researchers and policymakers to assess prototypes directly rather than rely on vendors’ own claims.

“The point of this was to accelerate our view as an informed customer and a user of these things, so that we could make advice on how this actually works,” he said.

Knight said he does not expect the global race to converge on one dominant technology. Different user communities have distinct computational needs, and no single architecture currently satisfies them all.

“I am almost certain there will be winning platforms, because the user community has different needs. It would be surprising to me that there would be a single platform that will be the winner,” he said.

He said the 1 TQOPS national mission cannot be delivered by hardware alone. Competitive commercial capabilities must span the full stack: hardware, control architecture, error correction, supply chain, software and algorithms.

“We need to combine the operational capability with the algorithmic development, so the hardware and software people work seamlessly together to make sure that this will work,” he said.

Evidence before politics

With billions of pounds at stake and six competing architectures in the field, Knight said the critical safeguard is objectivity. Platform down-selection must be driven by evidence, not political preference.

“It is really important that we have an evidence-based decision-making process, which enables us to choose things based on where we see the scalability is going to go,” he said.

Benchmarking is led by the NQCC in partnership with the National Physical Laboratory and the Quantum Software Lab at the University of Edinburgh, covering hardware, software and real algorithm applications.

Knight said objective comparisons of noisy intermediate-scale quantum machines against the path to utility scale are essential for establishing the baseline needed for fault tolerance.

“The gap between the fidelity that you have on a qubit [one error in 10³ to 10⁴ operations] and what is needed for a fault-tolerant machine is enormous. We have got to make sure that error correction is really part of the whole procedure,” he said.

Innovate UK is leading parallel work on critical supply chain resilience, hybrid quantum algorithms and collaborative research with the high-performance computing sector.

He said critical supply chain work was essential to identify the pinch points in delivering a utility-scale machine.

“We need to understand where we get the stuff from and where the limiting pinch points might be,” he said.

ProQure is the latest step in a 12-year national effort. The UK’s quantum programme, backed by more than £1 billion in public funding since 2014, now draws 2,500 participants to its annual showcase, demonstrating the journey from laboratory concept to commercial product.

The 2026–2030 spending review commits a further £2 billion to the broader quantum programme, with approximately £500 million for quantum computing, £200 million for sensing and positioning, navigation and timing, and £120 million for quantum networks, roughly doubling the UK government’s total quantum investment.

With Phase 1 contracts due in October and larger evaluation rounds to follow, the next two years will determine which platforms and which companies the UK is prepared to back at scale. Knight said those decisions will be made on the evidence, not in advance of it.

The question of whether quantum technology should be regulated has become one of the more persistent distractions in British technology policy. The real debate, one senior legislator argued this week, is not whether to regulate but how.

The United Kingdom has a proven model to draw on. Its fintech regulatory sandbox, developed with the Financial Conduct Authority (FCA) in 2016, has since been replicated in nearly 100 jurisdictions, many of which have taken the concept further than its originators. The lesson for quantum, the argument runs, is that intelligent regulation does not slow industries down; bad regulation does.

“Regulation can stifle, stop and crush innovation and development, but that is bad regulation. Right-size, right-touch regulation is not only not a stifler, but it is also a positive partner for growth, innovation and investment, and good for citizens, our companies and our countries,” said Lord Holmes, Member of the UK House of Lords.

“We developed the fintech regulatory sandbox with the FCA in 2016. Measure of success: replicated in just shy of 100 jurisdictions around the world, many of which have taken it further than us,” he said.

Lord Holmes drew a pointed contrast with the European Union’s approach to artificial intelligence (AI). He said the EU AI Act was bold and ambitious in its aims but overly prescriptive in its design, and had already caused difficulties early in its legislative journey. That experience, he argued, is precisely the cautionary tale that quantum policymakers must absorb before reaching for statute books.

“Principles-based, outcomes-focused regulation over prescription will always be problematic. Over-prescription will always stymie and potentially kill off innovation,” he said.

Among the principles Lord Holmes identified as most critical, interoperability stood out. He said it must be understood not only in technical terms but in regulatory and legislative ones.

A quantum framework built for the UK in isolation, without regard for how it sits alongside rules in the US, the EU, or Asia, risks creating friction that undermines the very competitiveness it is designed to protect.

He said the government’s most underestimated tool at this stage is not legislation but the power of signaling: conveying intention through narrative, convening power, purchasing power and acting as a good first customer for quantum products.

“Not passing a single regulation necessarily at this stage, but signaling intention, direction done through narrative, but also done through convening powers, purchasing powers and being a good first customer,” he added.

Cryptography clock

Lord Holmes was speaking at Commercialising Quantum Global 2026, organised by Economist Enterprise in London on June 17. His address focused on the role of regulation in scaling the UK’s quantum industry and the policy decisions policymakers must begin making now.

A Conservative peer and long-standing advocate for technology law, Lord Holmes has championed regulatory reform across sectors including financial services and digital identity. At the conference, he turned his attention to post-quantum cryptography (PQC) as an immediate and concrete test case for regulatory clarity.

The absence of government guidance on PQC is already flowing into private contracts. He noted that in legal agreements, particularly third-party outsourcing contracts, clauses on quantum cryptography are being inserted not because of any settled standard but because uncertainty leaves the contracting parties to absorb the risk themselves.

“We are already seeing this in legal contracts, particularly in third-party outsourcing contracts, where terms are going in around this whole issue. They are going in place because of current uncertainty, but what that means in reality is the risk is being borne by the contracting parties,” he said.

He said that some institutions choosing to self-regulate on PQC may gain a competitive edge, or simply find themselves over-regulated with no advantage at all.

On the question of mandation, Lord Holmes said the UK must consider what happens if the US moves further and faster, leaving British businesses stranded between domestic ambiguity and foreign standards. He offered “longer keys” as a potentially controversial alternative worth examining.

He said the absence of scientific certainty is not in itself a reason for regulatory or statutory uncertainty.

“It does not mean we need to reach for hard, gilt-edged regulation, but it does mean that signaling, that discussion, that engagement for government and regulators with businesses to learn and to be part of the journey together,” he said.

The same question of regulatory ambition runs through the thornier issue of protecting domestic quantum companies from foreign acquisition. He invoked two of Britain’s most cited technology exits to frame the sovereign risk of getting this wrong.

ARM was founded in Cambridge in 1990 as a joint venture between Acorn Computers, Apple and VLSI Technology; it was acquired by Japan’s SoftBank in 2016 for approximately £24 billion. DeepMind was founded in London in 2010 by Demis Hassabis, Shane Legg and Mustafa Suleyman, and acquired by Google in 2014 for a reported $500 million. Both companies retain UK headquarters but are no longer British-owned.

“If we imagine what happened with ARM and DeepMind, if we still had them as UK businesses, that would be a key part of our UK sovereign AI capability,” he said.

But Lord Holmes was careful to frame the issue as a dilemma rather than a directive. Telling UK quantum businesses they cannot sell to overseas buyers, he said, requires the government to put a credible alternative on the table: domestic sources of capital and a growth roadmap that makes staying at home commercially rational. Without that, protective legislation becomes a constraint rather than a shield.

“It is one thing to put legislation and regulation in place to say we are going to protect these companies, but if that means protecting them and not enabling them to get to the size, the scale, the opportunity, I am not sure that in any sense would be a good regulatory intervention,” he said.

Engage now, legislate later

Lord Holmes made it clear that he is not calling for sweeping quantum legislation. What he is calling for is earlier and deeper engagement between regulators and the industry they will eventually govern, well before formal frameworks become necessary.

“In no sense am I suggesting it is time to bring about a huge piece of cross-cutting quantum legislation. What we need is early regulatory engagement, so regulators, policymakers and special advisors get alongside quantum businesses, quantum funders and the entire ecosystem to see what elements can be developed at this stage,” he said.

Alongside formal regulatory engagement, Lord Holmes identified a set of non-legislative tools that governments consistently underestimate. Convening power, purchasing power and acting as a first customer are all instruments of industrial policy that can shape a nascent market without requiring a single statute to be passed. He said the danger is that governments mistake the absence of ready-made legislation for a reason to stay on the sidelines.

“It is critically important for governments not to think that because quantum is not quite at that level for cross-sector regulation, they should not get involved. They should get fully involved, to understand all of those emerging issues,” he said.

He also pointed to the talent dimension of quantum regulation, noting that quantum sits primarily within the science department but carries implications across many parts of Whitehall. Home Office immigration rules, he said, are as directly relevant to the sector’s growth as any technology-specific policy, and tweaking them to attract and retain overseas researchers must be part of the regulatory conversation.

“What do we need in terms of tweaking regulations to enable talent not only to flourish in this country but the talent that we can attract from overseas?” he said.

He closed by anchoring the entire regulatory debate in human stakes, asking his audience to imagine a single patient, a decade from now, whose Parkinson’s disease has been transformed by quantum-enabled drug discovery. That individual represents the opportunity visible in a grain of sand.

Britain ranks third globally in academic quantum research, is home to the second-highest number of quantum startups, and has a growing record of real-world deployments. Yet the country is failing to convert that scientific leadership into commercial scale, with the economic and national security benefits of quantum technology at risk of accruing to other nations.

The warning came from Lord Hague, former foreign secretary and chancellor of the University of Oxford, speaking at a major quantum conference in London. He co-authored a quantum strategy paper with former Prime Minister Tony Blair, published by the Tony Blair Institute for Global Change in November 2025.

“While Britain is a global leader in all these respects, we often fall short in converting this research into commercial applications. Action is needed to ensure the value of innovation generated by UK quantum research remains within our country, rather than slipping away,” he said.

“Last year, Oxford Ionics was acquired by a globally leading American quantum company in a $1 billion deal. Such a valuation is a phenomenal achievement that underlines the strength of our ecosystem, but imagine how far our talented quantum companies could go if they had the British or European capital they needed to grow into global leaders in their own right,” he said.

The stakes are considerable. McKinsey analysis found that quantum could generate up to $2.7 trillion of economic value worldwide within a decade, with global investment in quantum startups nearly doubling to $2 billion in 2024 and the sector forecast to grow by 35 percent annually until 2032.

Lord Hague identified three problem areas holding the UK back:

• Research and development: the country leads in long-term foundational work but must do more to focus it on specific engineering challenges that block commercial deployment, such as finding the most efficient paths for qubits to interact and reducing errors in quantum calculations.

• Capital: most interest in British quantum companies currently comes from Silicon Valley, leaving domestic firms without the funding needed to scale at home.

• Resilience: UK startups remain dependent on foreign suppliers for critical components including cryogenic cooling systems, while the country lacks the advanced packaging facilities needed to scale quantum computers.

On resilience, he said UK startups remain dependent on foreign suppliers for critical components including cryogenic cooling systems, while the country lacks the advanced packaging facilities needed to scale quantum computers.

“Without the support of a broad and resilient ecosystem, our companies will be forced to move abroad in order to scale,” he said.

The stakes extend beyond economics. Lord Hague adapted Theodore Roosevelt’s celebrated maxim to argue that military and diplomatic power alone is insufficient in an era of strategic technology competition.

“In today’s world of technological acceleration, big sticks are no longer sufficient. You also need a deep stack with your big stick,” he said. He added that some degree of domestic quantum capability will be critical to national security.

The exodus of British technology startups, largely to the United States, has long been attributed to a shortage of domestic private capital and the limited size of the UK’s home market, according to some industry experts.

The government has sought to address this through two state-backed vehicles: the £500 million Sovereign AI Fund and the £27.8 billion National Wealth Fund, both designed to scale domestic technology champions and retain high-value companies in the UK. But with mandates spanning a wide range of industries, neither fund has been able to direct the concentrated, high-risk capital that deep technology sectors such as quantum demand.

Policy momentum at risk

Lord Hague was speaking at Commercialising Quantum Global 2026, organised by Economist Enterprise and held in London on June 16. The annual conference brings together researchers, investors and policymakers to discuss how advances in quantum technology can be translated into commercial applications.

He acknowledged that both recent governments have made meaningful progress. Former Prime Minister Rishi Sunak’s 2023 National Quantum Strategy set out a framework of research hubs, challenge-led innovation funding and talent programs. The subsequent administration built on those foundations, announcing five new quantum research hubs shortly after taking office.

“Earlier this year, the technology secretary, Liz Kendall, committed £2 billion to invest in the sector. Her plans include funding to help companies scale quantum computing and develop new uses in areas like pharmaceuticals and financial services,” he said.

Kendall’s package also covers quantum sensors and navigation, additional support for the network of quantum research hubs and funding for Edinburgh’s Quantum Software Lab. Lord Hague praised the scale of the commitment but was unambiguous that more is needed.

He said that regardless of any change in leadership, it will be vital for the government to remain committed to supporting quantum and to go further in addressing the three key challenge areas of research, capital and resilience.

With other countries investing heavily, Lord Hague said the UK must be more ambitious. He called for a quantum translational research group to bridge the gap between academic science and commercial deployment, potentially modeled on Finland’s Technical Research Centre, which already works with Finnish quantum companies to overcome engineering constraints.

“It will be vital to ensure that this funding is well spent, with a focus on real-world applications in areas like healthcare and defense, rather than hitting technical benchmarks,” he said of the government’s £1 billion procurement plan for quantum computers.

The speech drew heavily on the Blair-Hague report, which warned that despite the UK’s strong starting point, the country risks failing to convert its quantum research leadership into commercial scale and strategic value.

“Tony Blair and I, despite our past political differences, worked together last year to author a paper on quantum technologies. Both of us know that technology and innovation are the key ingredients for the UK’s economic prosperity and national resilience,” he said.

The quantum paper was the seventh in the pair’s New National Purpose series, which has called on the government and MPs of all parties to make technology leadership a defining national priority. Lord Hague noted the personal irony: when he was leader of the opposition, most of his energies were spent trying to defeat Blair, which he described as “the least successful part of my entire life.”

“For the most part, politicians and civil servants are waking up to the importance of artificial intelligence (AI). They have not all done so yet with quantum, and this is frustrating, given that the global AI market has already concentrated around the US and China, while there is greater scope for Britain to play a significant role in the global quantum market,” he said.

The Blair-Hague report identified the same three structural failures Lord Hague outlined in his speech:

• Translation gap: world-class physics is not being consistently converted into deployable products.

• Capital and adoption gap: the UK’s largest quantum hardware funding announcements are ten times smaller than those of France and Australia.

• Stack gap: only 513 companies in Britain’s extended quantum supply chain, compared to 1,128 in Germany and 658 in France.

The Q-Day threat

Beyond the economic case, Lord Hague set out a stark national security argument. Quantum computers are expected to break classical encryption methods by the 2030s, a milestone already referred to as “Q-Day.”

“Britain’s enemies are already stockpiling our encrypted communications to break open. Quantum computers will not expose small flaws in the cyber security of encrypted software, but break the encryption altogether,” he said.

Too few organizations are taking serious steps to prepare for this eventuality, he warned. The Blair-Hague paper recommended requiring regulated critical industries to publish updates on their quantum-migration plans every two years, through bodies including the National Cyber Security Centre and the MI5 National Protective Security Authority.

Lord Hague highlighted recent UK demonstrations of quantum’s practical potential:

• The Royal Navy trialed quantum navigation systems over the Arctic with researchers from Imperial College London, providing a more secure alternative to satellite-based positioning.

• Bristol University demonstrated the first long-distance secure transfer of data over a quantum communications network.

• Oxford Quantum Circuits recently received a £100 million investment from the British Business Bank.

Quantum’s breadth as a general-purpose technology extends to drug discovery, the development of cheaper, more climate-friendly fertilizers and advanced weather and disaster forecasting. Exeter College, Oxford, is planning a quantum innovation district at the western gateway to the Oxford-Cambridge growth corridor.

On specific policy interventions, Lord Hague proposed fiscal incentives including research and development (R&D) tax relief and reduced employer national insurance contributions for quantum-related hires in sectors such as finance and pharmaceuticals. He also called for quantum procurement champions with technical expertise to be appointed in each relevant government department, tasked with horizon-scanning for new use cases and running regulatory sandboxes.

“Advanced market commitments work by setting out a commitment to purchase or subsidize a product if someone can invent and produce it to a high standard. They have been used to galvanize the creation of new vaccines, and similar advanced market commitments for quantum technologies could galvanize the sector and unlock new use cases,” he said.

Lord Hague also called for the National Wealth Fund to co-invest in quantum-enabling infrastructure, particularly domestic cryogenic cooling capacity, to de-risk private investment. Strategic bilateral partnerships with allied nations, including Germany, the Netherlands, Japan and Canada, would be needed to address supply chain gaps identified through a formal capability-mapping exercise.

The choices made by the next government, he concluded, will determine whether Britain leads in quantum or becomes an incubator for other countries’ industries.

The UK's quantum sector has moved from scientific promise to commercial delivery. After more than a decade of research investment, the government is now focused on a harder challenge: getting quantum technologies out of the laboratory and into real-world use.

The practical applications are concrete. Lord Vallance, Minister for Science, Innovation, Research and Nuclear, cited drug discovery, sensing minute changes in the body, energy optimization and supply chain transformation as problems that quantum could solve and that are genuinely beyond the reach of today's technologies.

“We’ll be launching a £33 million call to directly support the development of quantum computing supply chains here in the UK, and a £13 million call on software. This isn’t just about hardware. It’s about software solutions, error correction, industrial readiness of the entire system and, often forgotten, data readiness,” said Vallance.

"I am pleased to announce the launch of the UK Quantum Standards Network (QSN), led by the National Physical Laboratory, and supported by £10 million of government funding. This will be the UK central hub for quantum standards," he said at the June 16 event.

The QSN is described by the government as the world’s first national framework of its kind. Partners include the British Standards Institution (BSI), UKQuantum, the National Quantum Computing Centre (NQCC) and the National Cyber Security Centre (NCSC). Its purpose is to ensure quantum systems can be tested, benchmarked and integrated so they work together and can be deployed reliably.

“Standards must be shaped by the sector itself. Whether you are in industry or academia, this is your opportunity to help define the frameworks that will underpin global quantum markets,” Vallance said.

On the sensing side, a £32.8 million competition to support quantum sensing and position, navigation and timing (PNT) supply chains opened the previous week. Vallance also announced a further £14.3 million research and development call focused on quantum sensing missions, due to go live in July.

UK Research and Innovation (UKRI) has issued an additional £238 million in funding calls for quantum research this year, alongside long-term commitments to the National Quantum Computing Centre through a 10-year program designed to support initiatives requiring an extended outlook.

Procurement drives demand

Vallance was speaking at Commercialising Quantum Global 2026, organised by Economist Enterprise and held in London on June 16. His address covered the full breadth of the UK’s quantum strategy, from infrastructure investment and procurement to demand creation and international standards.

As Minister for Science, Innovation, Research and Nuclear, he oversees the government’s quantum and fusion programs and has been central to shaping the UK’s national quantum strategy.

“We launched this year Procure, our flagship quantum computing program, culminating in a procurement commitment of up to a billion pounds to target large-scale quantum computers by the early 2030s. If we can do it sooner, so much the better,” Vallance said.

The programme was already generating tangible market impact, with companies announcing major funding rounds and investment decisions in the UK on the basis that the government would be an early customer. Not every organisation would buy a quantum computer on day one, but access to one would be essential.

At London Tech Week on June 9, the government launched a £12 million fund focused on hybrid quantum computing algorithms that combine conventional and quantum computing approaches. It also announced the Quantum Growth Alliance, bringing together 10 major industry players from financial services, life sciences, energy, defense and telecoms to define real use cases and accelerate the pathway from innovation to adoption.

Vallance said the Alliance brought together companies like HSBC, Barclays, GSK, BP, Rolls-Royce, BAE Systems, BT and Vodafone not as observers of quantum but as its future users: the organizations that would deploy these technologies at scale across the economy.

By engaging future users now, the government hopes to define real use cases and accelerate the pathway from innovation to adoption. Supply alone is not sufficient for the sector to scale, Vallance said. Clear, credible demand signals from potential customers are equally critical.

Investment follows ambition

The QSN’s technical scope is broad. It will oversee specifications ranging from the linewidths of ultra-narrow lasers used to control qubits inside quantum computers to the size, weight and energy-efficiency requirements that ensure one quantum sensor’s readings can be trusted against another’s.

“Its purpose is simple: to ensure that quantum systems can be tested, benchmarked and integrated, so that they work together and can be deployed at scale. That matters for confidence, it matters for interoperability and it matters for enabling UK companies to compete globally,” he said.

International confidence in the UK’s quantum infrastructure is already visible. American quantum component manufacturer Vescent recently selected the National Physical Laboratory (NPL) as the location for its first office outside the United States, citing the UK’s emerging standards ecosystem.

The UK ranks second globally for the number of quantum companies and levels of private investment and is home to three of the top five quantum clusters worldwide. More than 12 years of sustained public investment through the National Quantum Technologies Programme (NQTP) has built capability across computing, networking, timing, imaging and sensing.

Vallance said scaling quantum deployment was a challenge relevant to many sectors, with the private sector now expected to take the lead in ensuring quantum companies not only start in the UK but scale and remain here.

“The task now is very clear: it’s to translate that strength into real economic and societal impact, and to do that quickly,” he said.

Private investment is following the government’s lead. Oxford Quantum Circuits (OQC) closed a £260 million Series C in June 2026, Europe’s largest ever private funding round for a quantum computing company, with participation from the British Business Bank. Quantum Motion raised $150 million and US firm Rigetti committed $100 million to expand its UK presence.

Vallance declined to put a precise timeline on when quantum would become mainstream but said the direction of travel was no longer in doubt. His ambition is to make the UK the place where quantum moves fastest from ideas to impact, delivering long-term growth, resilience and benefits across the economy.

Salesforce has opened its customer relationship management (CRM) platform to any external artificial intelligence (AI) agent or developer tool, allowing products such as Claude and ChatGPT to query, reason over and update live Salesforce data without leaving their own interfaces.

The capability, called Headless 360, represents a significant architectural shift: Salesforce is no longer positioning itself purely as the interface through which enterprise work gets done, but as the data and logic layer beneath any interface.

Launched on April 15, Headless 360 delivers three core innovations: new model context protocol (MCP) tools and coding skills that give any coding agent full access to the Salesforce platform; a new experience layer that renders rich, interactive components natively across Slack, Salesforce’s workplace messaging platform, as well as Claude, ChatGPT and other external tools; and new controls for managing how agents behave in production.

“Salesforce, for 27 years, has built technology and products to uplift humans, to enable humans. Headless 360 is how we actually do the same thing for agents, how we enable agents to work with humans. This changes the game,” said Joe Inzerillo, President of Enterprise and AI Technology at Salesforce.

Headless 360 allows any external tool or developer to connect to and update Salesforce while respecting the same permissions model that has been built over nearly three decades. Inzerillo said that means the sharing rules that prevent an intern from seeing board-level data are automatically enforced within any connected agent, with no need to rebuild the security model.

Ansar Ahmed, Vice President of Solution Engineering for UK and Ireland at Salesforce, demonstrated this live on stage at the World Tour London event, connecting Salesforce to both Claude and ChatGPT in minutes, querying a live sales pipeline from within each tool and updating a CRM record directly from within Claude.

“For 25 years, getting value out of Salesforce meant logging in, clicking through screens and navigating a UI. But AI agents do not use browsers. They call APIs, they invoke tools, and they run autonomously,” Ahmed said.

“The AI did not change. The context did. Salesforce has always been the system of record for your people, and now it is also a system of record for your agents,” he said.

Zahra Bahrololoumi, President, UKI at Salesforce, set the broader context for the day. The company has committed $6 billion to its UK operations through 2030 and recently opened a new AI center forming a connected campus with its Salesforce Tower in London.

“Agentic AI doesn’t just benefit commercial businesses. It can also elevate citizen services, from policing through to transport, health and welfare,” she said.

Salesforce has already applied the approach internally, building a legal request routing system using Headless 360 and Claude Code in five weeks and saving an estimated 12,000 lawyer-hours annually.

Police calls and premium retail

The Agentforce World Tour London event, organized by Salesforce and hosted by Bahrololoumi at a London venue on June 18, brought together enterprise customers, partners and developers. Several customer speakers presented live deployments that moved well beyond the pilot stage, anchored by a finding from Inzerillo.

“94% of people who have gotten on the journey are not seeing the ROI (return on investment) for AI. They have spent the time and money, but they are not seeing the ROI,” he said.

Inzerillo said the most common causes are fragmented data, agents that understand knowledge but cannot take action, failure to escalate to a human when stuck, and security gaps. Salesforce addresses this with five systems: context, work, agency, engagement and insight.

One of the day's most striking customer stories came from UK law enforcement.

Tom Kempster, Director of Digital and Innovation at Thames Valley Police, described a force handling between 1.3 and 1.4 million contacts per year across Thames Valley, Hampshire and the Isle of Wight, with average wait times on its non-emergency 101 line running at 26 minutes.

“Our traditional approach of peopling our way out of the situation was not going to work. We were looking at a potential £11 million opex increase to do this without technology,” Kempster said.

The force deployed Bobbi, an agentic AI built on Agentforce, for non-emergency contacts. Bobbi now handles 70 to 75% of those contacts autonomously, totaling approximately 162,000 interactions. Average wait times have fallen from 26 minutes to around 2.5 minutes, and customer satisfaction has risen by 15%.

“Any channel that the police open could potentially be an emergency. We cannot dictate that you can only use it under these circumstances, and we would be failing the public if we gave them a service that said ‘Sorry, I cannot help you with that,’ which is your more traditional chatbot approach,” he said.

He added that what sets Bobbi apart is that the contact center team’s expertise in handling distressed callers, including empathy, victim agency and de-escalation, was translated directly into how the agent communicates. The force has had multiple cases of people reaching out through Bobbi who could not use any other channel, resulting in arrests.

“It is not just about diverting contact. It is about delivering the service that the public expects, and it was a combination of what the agentic AI can do with its language that we have never seen before in technology,” he said.

Hannah Rose, Distinguished Solution Engineer at Salesforce, demonstrated how Canada Goose, a global performance luxury brand, has automated over 89% of routine customer service tasks using Agentforce while preserving the premium experience its customers expect.

“Before Agentforce, providing that high-touch service meant teams were jumping from system to system, trying to piece together a customer’s history, all while that customer waited. Every disconnected interaction became a make-or-break moment for customer loyalty,” Rose said.

A live demo showed the Agentforce AI recognizing a caller without requiring identification, confirming the correct order, selecting the customer’s preferred channel to send a return label, and escalating to a human stylist when styling advice was needed, passing the full conversation context and purchase history across seamlessly.

“Most AI systems can answer questions, but very few can carry this level of customer context into every interaction,” Rose said.

She said Agentforce Script allows brands to set hard guardrails in natural language, ensuring the agent behaves consistently each time a defined situation arises, which is critical in regulated industries where the wrong answer carries compliance risk.

Pipeline agents and F1

Katie O’Neil, Director of Product Marketing at Qualified.com, presented Adecco, the world’s largest staffing and recruiting company, which deployed an AI sales development representative, Ecco, on its website using Qualified and Headless 360. Ecco now sources 85% of the company’s inbound pipeline.

A demo showed Ecco identifying an inbound enterprise buyer, engaging her by voice on the website, switching to French on request, and following up with a bespoke email before booking a meeting with the aligned account executive, all without a human SDR (sales development representative) involved.

Qualified has more than 700 customers, all live in production, with every new customer going live within 45 days. O’Neil said the agent is configured in plain language via Qualified’s Agent Studio, requiring no coding or IT involvement, and can engage buyers across text, voice and video.

The event closed with Formula 1, whose four-year partnership with Salesforce uses Data 360 to serve 831 million fans globally. Tom White, Trackside IT Engineer at Mercedes-AMG PETRONAS Formula One Team, compared the challenge facing F1’s lean marketing teams to a pit stop.

“Imagine you are a Formula 1 driver pulling into the pits, engines roaring, time ticking. You need to get out fast, and you pull into your team’s garage expecting the full pit crew, but there is only one person. Compare that to the reality of F1: 18 specialists, sub three seconds, done. That is the difference between how some marketing teams work today and what we will show you next,” White said.

Agents now handle 70 to 75% of fan queries autonomously during peak race periods, with average handling times down 50%. An F1 marketing demo surfaced from Data 360 that Australia had three times the baseline viewership for last year’s British Grand Prix, prompting an audience expansion the team had not considered.

Stefano Domenicali, President and CEO of the Formula 1 Group, said the sport has grown into an entertainment and sales ecosystem that demands personalization at scale.

“The way Salesforce represents technology by keeping the human being at the center is really the key to your success,” he said.

For Salesforce, the day’s presentations pointed toward a near-term future in which the boundaries between enterprise software, AI agents and customer experience collapse into a single continuously operating layer. Both Bahrololoumi and Inzerillo said the next phase will focus on deepening agent autonomy, expanding Headless 360 integrations and scaling the London AI center into a hub for skills development and enterprise experimentation.

Genomics is on a collision course with its own computational limits. The volume of DNA data that must be processed to deliver personalized medicine is growing faster than classical computers can handle, and a world-leading research institute believes quantum computing is the only credible answer.

For most genomic workloads, classical methods remain sufficient. But as medicine moves toward whole-genome sequencing for individual patients, projected to cost as little as $50 per person, the matching problems involved become too large and too complex for heuristics-based approaches to handle reliably.

“Genomics eats Moore’s Law for breakfast,” said James McCafferty, chief information officer of the Wellcome Sanger Institute. “So much of the classical work in genomics is based on heuristics and can be very fragile. When you get into the world of precision medicine, where you can’t be forever applying heuristics, then you need to be thinking about new technology.”

The Wellcome Sanger Institute has already loaded the hepatitis D genome onto a quantum computer, a first for the field, and is working progressively toward more complex targets: the SARS-CoV-2 spike protein and, ultimately, the human leukocyte antigen region, which encodes the entire human immune system across approximately 60 million DNA bases.

“There is no field called quantum genomics, but we are going to create that field, and we are going to develop the fundamental algorithms that allow us to execute through some of these most difficult problems,” McCafferty said.

He said one goal is to build intelligence into the pipeline so that classical algorithms know when they have reached their limit and when to invoke quantum processes instead. He added that cancer is the institute’s most significant long-term target, given that the disease is almost entirely driven by genetic mutations.

Scaling toward a million qubits

The presentations were delivered at the QBase Real World Use Case Showcase, held on June 1 in London and organized by EdenBase Ltd. The event was moderated by Dr Eloisa Angeles, partner at EdenBase, and brought together five quantum leaders to demonstrate practical applications of quantum technology across sectors.

Mark Webber, head of quantum algorithms at Universal Quantum, opened the session with a frank assessment of where the industry stands.

Today’s machines carry between 100 and 1,000 qubits; the applications that matter most require 100,000 or more. Yet resource estimates are falling. The number of physical qubits needed to break RSA encryption has already dropped from an early estimate of 50 million to approximately 100,000, as algorithmic and error-correction techniques have improved.

“We are not yet at the scale to solve these problems in a way that provides an industry or research advantage,” Webber said. “But the hardware is developing very rapidly, and hardware platforms focused on scaling feel that they can get to this scale relatively quickly.”

Universal Quantum builds trapped-ion machines and has focused from the outset on reducing what Webber called scaling friction. The company, which has more than 100 staff in the UK and Germany, collaborated with Rolls-Royce and River Lane on a computational fluid dynamics (CFD) project to optimize airflow through aircraft engines. The approach runs the linear component of the problem on the quantum computer while retaining a classical optimizer in a hybrid loop.

“Quantum computers work best on linear problems,” he said. “In the case of CFD, there are relatively straightforward ways of mapping this to a quantum computer. The challenges of getting the data in and getting it out are where the key work has been focused, and solutions are now relatively well understood.”

Quantum inside the data center

Richard Murray, chief executive and co-founder of ORCA Computing, said quantum’s most immediate opportunity lies not in replacing classical computers but in being embedded within them. ORCA builds photonic quantum computers using off-the-shelf telecommunications components, operating at room temperature and installable inside an existing data center in approximately six hours.

“No computer that has ever been built operated in isolation,” Murray said. “Our observation is that quantum will not be any different, especially when you are looking to unlock the most near-term applications.”

He disclosed that ORCA recently installed one of its systems within the Toyota data center, a commercial data center in Japan.

“This hybrid future is not so much future; it is about what you can do today,” Murray said.

The company is embedding small quantum subsystems within classical generative artificial intelligence (AI) models, demonstrating results in generating new peptides for chemistry, image-data pattern recognition and cybersecurity.

“What you have to do is provide the tools so that machine learning engineers can do quantum through a well-organized software stack, without worrying too much about it,” he said. “They are already in demand. They cannot take a sabbatical to go and do a PhD.”

During the Q&A session, Murray addressed the geopolitical pressures bearing down on the industry. He said the US government has injected approximately $1 billion into IBM and is taking equity positions in domestic quantum firms, while France restricts quantum procurement to French companies. Trade restrictions have caused components sourced from China to the US to double in price overnight.

Murray said the industry is operating in what he described as a massive commercial, geopolitical and security race.

“Strangely, we are trying to build some tech in a world where half of the discussion is about geopolitics,” he said.

Finance optimization benchmarked

Irene Papaefstathiou, quantum solutions lead at Qoro Quantum, brought the discussion into financial services. Classical computers are hitting practical ceilings in areas such as portfolio optimization, stochastic risk modeling and fraud detection, she said. Finance practitioners routinely solve proxy models rather than the actual problem, because the real version is too computationally demanding.

“It is clear that classical computation is hitting some walls in interesting problems in finance,” said Papaefstathiou, who previously worked on quantitative applications in finance at the quantum team of Moody’s. “You only need researchers to pick the problems they want to study, and then Maestro picks the simulation method that is most efficient. It is just 20 lines of code instead of thousands.”

Maestro is Qoro Quantum’s execution platform, which allocates computations across CPUs, GPUs and QPUs without requiring users to specify how. In collaboration with Partners Computing and a central bank, the platform addressed a liquidity settlement problem by enabling banks to settle transactions instantaneously.

Without optimization, banks must hold 50 to 70% more liquidity than necessary. Qoro’s approach outperformed both simulated annealing and the central bank’s own heuristic solver across a specific parameter range.

Sensing what lies beneath

Tony Lowe, chief executive of Delta.g, offered the most commercially immediate use case of the afternoon: finding objects underground without drilling. Accidental utility strikes, the result of digging without knowing what lies beneath, cost the UK £2.4 billion a year.

Current underground sensing technologies, including ground-penetrating radar and magnetometers, are decades old, in diminishing returns and produce high rates of false positives and false negatives.

Lowe said he is not a quantum physicist but an operator, and that however impressive the technology, it means nothing unless it solves a real customer problem. He said his sole measure of success is whether quantum can deliver results today

Delta.g’s quantum sensor cools rubidium atoms to temperatures colder than space, places them in a quantum superposition, then drops and measures them to detect gravitational density contrasts underground. Lowe described it as “noise-canceling headphones for underground survey,” eliminating environmental interference at source rather than measuring through it. The system is passive, unspoofable and requires no re-baselining.

The company is working with Saudi Aramco, BP, Shell and BAE Systems, and has delivered programs for the UK Department for Transport through National Rail and National Highways.

Lowe noted that Delta.g won a US government bid that was subsequently canceled and relaunched as a US-only bid, reflecting a broader pattern of protectionism that he said UK quantum firms must navigate.

He said his personal mission is to show that quantum can be useful in the near term, and that early industrial successes will strengthen the case for continued scaling.

“I personally have a vision that quantum will discover near-term applications,” he said. “On the back of significant industrial successes, we then have a system that keeps scaling.”

Closing the event, Eric Van der Kleij, partner at EdenBase and co-founder of QBase, said the willingness of competing quantum companies to share a platform was itself a sign of progress, describing the dynamic as co-opetition that he believes will help the entire industry move forward.

For years, sustainability sat at the margins of corporate strategy, funded by goodwill and governed by pledges. Artificial intelligence (AI) and data-driven optimization are now changing that, giving boards a language they understand: return on investment.

The shift is visible across sectors. Energy optimization powered by AI and the Internet of Things (IoT) is generating measurable savings for large enterprises. Green economy stocks are outperforming major indices. Food researchers are warning that AI tools can only deliver where basic digital infrastructure exists. And climate lawyers are watching voluntary commitments harden into legal duties.

“If sustainability can be linked as a business differentiator, it has a direct impact on the top line and bottom line. If you are able to craft the business case, that is generating interest,” said Hemakiran Gupta Kuralla, global head of sustainability services practice at Tata Consultancy Services (TCS).

“We show you how you can save in the whole energy optimization chain, and then I take my money out of the savings you make. We signed with one of the largest retail chains worldwide in North America. We are talking about $8 million to $10 million per year in savings,” he said.

The approach operates on a gain-sharing model: TCS identifies savings across a client's energy chain and takes its fee from the savings generated, meaning clients carry no upfront financial risk.

The model is underpinned by TCS Clever Energy, the company's AI and IoT-based energy management platform, which uses AI and machine learning (ML) frameworks to monitor energy demand, detect usage anomalies and generate predictive analytics.

The platform acquires real-time energy data from diverse infrastructure spread across geographies, including legacy and proprietary systems, and provides around-the-clock monitoring with an enterprise-wide consolidated view for faster decision-making.

“Think of sustainability as a differentiator that builds resilience into your business operations. Carbon reduction is a byproduct. Environmental, social and governance (ESG) will happen, but if you look at it from a resilience point of view, you get the board to agree,” Kuralla said.

The compelling question for boards is not whether to be green, but what happens when energy supply fails. Banking, manufacturing, and retail operations are all vulnerable to grid disruptions, and energy optimization technology directly addresses this exposure.

Commercial and industrial buildings account for 94% of the world’s total delivered energy and more than one-third of all greenhouse gas emissions.

“We want to be the world’s largest AI-led tech services company. Now 80% of the energy consumption across all our delivery centers is renewable. It used to be about 25% about five or six years ago,” he said.

TCS is part of the Tata Group, a conglomerate of more than 50 companies that has structured sustainability governance through its Alingana framework, meaning “embracing togetherness.” The framework requires every group business to manage decarbonization, circularity and nature and biodiversity, with each company setting its own targets and tracking progress monthly.

Food systems under strain

Kuralla was speaking at Economist Impact’s Sustainability Week in London, where Vijay Vaitheeswaran, global energy and climate innovation editor at The Economist, moderated a series of conversations on translating sustainability commitments into commercial outcomes. The session brought together voices from technology consulting, food systems research, investment banking and environmental law.

Pratima Singh, principal for policy, research and insights at Economist Impact, presented findings from the newly launched Resilient Food Systems Index (RFSI), a study mapping food security performance across 60 countries across four pillars: food affordability, availability, quality and safety, and climate risk responsiveness.

“We need to feed, but more importantly, nourish, 10 billion people by 2050. This is not a challenge that any one of us, or any one group, can solve on our own,” she said.

The index, supported by Cargill and built on over a decade of Economist Impact food security research, found that more than 673 million people face severe food insecurity, while 2.6 billion cannot afford a healthy diet. Portugal leads with a score of 76.8; the Democratic Republic of Congo scores 34.8. Even the top-ranked country falls well short of a perfect score.

“There is limited ability to scale agri-tech solutions. We heard a lot about AI and technology being the solution for climate and for food, but there is limited ability to scale some of these innovative solutions when very basic enablers like reliable electricity, the internet or other digital tools are not available and not yet universal,” she said.

Infrastructure is the binding constraint on food system resilience. The RFSI availability pillar scores an average of 58.3 across the 60 countries surveyed, with transport and logistics lagging in many nations, increasing post-harvest food losses. Climate risk responsiveness is the weakest pillar at an average of 56.4.

“We can only manage what we measure. We need to rethink the blueprint for food systems, because the challenges we are facing today and in the next few years are going to be very different, and we cannot use yesterday’s playbook to solve tomorrow’s challenges,” Singh said.

Capital and the courts

Chuka Umunna, global head of sustainable solutions and Europe, Middle East and Africa (EMEA) head of green economy investment banking at J.P. Morgan, told the session that geopolitical disruption has not weakened the investment case for sustainability; it has transformed its framing.

“Energy transition, energy security is the other side of the coin to national security and strategic autonomy. That has changed the way investors are looking at this,” Umunna said. “The AI tailwind is particularly driving interest in renewables. It means that people are overweight utilities, which they see can benefit from the growth of the grid as we need to upscale.”

Green economy stocks are up 89% over the previous 12 months, compared to the Nasdaq up 20% and the S&P 500 up approximately 15%. Two factors explain the outperformance: green economy stocks were disproportionately hit by the higher-interest-rate environment, so rate reductions have benefited them acutely.

Public assets under management in sustainability-focused funds stand at approximately $3.6 trillion, exceeding any previous high.

“In 2021 we set ourselves the goal to finance and facilitate two and a half trillion dollars to help drive delivery against the United Nations (UN) Sustainable Development Goals, and a trillion of that was very much aimed at helping with the transition. We have done over 900 billion dollars worth of financing against that goal on the green side, more than 310 billion,” he said.

J.P. Morgan subsequently set a goal to finance and facilitate $1.5 trillion to support industries driving the national security and resilience of the United States and its allies across five sectors: aerospace and defense, energy security and grid resilience, advanced manufacturing, frontier technology and pharmaceuticals. An initial $10 billion from the bank’s own balance sheet has been committed to equity investments in the space.

Laura Clarke, chief executive of ClientEarth, an environmental law organization, told the session that the legal landscape around corporate sustainability is undergoing a structural shift.

“What we are seeing from a legal and litigation perspective is moving from some of these climate and environmental responsibilities being more voluntary and discretionary to being a legal duty with real consequences if things do not go right,” Clarke said.

She identified three rising trends in climate litigation: greenwashing cases targeting spurious environmental claims; directors’ duties cases examining the personal liability of board members for climate risk; and polluter-pay cases holding corporates accountable for emissions, biodiversity loss and pollution affecting human health.

“Climate risk is a fact, and everyone needs to be planning for it, managing it, and finding the best way of doing that. There is a risk of being sued by a big oil company, but there is equally a risk of being held liable for not doing enough to look after the long-term commercial viability of your company,” she said.

“There are 2.1 million unplugged oil wells in the US leaking methane into the atmosphere, poisoning local communities. The reason is that big oil and gas owners make all the profit, sell the well to a shell company, and, just as that oil well needs to be plugged and made safe, that shell company goes bust. It is a systemic business model that evades the asset retirement obligation,” she said.

She cited the International Court of Justice (ICJ) advisory opinion on states' climate obligations, issued in 2024, as an authoritative statement now on the desks of judges and prosecutors worldwide.

She also referenced Luciano Lliuya v. RWE AG, in which a Peruvian farmer filed a claim for declaratory judgment and damages against RWE AG, Germany's largest electricity producer, establishing that a corporation can be held liable for its proportionate share of emissions-related damages. Victims of Typhoon Odette in the Philippines are currently pursuing a similar case against Shell.

Throughout the session, all four speakers pointed to the same conclusion: sustainability is no longer a question of intention but of execution, measurement, and accountability.

From AI-optimized energy systems and food security indices to green finance mandates and climate courts, the infrastructure for holding corporations to their environmental commitments is being assembled quickly.

Enterprise adoption of quantum computing has crossed a threshold few in the industry expected so soon. Large businesses are no longer evaluating whether artificial intelligence (AI) and quantum technologies belong in their strategy but are actively deploying them to tackle real operational problems.

A new study suggests the shift is already well underway. A survey of 1,003 senior business decision-makers across the United Kingdom found that 65% of respondents said their organization is already adopting or piloting quantum computing.

More strikingly, 41% of large UK enterprises estimated that quantum computing could unlock more than £100 million in value to their business in as little as one year.

“The era of enterprise quantum computing adoption has arrived. Companies are no longer asking if they should explore quantum, but how quickly they can implement it,” Murray Thom, vice president of quantum technology evangelism at D-Wave, told TechJournal.uk in an email interview.

“This study shows that UK businesses increasingly see quantum computing as a practical tool for tackling real business challenges, from supply chain optimization to manufacturing to AI,” he said.

Organizations actively engaging with quantum computing are estimated to have nearly twice the commercial value of those waiting for the technology to mature. They were also more likely to believe quantum is already delivering value today: 37%, compared with 16% of business leaders overall.

One in five UK business leaders surveyed said quantum computing is already a strategic boardroom priority. A further 34% described it as an emerging business tool, signaling that the technology has moved well beyond the domain of research scientists.

The data also suggests that early engagement sharpens an organization’s understanding of the technology’s commercial potential. Those already piloting quantum computing were significantly more confident about its near-term value than those still weighing whether to begin experimenting at all.

Surveyed UK business leaders identified several priority areas that would benefit most from improved optimization: workforce scheduling, cited by 90% of respondents, resource allocation at 89%, supply chain optimization at 88% and manufacturing processes at 82%.

These are precisely the kinds of computationally intensive problems that quantum annealing systems are designed to address, suggesting the UK enterprise market is already converging on the applications most likely to deliver near-term commercial value.

D-Wave is the world’s first commercial supplier of quantum computers and the only company to offer dual-platform quantum computing products and services, spanning both annealing and gate-model technologies. The company serves more than 100 organizations across commercial, government and research sectors through on-premises deployments and its Leap™ quantum cloud service.

AI pressures accelerate demand

The findings were published on June 3 in a report commissioned by D-Wave Quantum Inc. and conducted by Censuswide between May 7 and May 13, 2026. The report, titled “The Quantum Effect,” examines how organizations are moving from theoretical exploration of quantum computing into practical deployment.

One of the most striking findings is the extent to which AI disappointment is fueling interest in quantum. More than a third of UK business leaders said AI has delivered some return on investment (ROI), but results have fallen short of expectations. Nearly two-thirds expressed concern that existing energy infrastructure cannot support the continued expansion of AI.

Against that backdrop, 87% of respondents said quantum computing could help optimize AI-related processes and complex computational challenges.

Thom said quantum annealing systems are particularly well-suited to the large-scale optimization problems enterprises are already wrestling with, including workforce scheduling, resource allocation, and supply chain optimization.

“While it is possible to run simulated quantum annealing with a traditional computer, it is slower, less accurate and can be less energy-efficient than D-Wave’s real annealing quantum computers that organizations can use today,” He said. “This technology enables customers to go beyond modeling quantum effects in theory, and work with real, production-grade quantum systems built to tackle hard optimization problems in practice.”

Despite the momentum, significant barriers to quantum adoption remain. Cost concerns were the leading obstacle, cited by 46% of UK business leaders surveyed. A lack of internal expertise was flagged by 33% and limited awareness of the technology by 30%.

Education is the critical lever for closing that gap. It must be practical and business-focused rather than theoretical, giving leaders and technical teams just enough grounding to know where the technology can create value and how to act on that knowledge.

“The organizations making the greatest progress are those treating quantum as a practical tool rather than a future-gazing scientific endeavor,” Thom said. “Leaders and technical teams do not need to become quantum physicists. They just need enough understanding to identify where quantum can strategically deliver value, make informed investment decisions and translate early experimentation into long-term competitive advantage.”

Building quantum literacy across an organization requires connecting the boardroom and the engineering floor through a shared understanding of the technology’s potential. Businesses that develop this internal capability now will be better positioned to identify meaningful use cases and realize tangible outcomes as the market matures.

He said this is how organizations can begin to build a quantum workforce, moving teams from pilots to practical deployment and translating early experimentation into long-term competitive advantage.

Roadmap targets fault tolerance

D-Wave has published detailed hardware roadmaps for both of its quantum computing platforms. On the annealing side, the current Advantage2™ system operates at 4,400 qubits. The next-generation Advantage3™ system is expected to reach 20,000 qubits by 2029 and 100,000 qubits by 2031, significantly expanding the range of optimization problems the technology can address.

The gate-model roadmap spans two distinct eras. In the post-NISQ (noisy intermediate-scale quantum) era, D-Wave targets 17 physical qubits with a 2x error reduction factor in 2026, scaling to 181 physical qubits and a 2000x error reduction factor by 2028.

The fault-tolerant era follows, with 10 logical qubits targeted by 2030 and 100 logical qubits by 2032, enabling circuits with millions of gates.

Hybrid quantum-classical solvers are already available through the Leap™ service and are in active use today. The gate-model roadmap is built around D-Wave’s superconducting dual-rail architecture, with current physical error rates of 10⁻³ projected to be reduced by more than 2000x over the roadmap period.

“D-Wave is the first and only company to offer dual-platform quantum computing products and services, spanning both annealing and gate-model quantum computing technologies,” Thom said. “This dual approach is central to delivering a comprehensive quantum computing platform capable of addressing customers’ full range of computationally complex problems.”

A paper published in the Chinese Journal of Computers, titled “Quantum Annealing Public Key Cryptographic Attack Algorithm Based on D-Wave Advantage,” drew widespread attention in 2024 when a research team led by Wang Chao of Shanghai University reported using D-Wave’s systems to factor a 22-bit RSA integer, raising questions about quantum computing’s implications for encryption.

Thom was measured in his assessment. He said the research uses a hybrid algorithm to factor small numbers and is a continuation of older work rather than a fundamental breakthrough.

“Breaking modern encryption would require quantum processors many orders of magnitude larger than today’s scale,” Thom said. “There are post-quantum encryption protocols available. D-Wave does not specifically focus on cryptography, but our technology has been used to power intrusion detection and threat detection applications.”

At the regional level, the UK emerged as the clear leader in European quantum adoption across the three countries surveyed. Germany followed at 60% and Italy at 46%. Thom said cities that combine commercial momentum, ecosystem development and research strength will be best positioned to become regional hubs as adoption accelerates.

That advantage is anchored in national policy support and funding, a strong concentration of academic and research institutions, and a growing ecosystem of founders and startups focused on turning quantum potential into tangible commercial value.

Within the UK, London is emerging as a significant regional quantum hub, anchored by strong academic infrastructure, national policy support and a growing network of startups. Thom said the coming years will bring expanded annealing and gate-model hardware capabilities as D-Wave advances both platforms toward broader commercial deployment.

The United Kingdom has recorded the highest artificial intelligence (AI)-era cyber attack rate of any country in a new five-nation European survey, with 77% of British organizations reporting a cyber incident in the past year, 11 percentage points above the European average.

Yet the same research shows the UK ranking first in Europe for cybersecurity maturity, formal resilience frameworks and executive engagement, presenting what one senior industry figure has called a resilience paradox.

The findings, drawn from a ManageEngine survey of 1,514 information technology (IT) and business decision-makers across the UK, Spain, Germany, Italy and the Netherlands, suggest that visibility and maturity are amplifying rather than suppressing the number of attacks.

A highly digitized, interconnected infrastructure combined with rapid cloud adoption and hybrid working has elevated Britain's exposure. The challenge facing organizations is no longer primarily one of prevention.

“UK organizations are facing one of the most challenging cyberthreat environments in Europe, with attacks growing in both volume and sophistication. However, the findings also show that businesses are responding proactively by investing in resilience, strengthening governance and prioritizing preparedness for AI-driven threats,” said VimalRaj Sampathkumar, technical head for the UK and Ireland at ManageEngine.

Sampathkumar framed the situation as a structural shift in how British organizations think about risk. Cyber security in the UK is no longer primarily a prevention problem. Organizations are assuming attacks will happen, and the focus has moved to how quickly they can detect, respond and recover.

“What stands out amongst the findings is not a lack of awareness but rather the sheer operational strain organizations are under. UK businesses are investing, formalizing resilience practices, and increasing board engagement, but attackers are evolving just as quickly,” he said.

The gap between detection and recovery is one of the study’s most striking findings. While 94% of UK organizations detect incidents within 24 hours, fewer than half recover within 10 days. About 26% said recovery can extend beyond 10 days, with approximately 6% taking more than 20 days.

Nearly all UK organizations (96%) conducted a formal review after an attack, and 83% implemented at least some post-incident improvement. However, 13% resolved the incident without making any strategic changes, and only 37% adopted broader, long-term improvements.

ManageEngine is a division of Zoho Corporation and provides enterprise IT management software spanning IT service management, endpoint management, identity and access management, privilege account management and security information and event management (SIEM). The company has more than 60 integrated products.

Sampathkumar and senior technology evangelist Vivin K Sathyan presented the survey findings at a media briefing in London ahead of Infosecurity Europe 2026.

AI attacks accelerate

On June 2, Sampathkumar and senior technology evangelist Vivin K Sathyan at ManageEngine presented findings from the company's Owning Operational Resilience in 2026 report at a media briefing in London ahead of Infosecurity Europe 2026.

Also in attendance was Muiez Azeez, senior IT administrator at the University of Bedfordshire, who offered a customer perspective on the pressures facing British institutions.

About 43% of UK respondents predict that AI-powered attacks will be their biggest risk over the next 12 months, ahead of traditional threats such as ransomware, phishing and data breaches. AI and advanced threat preparedness will be the top spending commitment for 41% of UK organizations over the next 12 to 24 months.

“We are seeing an uptick in the number of AI-related attacks, so the defense also has to be on the same level, though we are still catching up,” Sathyan said.

He said identity-related threats are the primary category where AI is amplifying risk. Decoding user and entity behaviors has become significantly harder as AI enables attackers to blend in with normal network activity.

Sathyan said the approach ManageEngine takes to address this challenge is risk scoring, looking at dynamic parameters alongside static ones to add more context. He said organizations using multiple siloed tools can no longer decode behaviors the way they once could.

Sampathkumar said the nature of the threat had not fundamentally changed but its reach had.

“It is not a new type of attack that is emerging. The difference is the scale and speed at which attackers can now infiltrate a company’s network. Where an attacker could traditionally infiltrate 10 machines at a time, with AI the same person can now infiltrate 1,000 machines simultaneously,” he said.

The operational pressure bearing down on IT and security teams compounds the threat picture. About 46% of UK respondents cited a skills gap driven by rapidly evolving threats as their primary operational challenge, the highest of any country surveyed and 9 percentage points above the European average of 37%.

Separately, 60% said pressure on their teams had increased over the past 12 months, and one in four said workload had critically limited their ability to prevent or respond to incidents.

“The shift has now moved from alert fatigue or technical fatigue. It is not that companies do not know what to do in the event of an incident. It is that they are operationally stretched,” Sathyan said.

Team fatigue and burnout were cited as a key challenge by 29% of UK respondents, the highest rate in Europe and notably above the European average of 21%. Insufficient support from management was cited at the same level.

“When you have the right context in hand, you will be able to solve the problems a lot more quickly and recover faster. But when you do not have enough context to act, you do not know what you are really fighting,” he said.

Sathyan said fragmented tooling is the root cause of operational overload. When identity and access management, SIEM, endpoint management and vulnerability tools operate independently, teams lose the holistic view needed to respond effectively. Adding more tools is not the answer; contextual integration between existing ones is.

Zia LLM and agents

ManageEngine has built its own large language model (LLM) in response to customer concerns about sharing data with hyperscalers. The model, called Zia LLM, is designed around a right-sizing principle.

“Customers want to adopt AI but are very concerned about sharing data with hyperscalers,” Sampathkumar said.

He said customers sometimes want data processed internally within their own tools, with the AI built internally, and that this was one of the reasons the company built its own LLM.

Sathyan said the company has operated an AI research initiative for 12 to 13 years, moving from basic machine learning to its current LLM. He said enterprise customers can be served effectively with a few billion model parameters rather than trillions, avoiding unnecessary computational cost.

“When all the noise settles, only contextual AI stands out. The overall idea is not to introduce AI as a separate entity, but to integrate it contextually into existing offerings so that end users get more value out of the box,” he said.

ManageEngine also offers integrations with hyperscaler AI providers for customers who prefer that route. Its copilot product, Ask Zia, functions as a conversational assistant embedded within existing tools. Through its Agent Studio marketplace, customers, partners and developers can create and customize their own AI agents.

The proliferation of AI agents is creating a new governance problem that few organizations have yet addressed. When a single user deploys multiple agents, each agent inherits that user’s identity and permissions, making it nearly impossible to audit individual agent actions or enforce least-privilege access.

“When one physical user fires off multiple AI agents to do their job, you have multiple agentic identities mapped to the same user. We will have to have governance policies and auditing rails in place, and we are actually looking at that possibility,” Sathyan said.

He said a small number of large enterprises deploying agents at scale are beginning to adopt what he called AASB (agent-as-security-broker), a framework analogous to CASB (cloud access security broker). The key questions are whether agents operate with just-in-time access or carry bloated standing permissions inherited from the human user.

Azeez said the education sector faces a particular version of these pressures, shaped by chronic under-resourcing and an open, collaborative operating model.

“In the education sector, you are limited with staff and you have to manage within that. On top of that, you have too many collaborations, because that is how teaching works. There is so much load on the cybersecurity side to keep everything secure,” he said.

The University of Bedfordshire is actively evaluating a security operations center (SOC) solution and working to build AI security visibility into that function.

Azeez said higher education institutions vary widely in maturity, with some domains fully mapped while others remain exposed. The university is audited annually and its resilience posture has improved year by year.

“The most resilient organizations over the next few years will likely be those that combine strong governance, operational simplicity and trusted AI-driven security,” Sampathkumar said.

For the UK, the survey suggests that combination is within reach, but assembling it under growing operational strain and accelerating AI threats will define the next phase of Britain’s cyber resilience story.

The rapid spread of artificial intelligence (AI) tools across the UK workforce is generating a wave of unplanned costs and compliance failures that most organizations are struggling to contain.

Research published this week by Asana, a work management platform, found that 82% of UK information technology (IT) decision-makers have experienced unexpected or unplanned AI-related cost increases in the past 12 months. At the same time, 58% say their organization reports high AI adoption but limited measurable productivity gains.

“Most organizations are past asking whether their people will use AI. They already are. The challenge now is turning that into measurable business value, without losing the governance and visibility needed to manage risk,” said Christina Francis, head of UKI and Northern Europe at Asana.

The findings point to a deep accountability gap. More than six in 10 UK IT decision-makers say they are highly or fully accountable for AI-driven business outcomes, yet adoption is increasingly happening outside traditional governance processes. Those responsible for returns on AI investment are often unable to fully track or measure usage across their organizations.

A key driver of rising costs is the proliferation of shadow AI. One in four UK workers say they often use AI tools not formally approved by their organization, while 38% regularly use personal AI accounts for work tasks. Across the combined UK and US workforce, those figures rise to 32% and 45% respectively.

UK IT leaders estimate that almost a third of new AI tools introduced over the past year were adopted without formal review or approval. Rather than viewing shadow AI purely as a compliance issue, the research suggests it may signal that employees are seeking productivity gains their organizations have not yet enabled.

For business leaders, the challenge is not simply restricting AI use, but creating governed ways for employees to benefit from it.

“People want faster, smarter ways to work. The question is whether organizations can give them trusted, approved ways to do that, or whether they end up working around the tools entirely,” Francis said.

Governance failures and context

Saket Srivastava, chief information officer of Asana, and Francis met with UK-based journalists in London on June 2 to discuss AI governance and adoption, an event TechJournal.uk was invited to attend. The research, conducted by Censuswide among 1,002 IT decision-makers and 3,002 knowledge workers across the UK and United States, was published on June 12.

The governance shortfall is producing direct business harm. More than half of UK IT leaders (53%) say an AI tool or agent has taken an action in the past 12 months that resulted in financial, legal, reputational or compliance damage. Nearly half of UK workers (49%) say they do not know how their company uses their work, including emails, documents and tasks, to train or improve AI systems.

Execution failures are compounding the problem. Nearly half of UK IT leaders (46%) say AI initiatives often or always fail or stall because AI lacks complete organizational context. Some 37% of UK knowledge workers spend at least 30 minutes a day fixing or reworking AI outputs due to missing context, while 67% say they have to re-explain context to AI tools at least sometimes.

Workers are also struggling to feed AI the information it needs. Two in five UK employees say they need to check three or more tools to gather the context required to complete their work. The cumulative cost of that friction is significant.

Time lost to reworking flawed outputs, re-explaining background information and switching between disconnected systems adds up to a considerable daily productivity burden. Many organizations have yet to solve the challenge of providing AI with the structure and knowledge needed to deliver reliable outcomes at scale.

“As organizations move toward more dynamic, cross-system workflows, the need is shifting from coordination to Adaptive Work Orchestration, where humans and AI operate against shared context, with embedded governance and continuous visibility,” said Riana Barnard, industry analyst at Frost & Sullivan.

Francis said the organizations that get ahead will be those that combine visibility, governance and context, treating them not as competing priorities but as a single integrated challenge.

Asana’s agentic response

On June 4, Asana used its Work Innovation Summit in London to unveil what it calls an operating system for human-agent teams. The new product suite, called Agentic Work Management, is designed to enable organizations to run critical workflows in which humans and AI agents work from the same plan, with shared context and unified governance.

Asana refers to the approach as Adaptive Work Orchestration, an AI-powered operating system designed to bridge the gap between human teams and AI agents, enabling them to execute critical business plans together. The company also uses the term "Agentic Work Management" to refer to the same platform.

“For 18 years, Asana has solved one of the hardest problems in business: helping teams coordinate at scale across goals, decisions and handoffs. The foundation we built is precisely what the agentic era requires,” said Dan Rogers, chief executive of Asana.

Central to the launch is Asana Dash, an AI chief of staff for individual users that captures follow-ups from meetings, Slack threads and email and converts them into structured work items. AI Teammates, the platform’s agent layer, now feature a chat-based interface, a skills library for repeatable work, and integrations with tools including Gmail, Outlook, Slack, HubSpot, Figma and Canva.

Early results from enterprise deployments point to significant operational gains. FedEx, the logistics company, deployed AI Teammates across its marketing and sales operations, consolidating intake from more than 24 forms into a single intelligent workflow. Planning cycles fell from weeks to days, and more than 1,200 hours were reclaimed annually.

The deployment generated hundreds of thousands of dollars in operational savings. At the leadership level, AI Teammates provided full visibility into global initiatives, reclaiming over 300 hours that had previously been spent on manual alignment. In the sales function, intake review time dropped from 90 minutes to 30 minutes.

Three new applications are due to follow: Asana Service Management for IT and HR teams, Command by Asana for product and engineering workflows, and Asana Client Management for agencies. All three build on Asana’s acquisition of StackAI in May 2026, which extended its orchestration capabilities across enterprise systems including customer relationship management and enterprise resource planning platforms.

Agentic Work Management and AI Teammates are available immediately. Asana Dash and the three new applications will be released in phases over the coming months.

Britain is staking its economic future on artificial intelligence (AI) hardware, committing £1.1 billion ($1.4 billion) to build a sovereign chip industry, a national supercomputer, and a generation of engineers capable of competing with the world's largest technology powers.

The plan spans a £750 million national AI supercomputer, a £120 million innovation program to fund the design and testing of novel chips, and a new investment fund backed by up to £150 million from the British Business Bank. The government is also committing £80 million in total to skills.

“On AI, Britain has three options. We could stick our heads in the sand and hope for the best. Or you can remove the guardrails completely and ignore the consequences. Or you can take a third path, where we back the British businesses creating the jobs and technologies of the future, but never lose sight of who that progress must serve,” said Keir Starmer, Prime Minister of the United Kingdom.

“What matters is whether Britain is ready for it. Whether British innovators have the tools, the investment and the backing they need to lead it. And whether working people have the opportunity to benefit from it,” he said.

Starmer said the third path meant British tech companies starting, scaling and staying in Britain, with the rewards felt across the country. He said government must be active in technology policy, supporting risk-takers and providing the conditions for businesses to thrive, while also ensuring national sovereignty and giving working people security through change.

He said innovation was in Britain’s DNA. The country had invented the World Wide Web, pioneered the jet engine and powered the Industrial Revolution. It was the job of government not just to recognize the scale of that talent but to match it with the scale of its ambition.

He said ideas being born in Britain were not enough. They had to be able to grow there too, which was why the government was crowding in capital, betting on British businesses and creating confidence for investors worldwide.

The global AI chips market is forecast to reach one trillion dollars in the early 2030s. Securing just 5% of that market would generate £50 billion in revenue and create tens of thousands of highly paid jobs in the UK.

Of the £750 million earmarked for the national AI supercomputer, £400 million will go toward next-generation chips. £150 million of that will buy next-generation inference chips this summer, creating an immediate opportunity for British firms. A further £250 million will support more specialized chips as the most promising technologies mature.

Light over electricity

Starmer made his remarks at London Tech Week 2026, the annual technology conference held in London and organized by London & Partners, on June 8. A government press release published the same day set out the full details of the AI Hardware Plan, covering chip procurement, supercomputing infrastructure and skills investment.

Among the most striking announcements was the expansion of the Scaling Inference Lab, delivered by the Advanced Research and Invention Agency (ARIA) and CommonAI, into a national capability for testing and validating AI chip technologies. At least £20 million from the AI Hardware Innovation Program will fund the expansion.

The Lab is already delivering results. British AI company Oriole Networks, working with AMD through the Lab, will deploy the world’s first large-scale AI system that uses light rather than electrical signals to move data between chips. The photonic interconnect technology is designed to significantly boost the performance of UK data centers.

The shift from general-purpose chips to bespoke hardware plays directly to the UK’s strengths. British companies are already leading the next generation of AI hardware. Arm’s chip designs are used in everything from smartphones to AI data centers. Startups Fractile and Olix have raised more than £320 million between them.

A £120 million AI Hardware Innovation Program will provide companies with funding to design, develop, and test novel chips before they know whether the underlying technology will succeed commercially. The government described this as a way for Britain to ensure the next generation of world-leading chip companies is grown at home.

“Last year, I set us a target of upskilling seven and a half million workers with AI training by 2030. Today, I am pleased to report back that 1.7 million workers have already received that training,” Starmer said.

He said these figures came one year after he had set the target at the same stage. He added that while the government could not know exactly where AI would cause disruption, it knew how it would respond and whose side it would be on.

The skills investment covers a new £12 million Centre for Doctoral Training in Chip Design and a £20 million expansion of the TechFirst program to support 500 more Doctor of Philosophy (PhD) students. Undergraduate semiconductor bursaries will rise to 500 per year. Arm has joined as a strategic partner for TechFirst.

He said the government was rolling out AI tutors to 450,000 children on free school meals to close the attainment gap, and announced a new AI jobs tool to help people out of work find employment, create their curriculum vitae (CV) and return to the workforce.

Warrington and beyond

The story Starmer chose to open his speech was not a statistic or a policy announcement. It was a factory in Warrington.

“For centuries, Warrington was at the forefront of Britain’s soap-making industry. And until a few years ago, an Unilever factory was its epicenter. Generations of local people worked there. Families built their lives around it. But then it closed. And for many people in the town, that factory became a symbol of a community left behind,” he said.

That factory was now being transformed into a new AI data center, Starmer said, bringing skilled jobs and investment to a town that had watched opportunity pass it by. He said similar stories were unfolding in Lanarkshire, Liverpool and Leeds.

“Young people can look at that site and see not what their community used to be, but what it can become,” he said.

AI was already delivering faster diagnoses in the National Health Service (NHS), reducing court backlogs and speeding up planning decisions. He described these as real benefits that people across Britain are feeling right now.

UK startups had raised close to half of all European tech investment in 2025, a figure Starmer described as a profound achievement. Britain was the third-largest technology economy in the world. Each investment, he said, was an endorsement of British talent, British industry and the approach the country was taking.

He cited Reflection AI as expanding in Britain, creating 1,000 roles over three years because of the country’s talent pool. AMD was among the companies doubling down on the UK. The government had also created a Global Talent Taskforce to strengthen the routes bringing exceptional innovators to Britain.

Liz Kendall, Secretary of State for Science, Innovation and Technology, who announced the AI Hardware Plan at the same event, said the UK was already a global leader in chip design and believed it was a race Britain could win.

On children’s safety, Starmer said the pace of technological change could not be an excuse for harm. He called on tech companies to introduce device controls preventing children from sending and receiving sexually explicit images, citing an earlier incident involving the Grok platform.

“If they choose not to, then we will act. And we will change the law. When it comes to the safety of our children, standing by is not an option,” he said.

He said the question was not whether the AI revolution was coming, but whether Britain would shape it or be shaped by it. He said the government had made its choice: to take control, to be ambitious about what Britain could achieve, and to make AI work for every part of the country.