Panattoni Seeks Approval for 1 Million sq ft Industrial and Logistics Scheme in Kent

Panattoni has submitted a hybrid planning application for Panattoni Park Maidstone, a proposed industrial and logistics development that would deliver more than 1 million sq ft of commercial space on a former manufacturing site in Kent.

The plans cover a 70-acre brownfield site at Lenham, located close to the A20 and M20 motorways, providing access to London, the M25 and the Channel ports. The proposed scheme would provide approximately 1.04 million sq ft of industrial and logistics accommodation, with the first phase comprising two speculative units of 100,000 sq ft and 150,000 sq ft. The second phase is seeking outline consent and would allow occupiers to develop built-to-suit facilities designed to meet specific operational requirements.

The application comes at a time when the South East industrial market continues to experience limited availability of modern warehouse space, particularly for larger units. Developers have increasingly focused on regenerating brownfield sites, reflecting planning policy objectives while responding to sustained occupier demand for well-connected logistics locations.

Panattoni said the proposals followed an extensive public consultation programme held earlier this year. More than 150 people attended a public exhibition in Lenham, with consultation results indicating that almost 85% of respondents supported the redevelopment of the site for employment use, while 98.5% favoured the redevelopment of brownfield land rather than greenfield sites.

The proposed development has been designed to achieve BREEAM Excellent certification and EPC A+ energy performance ratings. In addition to new industrial and logistics buildings, the scheme includes around 5 kilometres of new and upgraded roads, cycleways and pedestrian routes, together with landscaping and improved public access across the site.

The project follows Panattoni’s successful redevelopment of Panattoni Park Aylesford, another former industrial site in Kent that is now fully occupied. According to the developer, that scheme contributes an estimated £180 million annually to the local economy through business activity and employment.

If planning permission is granted, Panattoni Park Maidstone would add significant new industrial capacity to a region where development opportunities remain limited and demand for modern warehouse and manufacturing facilities continues to outpace supply. The combination of speculative units and bespoke development plots is intended to accommodate a broad range of occupiers across the logistics, manufacturing and distribution sectors.

Commercial agents Colliers, CBRE and Vail Williams have been appointed to market the development.

Warsaw Office Market Gains Momentum as Leasing Activity Rises and Prime Space Tightens

Warsaw’s office market strengthened during the second quarter of 2026, with leasing activity accelerating after a slower start to the year. Rising occupier demand, combined with a limited pipeline of new developments and falling vacancy rates, is tightening the availability of modern office space across the Polish capital, particularly in central business locations.

According to the latest research published by AXI IMMO, total gross office take-up reached approximately 420,000 sqm during the first half of 2026, representing a 38% increase compared with the same period of 2025. Net take-up totalled around 220,000 sqm, with the second quarter accounting for the strongest performance as several large transactions returned to the market after a relatively cautious opening to the year.

Among the largest agreements completed during the first six months were Frontex’s renewal of 21,500 sqm at Warsaw Spire B, Visa Europe’s lease of 17,300 sqm at The Bridge, and Poczta Polska’s renewal of 17,000 sqm at Domaniewska Office Hub. Business services, financial institutions and technology companies remained the most active occupier groups, reflecting continued demand from sectors that rely on high-quality office environments despite the widespread adoption of hybrid working.

The increase in leasing activity has coincided with historically low levels of new office construction. Developers delivered only around 50,000 sqm of new office space during the first half of the year, while approximately 130,000 sqm remained under construction, with more than 90% of current projects located in central Warsaw. The restrained development pipeline reflects continued caution among investors and developers following several years of changing workplace demand and higher construction costs.

The shortage of new supply has contributed to a further decline in vacancy rates. At the end of June, Warsaw’s overall office vacancy rate stood at 8.5%, down from 9.5% at the end of the first quarter and more than two percentage points lower than a year earlier. The tightest market conditions continue to be found in central locations, where vacancy has fallen to 4.8%, while the rapidly developing Rondo Daszyńskiego business district recorded availability of just 3.6%, highlighting the limited choice of premium office space.

The market also continues to demonstrate a growing divide between newer, highly specified office buildings and older properties. Tenants are increasingly prioritising offices that offer strong environmental performance, modern technical standards, flexible workplace layouts and excellent public transport access. Older buildings that no longer meet these expectations are increasingly being refurbished, repositioned or converted to alternative uses, including residential and hotel developments, further reducing available office stock.

Limited supply is also supporting rental growth across Warsaw’s prime office districts. Asking rents in central Warsaw generally range between €15 and €28 per sqm per month, while premium developments are achieving rents of between €25 and €32 per sqm per month. The highest-quality buildings continue to attract the strongest occupier interest as companies compete for a relatively small pool of available space.

Looking ahead, market conditions are expected to remain favourable for landlords. With relatively few office projects scheduled for completion over the next two years, analysts anticipate that the supply of modern, high-quality office space will remain constrained until a larger development cycle begins, which is not widely expected before 2028. If occupier demand remains stable, vacancy rates are likely to continue falling while competition for prime office buildings is expected to maintain upward pressure on rents in Warsaw’s most sought-after business locations.

The latest figures reinforce Warsaw’s position as one of Central and Eastern Europe’s most active office markets, although the balance between supply and demand is increasingly shifting in favour of owners of modern, well-located buildings as occupiers focus on quality, sustainability and long-term workplace strategies.

AI Pioneers Divide Over Jobs, Regulation and Control of Advanced Models at Ai4

Artificial intelligence could transform employment, education and economic productivity, but three of the field’s most prominent figures remain divided over whether society is adequately prepared for the disruption ahead.

Geoffrey Hinton, Fei-Fei Li and Andrew Ng discussed the future of AI during a keynote session at the Ai4 conference at The Venetian in Las Vegas on Wednesday, 5 August 2026.

The conversation was moderated by Yun-Hee Kim, Deputy Editor of Washington Post Intelligence.

Hinton shared the 2024 Nobel Prize in Physics with John Hopfield for foundational discoveries and inventions that enabled machine learning with artificial neural networks. Li led the creation of ImageNet and is now co-founder and chief executive of World Labs, which develops spatial-intelligence technology. Ng is the founder of DeepLearning.AI, chairman and co-founder of Coursera and the founding lead of the Google Brain project.

Their discussion exposed significant differences over employment, AI safety, regulation and the release of open-weight models.

Hinton warns that routine intellectual work is vulnerable

Hinton offered the strongest warning about employment.

He argued that AI systems are likely to become better than people at many call-centre and administrative duties involving routine intellectual work. They may eventually provide more accurate answers, operate continuously and process much larger volumes of enquiries at lower cost.

The central question, he said, is not whether AI will create new occupations, but whether it will create enough of them and whether people displaced from existing work will be able to perform the new roles.

Hinton compared the change with the mechanisation of manual labour. Excavators did not eliminate all construction employment, but they significantly reduced the number of people required to dig by hand. AI could have a similar effect on standardised information-processing work.

He illustrated the point with an example involving an employee who responds to complaints for a healthcare organisation. According to Hinton, preparing a response previously took around half an hour, while a chatbot can now generate a draft that the employee checks and adjusts in several minutes.

The example demonstrated how AI can increase an individual worker’s output without necessarily removing the entire occupation. However, where the total amount of work is fixed, higher productivity may ultimately reduce the number of employees required.

Hinton contrasted this with healthcare, where additional capacity may be absorbed by unmet demand. More productive doctors and nurses could potentially provide more care rather than simply reducing staffing levels.

Ng argues that narrow roles will become broader

Ng placed greater emphasis on the ability of workers to expand their responsibilities with the assistance of AI.

He said software engineering involves much more than writing individual sections of code. Engineers also define products, speak with users, design systems, test services and coordinate with other parts of a business.

AI may automate part of the coding process without replacing the complete role.

Ng said developers who previously specialised in front-end, back-end or mobile work are increasingly able to operate across a broader range of activities. AI tools can help them complete parts of the development process faster and take greater responsibility for a product from initial design through to deployment.

He suggested that a similar development could occur in marketing. Employees who previously coordinated campaigns may use AI to assist with preliminary content, research and design, allowing them to manage a wider part of the marketing cycle.

The challenge for education is therefore not merely to show employees how to use an AI application. Training must also help them identify the more valuable responsibilities they can assume once repetitive tasks become faster.

Ng argued that people still possess a substantial contextual advantage over AI. Employees understand the history of their companies, relationships with colleagues, customer behaviour and the practical reasons why an apparently sensible proposal may not work.

An AI system may produce several useful ideas alongside others that an experienced person immediately recognises as unrealistic. Human judgement is therefore still required to distinguish between plausible output and recommendations that do not fit the business context.

Li puts motivation at the centre of education

Li concentrated on the importance of personal agency in learning.

She argued that the most important element of education is not the curriculum or the technology, but the learner’s motivation and willingness to continue developing.

AI should consequently be presented as a tool that helps people become more capable, rather than as a system so intelligent that students have little reason to build their own knowledge.

This distinction is particularly important in schools. Teachers may be concerned that students will use AI to avoid the learning process, completing assignments without acquiring the underlying skills.

Li said teachers, parents and students have not always received a sufficiently clear explanation of how AI can support education while preserving personal responsibility.

Presenting AI mainly as a system that can outperform people may discourage students. A more constructive approach would demonstrate how it can explain difficult subjects, provide feedback and help learners explore their curiosity.

Hinton also described the potential of AI tutoring. He argued that an individual tutor can respond to a pupil’s interests more effectively than a teacher who must periodically deliver the same material to an entire classroom.

He suggested that AI tutors could eventually provide personalised support for routine learning, while teachers devote more time to projects, discussion, social development and interaction between students.

This was presented as a future possibility rather than evidence that AI tutors already outperform qualified teachers.

Regulation should direct development, Hinton says

The panel also divided over the appropriate role of government.

Li argued that policy should not be understood only as regulation or restriction. Governments can encourage responsible development through investment in universities, public research, education and nonprofit institutions.

Modern artificial intelligence grew partly from academic laboratories and openly published research. Li said continued public investment would help prevent future development from being determined exclusively by the commercial priorities of a small group of companies.

She favoured examining AI at the level of individual applications. Healthcare, transport, financial services and other regulated industries already have systems intended to protect the public. Those rules may need to be updated as AI changes the products and services offered within each sector.

Hinton supported a more interventionist approach.

He rejected the common comparison between regulation and the brakes on a vehicle. Regulation, he argued, should be regarded as the steering system: its purpose is not necessarily to halt AI development but to direct it towards outcomes that benefit society.

He referred to California Senate Bill 1047, legislation concerning safety requirements for certain advanced AI models. The bill passed the California legislature in 2024 but was vetoed by Governor Gavin Newsom.

Hinton argued during the panel that developers of powerful models should conduct safety testing and provide greater transparency about the results before release.

He also called for a system through which writers, artists and other creators could determine whether their work may be used to train AI models and negotiate payment for that use.

Technology companies pay for computing chips, electricity and other infrastructure, he said, and should not automatically treat professionally produced data as a free resource.

The licensing proposal was Hinton’s policy suggestion during the discussion rather than a description of an existing legal framework.

Open-weight models expose another disagreement

Ng strongly supported the continued availability of open AI models.

He argued that accessible models reduce the danger of a limited number of technology companies becoming gatekeepers for artificial intelligence. He compared the risk with the mobile-device market, where Apple and Google exercise considerable control over the applications that can reach users through their operating systems.

Ng said the AI market should accommodate successful proprietary platforms alongside systems that businesses, universities and researchers can download, examine and adapt.

Open alternatives may provide lower-cost access and allow organisations to retain more control over their technology and information.

Hinton distinguished between conventional open-source software and open-weight AI models.

Open-source software makes its code available for inspection, allowing developers to find errors and propose improvements. Open-weight releases provide access to the numerical parameters of a trained AI model.

Hinton warned that such models can be modified at much lower cost than would be required to train a comparable foundation model from the beginning. This could make it easier to adapt them for harmful purposes.

He nevertheless acknowledged that capable open-weight models are already widely available and that reversing the development may no longer be practical.

Li rejected the idea that all AI systems must be either completely open or completely closed.

She argued that science and software have historically operated across a spectrum. Research findings may be publicly accessible, while dangerous materials, sensitive applications and commercial products remain subject to different levels of control.

AI is likely to develop in a similar manner, with access determined by the intended use, capability and risk of each system.

Productivity does not guarantee shared prosperity

Despite their differences, the speakers agreed that AI is likely to produce substantial productivity gains.

The more difficult question is how those gains will be distributed.

A business that enables one employee to complete several times more work could use the additional capacity to improve its service. It could also reduce its workforce while retaining most of the financial benefit for shareholders.

Li warned that higher productivity does not automatically produce shared prosperity. Education, government policy and corporate decisions will determine whether workers and communities participate in the economic gains.

The panel therefore presented three distinct approaches to the employment question.

Hinton believes concern about job displacement is justified, particularly for occupations dominated by routine intellectual work. Ng expects AI to broaden many roles and increase the value of employees who combine technological tools with business knowledge. Li considers motivation, education and human agency essential to ensuring that people remain active participants in the transition.

Implications for commercial property

The panel did not focus directly on real estate, but the issues it raised have clear implications for the sector.

Automation may reduce future space requirements for call centres, shared-service operations and administrative departments. Businesses that can process the same workload with fewer employees may consolidate offices or reconsider expansion plans.

At the same time, AI is increasing demand for data centres, high-capacity electricity connections, cooling systems and other digital infrastructure.

Office design may also change as employers automate more repetitive individual tasks and place greater emphasis on collaboration, customer relationships and decision-making. This could favour flexible workplaces designed around teams rather than rows of administrative workstations.

Education and retraining may create further demand for specialised training facilities, university partnerships and technology campuses in locations with access to qualified labour.

These property effects remain dependent on the speed and scale of AI adoption. They should not be treated as conclusions reached by the Ai4 speakers, but as potential market consequences of the employment and infrastructure changes discussed during the session.

The debate made clear that there is no settled view of AI’s effect on the labour market. Companies are already deciding which duties to automate, governments are considering new safeguards, and schools are determining how generative systems should be used.

AI development is unlikely to pause while those decisions are made. The more immediate challenge is whether institutions can adapt quickly enough to direct the technology towards better services, stronger education and broader economic benefits.

AI-Generated Complaints Increase Pressure on Public Services and Businesses

fletcher@cijeurope.com
5 August 2026
Education, Healthcare, Public Sector, Tech

The rapid adoption of artificial intelligence is creating a new operational challenge for organisations across the United Kingdom, as AI-generated complaints become increasingly common in sectors including education, healthcare and public services.

According to legal experts, the growing use of generative AI tools is enabling individuals to produce lengthy, professionally written complaints with minimal effort. While this has improved access to formal complaint procedures for many people, organisations report that the increasing volume and complexity of AI-assisted submissions is placing significant pressure on staff responsible for investigating and responding to them.

Schools have been among the sectors most affected. Many report a rise in detailed complaints from parents that appear legally sophisticated but frequently contain inaccurate interpretations of legislation or policy. The issue has prompted Parentkind, working with the UK Department for Education, to encourage parents to keep complaints clear and relevant and to use AI tools with caution.

Public authorities are facing similar challenges. The Information Commissioner’s Office (ICO) has warned that AI-generated Freedom of Information requests are becoming increasingly common, with many containing ambiguous wording or incorrect references to legislation that require clarification before requests can be processed. This has increased the administrative burden on organisations already operating under statutory response deadlines.

One of the principal concerns is the amount of staff time required to process AI-generated submissions. Complaints that previously consisted of a brief letter can now extend over multiple pages, making it more difficult to identify the key issues and the outcome being sought. Organisations often spend considerable time separating relevant facts from unnecessary or repetitive content before beginning their investigation.

Legal specialists also highlight the tendency of large language models to generate inaccurate information, sometimes referred to as “hallucinations”. Complaints may include references to legislation, case law or regulations that do not exist, requiring organisations to verify legal citations that ultimately prove to be incorrect.

Another challenge is the increasingly confrontational tone often produced by AI tools. Complaints can contain broad allegations and strongly worded legal threats that make early, informal resolution more difficult. As a result, disputes may escalate unnecessarily before meaningful dialogue has taken place.

The imbalance in resources between complainants and organisations has also become more pronounced. AI enables individuals to generate multiple detailed submissions quickly, while organisations remain responsible for investigating each complaint thoroughly and producing balanced, evidence-based responses. This has created additional pressure for public bodies and businesses already operating with limited administrative resources.

Rather than responding to lengthy complaints point by point, legal experts recommend that organisations focus on the central issues requiring resolution and avoid being distracted by unnecessary detail. Existing complaints procedures generally provide sufficient flexibility to concentrate on material issues rather than every argument presented.

The use of AI to draft responses is also viewed cautiously. While automation may appear to offer efficiency gains, organisations are advised to consider the risks associated with confidentiality, factual accuracy and the impersonal nature of AI-generated correspondence. Maintaining human oversight is considered essential, particularly where sensitive or complex complaints are involved.

To manage the growing trend, organisations are encouraged to review complaints procedures, train staff to recognise AI-generated submissions, seek clarification where complaints are unclear and establish reasonable expectations regarding the length, format and frequency of correspondence. Introducing structured complaint templates or proportionate word limits may also help improve efficiency while ensuring complainants continue to receive fair consideration.

As AI tools become more widely available, legal experts expect AI-assisted complaints to become a permanent feature of organisational governance. The organisations most likely to manage this effectively will be those that strengthen their procedures, maintain proportionate responses and balance operational efficiency with fair treatment of complainants.

Source: CMS

India’s Digital Infrastructure Expands Beyond the Big Cities

fletcher@cijeurope.com
5 August 2026
CIJ - Spotlight, Indicators, Mixed-use, Tech

For years, Mumbai and Chennai have been at the heart of India’s data centre industry, attracting the majority of investment thanks to their strong connectivity, established business ecosystems and international network links. While these two cities continue to dominate the market, a new trend is beginning to reshape the country’s digital infrastructure. Developers and investors are increasingly looking towards smaller cities as demand for data processing continues to grow across India.

The rapid expansion of cloud computing, artificial intelligence, digital payments, online entertainment and connected devices has significantly increased the need for secure and reliable data storage. At the same time, businesses are seeking locations that offer room for expansion, competitive operating costs and access to renewable energy, creating new opportunities for cities outside the traditional metropolitan hubs.

Smaller Cities Gain Momentum

India’s next phase of data centre development is being driven by a broader geographical spread of digital services. As businesses and consumers in regional markets become more reliant on online platforms, there is growing demand for infrastructure that can process information closer to end users.

Cities such as Visakhapatnam, Kochi, Ahmedabad, Bhubaneswar, Jaipur and Vijayawada are increasingly being considered for future developments. Rather than replacing Mumbai or Chennai, these locations are expected to complement the country’s established data centre markets by supporting regional demand and strengthening network resilience.

This decentralised approach also reflects the growing need for edge computing, where data is processed nearer to the source rather than travelling long distances to central facilities. Lower latency is becoming increasingly important for applications including online gaming, financial transactions, video streaming and industrial automation.

Lower Development Costs Create Opportunities

One of the main attractions of emerging cities is the availability of land. Large development sites remain easier to secure than in India’s largest metropolitan areas, where industrial land is becoming increasingly scarce and expensive.

Lower land prices, together with potentially reduced construction and development costs, can improve the financial viability of regional projects. These advantages are particularly attractive for facilities designed to serve local markets or provide backup capacity rather than hosting the country’s largest hyperscale operations.

However, developers recognise that land costs represent only one element of a data centre investment. Reliable electricity, fibre-optic connectivity and long-term infrastructure remain the most critical factors in determining whether a location can support large-scale digital operations.

Renewable Energy Is Becoming a Competitive Advantage

Access to renewable electricity is increasingly influencing investment decisions. Many technology companies have committed to reducing carbon emissions and are seeking facilities that can operate using cleaner sources of energy.

States investing in solar and wind generation are becoming more attractive destinations for future data centres, particularly where renewable electricity can be secured through long-term agreements. Lower-carbon energy not only supports environmental objectives but can also improve long-term operating costs.

As sustainability becomes more important for global investors and cloud providers, access to renewable power is expected to play a greater role in site selection.

Government Policies Encourage Investment

Several state governments have introduced policies aimed at attracting digital infrastructure projects through investment incentives, simplified approval procedures and support for industrial development.

These initiatives are encouraging companies to consider locations outside the traditional metropolitan centres. Improved transport infrastructure, expanding fibre networks and stronger power grids are making a growing number of regional cities suitable for digital infrastructure investment.

Competition between states is likely to intensify as data centres become increasingly important contributors to local employment, technology investment and economic growth.

Diversifying Risk

Developers are also seeking to reduce operational risk by expanding into multiple locations. Concentrating large amounts of digital infrastructure in only a few metropolitan areas increases exposure to regional power failures, network disruptions and extreme weather events.

By distributing facilities across different parts of the country, operators can improve business continuity while creating more resilient digital networks capable of supporting India’s growing digital economy.

Regional facilities can also serve as disaster recovery sites, ensuring that critical services remain operational even if one location experiences disruption.

Challenges Remain

Despite growing interest, smaller cities still face important challenges before they can compete with India’s largest data centre markets.

Reliable electricity remains one of the biggest concerns. Data centres require continuous power, and even short interruptions can affect business operations. Although power infrastructure is improving across many regions, not every city can yet provide the level of reliability required by global technology companies.

Water availability is another consideration. Cooling systems used in many facilities consume significant amounts of water, creating environmental concerns in regions already facing water shortages. As a result, developers are investing in more efficient cooling technologies and exploring alternative water management solutions.

The availability of skilled engineers, technicians and specialised contractors also varies considerably between cities. Established technology hubs continue to offer deeper talent pools, making workforce development an important priority for emerging markets.

High-capacity telecommunications infrastructure is equally essential. Multiple fibre routes and resilient network connectivity are required to ensure uninterrupted digital services for customers across India.

A More Balanced Data Centre Network

India’s data centre market is entering a period of broader geographic expansion rather than fundamental relocation. Mumbai and Chennai are expected to remain the country’s principal digital infrastructure hubs because of their mature ecosystems, international connectivity and concentration of enterprise customers.

At the same time, the continued rise of cloud computing, artificial intelligence and digital services is creating opportunities for regional cities to play a much larger role in supporting the country’s digital economy.

Instead of competing directly with the established leaders, these emerging markets are likely to form part of a more balanced national network that improves resilience, supports regional economic development and brings digital infrastructure closer to businesses and consumers across India.

Source: © CIJ.World India Research & Analysis Team

Foreign Capital Finds New Momentum in Japan’s Evolving Investment Landscape

Japan is entering a new phase in its relationship with international capital. While the country has long been recognised as one of Asia’s largest sources of overseas investment, recent developments show that it is also becoming a more attractive destination for foreign investors. Record levels of inward foreign direct investment (FDI), combined with continued overseas expansion by Japanese companies, underline Japan’s increasingly important role in global capital flows.

Government initiatives aimed at strengthening economic growth, together with investment opportunities in technology, digital infrastructure and advanced manufacturing, are reshaping the country’s investment profile. At the same time, geopolitical tensions and greater scrutiny of strategic industries are influencing where and how foreign capital enters the market.

Record Inward Investment

According to the JETRO Invest Japan Report 2025, Japan’s inward FDI stock reached a record ¥53.3 trillion at the end of 2024, representing annual growth of 4.5%. Although annual investment inflows moderated compared with the previous year, the country continued to record a positive net inflow of foreign capital.

Greenfield investment was particularly strong, reaching a record US$31.6 billion. Much of this activity was concentrated in large-scale data centres, logistics facilities and automation projects, reflecting growing demand for artificial intelligence infrastructure, cloud computing and supply-chain modernisation. European and North American investors remained among the leading contributors to new investment projects, particularly through acquisitions and private equity transactions.

Japanese Capital Continues to Expand Overseas

Japan remains one of the world’s largest exporters of capital. Japanese corporations and institutional investors continue to pursue acquisitions and expansion opportunities abroad, particularly in the United States and ASEAN economies.

Corporate investment overseas is being driven by supply-chain diversification, access to new consumer markets and long-term growth opportunities. Japanese pension funds and institutional investors have also maintained significant allocations to international equities and other foreign assets as part of broader portfolio diversification strategies.

This dual position—as both a major recipient and provider of investment—gives Japan a distinctive role within the global financial system.

Shifting Sources of Foreign Investment

Although the United States remains Japan’s largest single source of inward investment by stock, investment patterns changed noticeably during 2024. U.S. companies recorded both record investment activity and record withdrawals, resulting in a net divestment of approximately ¥1.6 trillion. This reduced the gap between the United States and the United Kingdom, the second-largest foreign investor in Japan.

At the regional level, Asia overtook North America as the largest source of inward FDI for the first time in two years, highlighting Japan’s strengthening economic links with neighbouring markets and regional supply chains.

Meanwhile, investment activity has become increasingly concentrated in sectors viewed as strategically important, including digital infrastructure, semiconductors, logistics, renewable energy, advanced manufacturing and life sciences.

Balancing Openness with National Security

Japan continues to encourage overseas investment while introducing tighter safeguards around industries considered critical to national security.

In 2025, the government strengthened screening procedures for foreign investment in selected sectors by expanding the range of industries requiring prior notification under the Foreign Exchange and Foreign Trade Act. The revised rules place greater emphasis on protecting critical infrastructure, advanced technologies and sensitive information from potential security risks.

Rather than discouraging investment, the revised framework seeks to provide greater certainty by distinguishing between sectors that remain fully open to international capital and those requiring enhanced regulatory oversight.

Ambitious Long-Term Targets

Recognising the contribution foreign investment can make to innovation, productivity and regional development, the Japanese government has adopted more ambitious investment objectives.

Under the Program for Promotion of Foreign Direct Investment in Japan 2025, announced in June 2025, Japan increased its target for inward FDI stock from ¥100 trillion to ¥120 trillion by 2030, while also expressing its intention to reach ¥150 trillion during the early 2030s. The programme includes measures to improve the investment environment, support projects in strategic industries such as green transformation, digital transformation and life sciences, and encourage greater investment outside Japan’s largest metropolitan areas.

A Stable Destination for Global Investors

Despite slower economic growth than many emerging Asian markets, Japan continues to offer qualities that remain highly attractive to international investors. Strong legal institutions, advanced infrastructure, sophisticated financial markets, skilled human capital and world-leading industrial capabilities provide a stable foundation for long-term investment.

As companies diversify supply chains, expand digital infrastructure and invest in next-generation technologies, Japan is positioning itself as both a secure investment destination and an increasingly important partner in global innovation.

The combination of record foreign investment, continued overseas expansion by Japanese companies and supportive government policy suggests that cross-border capital will remain an important driver of Japan’s economic development throughout the remainder of the decade.

Source: © CIJ.World Japan Research & Analysis Team

EU AI Act Enforcement Creates a Three-Speed Regulatory Landscape Across Europe

The implementation of the European Union’s AI Act is progressing at markedly different speeds across Member States, creating an increasingly fragmented regulatory landscape that businesses must navigate as enforcement begins to take shape. According to Deloitte Legal’s latest National Implementation of the EU AI Act across Member States – July 2026 report, Europe has effectively divided into three groups based on their readiness to enforce the new legislation.

A small group of countries has already established operational enforcement frameworks, with designated supervisory authorities, single points of contact and AI-specific sanctions either fully in force or close to implementation. Finland, Italy, Hungary, Ireland, Malta, Slovenia and Cyprus are identified as the most advanced jurisdictions, providing businesses with greater regulatory certainty but also signalling that AI compliance requirements are likely to be enforced sooner in these markets.

A larger group of Member States has advanced legislation but has yet to complete the final stages of implementation. Countries including Germany, France, Poland, Spain, the Netherlands, Sweden, Czechia and Portugal have largely defined their institutional structures but are still finalising enforcement procedures, sanctions, supervisory authorities or regulatory sandboxes. As these measures become operational, companies should expect enforcement activity to accelerate rapidly.

The report also identifies a third group of countries that remain at an earlier stage of implementation. Austria, Belgium, Bulgaria, Croatia, Estonia, Romania and Slovakia continue to rely primarily on existing regulatory regimes while dedicated AI enforcement legislation and supervisory structures are still under development.

Despite these differences, almost all Member States have completed the designation of fundamental rights bodies required under Article 77 of the AI Act. However, the report highlights that the designation of market surveillance authorities and national coordination mechanisms under Article 70 remains the principal bottleneck delaying full implementation across much of the European Union.

The study notes that Member States are adopting different approaches to supervision rather than creating entirely new AI regulators. Communications authorities, digital infrastructure regulators, consumer protection agencies, cybersecurity bodies and data protection authorities are all emerging as national contact points, resulting in different regulatory entry points depending on the jurisdiction. This diversity means businesses operating across multiple EU markets will increasingly need country-specific compliance strategies rather than relying solely on a single European framework.

The report also examines the impact of the proposed Digital Omnibus package, which adjusts some implementation deadlines for high-risk AI systems and extends certain relief measures for smaller businesses. While these changes provide additional preparation time, Deloitte concludes that they do not alter the need for organisations to establish AI governance frameworks, comply with transparency obligations or prepare for the broader requirements of the AI Act.

Another significant finding is the uneven development of regulatory sandboxes across Europe. Denmark, Latvia, Lithuania and Spain already operate AI testing environments, while many other Member States have included sandboxes in legislation but have yet to launch them. Several countries remain in the planning stage, limiting opportunities for businesses to test innovative AI applications under regulatory supervision.

Looking ahead, the report recommends that companies prioritise compliance efforts in countries where enforcement structures are already operational, while building flexible AI governance programmes capable of adapting to different national requirements. It also encourages organisations to engage with regulatory sandboxes where available and to use the extended implementation timetable to strengthen AI inventories, governance processes, risk management and documentation rather than delaying compliance preparations. According to Deloitte, organisations that establish robust AI governance now will be better positioned as national enforcement frameworks continue to mature across the European Union.

Source: Deloitte

India’s Industrial Corridors Are Redrawing the Country’s Real Estate Map

India’s ambitious programme of industrial corridor development is transforming far more than the country’s manufacturing sector. Large-scale investments in transport infrastructure, logistics parks and planned industrial zones are reshaping where factories are built, how goods move across the country and where investors are directing capital.

For decades, India’s economic growth has been driven primarily by services, while manufacturing has faced challenges including fragmented infrastructure, high logistics costs and uneven industrial development. Today, both the central and state governments are seeking to change that picture through integrated industrial corridors designed to connect production centres with highways, rail freight networks, ports and airports.

The impact extends well beyond manufacturing. Commercial property, logistics, housing and land markets are all evolving as industrial activity expands into new regions.

Infrastructure Becomes the Foundation for Growth

India’s industrial strategy is increasingly centred on infrastructure rather than individual factory developments. New industrial corridors combine dedicated freight routes, expressways, modern logistics facilities and utility infrastructure to create locations where manufacturers can operate more efficiently.

This integrated approach reduces transportation times, improves supply chain reliability and lowers logistics costs, making new industrial locations more attractive to domestic and international businesses.

The National Industrial Corridor Development Programme (NICDP) now encompasses dozens of industrial projects across multiple economic corridors, reflecting its growing importance within India’s long-term manufacturing strategy.

Manufacturing Is Moving Towards Planned Industrial Hubs

Industrial corridors are encouraging companies to relocate from scattered manufacturing locations to purpose-built industrial zones offering reliable access to transport, electricity, water and digital infrastructure.

Rather than developing isolated factories, businesses are increasingly choosing integrated industrial parks where suppliers, manufacturers, logistics providers and supporting services operate within the same ecosystem.

This clustering effect improves efficiency, encourages collaboration across supply chains and makes regions more attractive to future investment.

As additional industrial nodes become operational, new manufacturing centres are emerging beyond India’s traditional industrial regions, helping diversify economic activity across the country.

Property Markets Benefit from Industrial Expansion

One of the most immediate effects of corridor development is rising demand for land surrounding new industrial zones.

As manufacturers, logistics companies and commercial developers compete for strategically located sites, land values often increase well before industrial projects reach full capacity.

The impact extends beyond industrial land. Residential developments, retail centres, offices, hotels and supporting services frequently follow as employment opportunities attract new workers and businesses.

This pattern has been observed in established industrial regions as well as emerging markets where infrastructure investment is creating entirely new commercial districts.

Warehousing Demand Continues to Rise

The expansion of manufacturing is also strengthening India’s logistics property market.

Modern factories require efficient distribution networks capable of supporting both domestic consumption and exports. As a result, demand for Grade A warehouses, distribution centres and logistics parks continues to grow alongside industrial development.

Manufacturing has become one of the largest drivers of warehouse leasing activity, reflecting the increasing integration of production facilities with modern supply chains.

Developers are responding by expanding logistics parks near industrial corridors, freight terminals and major transport routes to reduce delivery times and improve operational efficiency.

Institutional Investors See Long-Term Potential

Industrial real estate has become an increasingly attractive asset class for institutional investors.

Pension funds, sovereign wealth funds, private equity firms and global real estate investors are allocating more capital towards logistics parks, industrial developments and income-producing manufacturing assets.

The appeal lies in the sector’s long-term growth prospects, supported by expanding manufacturing activity, rising domestic consumption and government policies aimed at strengthening India’s position as a global production base.

As industrial property becomes more standardised and professionally managed, investment vehicles such as Real Estate Investment Trusts (REITs) and infrastructure funds may play a larger role in financing future developments.

Corridor Development Is Creating New Urban Centres

The influence of industrial corridors extends well beyond factory construction.

As employment opportunities increase, demand also grows for housing, schools, healthcare facilities, retail centres and commercial services. Many industrial locations are gradually evolving into mixed-use urban centres where people can both work and live.

This creates opportunities for residential developers alongside industrial and logistics investors, contributing to broader regional economic development.

In several emerging markets, infrastructure investment is acting as the catalyst for entirely new urban growth corridors that combine manufacturing, logistics and residential communities.

Challenges Remain

Despite strong momentum, industrial corridor development continues to face several challenges.

Large infrastructure projects require substantial investment and long implementation periods, while land acquisition and environmental approvals can delay construction. Coordination between central and state governments also remains essential to ensure transport links, utilities and industrial zones are delivered simultaneously.

In addition, sustained success will depend on developing skilled workforces, improving urban services and ensuring that supporting infrastructure keeps pace with industrial expansion.

Maintaining environmental sustainability and balancing industrial growth with responsible land use will also become increasingly important as new corridors develop.

Building India’s Next Industrial Economy

Industrial corridors are becoming one of the defining features of India’s long-term economic strategy.

By combining modern transport infrastructure with planned industrial development, the country is creating new locations capable of supporting manufacturing, logistics and export growth on a much larger scale.

For the real estate sector, these projects represent more than infrastructure investment. They are reshaping land values, driving warehouse development, attracting institutional capital and creating entirely new property markets.

As additional corridors become operational over the coming decade, they are expected to play an increasingly important role in determining where businesses invest, where people live and how India’s commercial real estate landscape continues to evolve.

Source: © CIJ.World India Research & Analysis Team

AI4 2026: Energy, Chips and Data Centres Become the New Foundations of Economic Growth

The next phase of artificial intelligence will depend as much on power stations, semiconductor factories and data centres as it does on software, according to technology leaders speaking at AI4 2026.

During a discussion on the future of computing infrastructure, former Intel CEO Pat Gelsinger, now a general partner at venture capital firm Playground Global, joined Sachin Katti, Head of Compute at OpenAI, to examine the physical systems required to support increasingly capable AI models.

The discussion reflected one of the central themes emerging from the conference: artificial intelligence may appear to users as software, but its continued expansion depends on an extensive industrial network of chips, electricity, cooling systems, communications infrastructure and highly specialised manufacturing.

AI4 lists Gelsinger as a general partner at Playground Global and Katti as OpenAI’s Head of Compute. (⁠Ai4 2026)

Chips become the fuel of the AI economy

Gelsinger described semiconductors as the underlying fuel of an economy increasingly driven by AI-generated tokens.

Every model training exercise, business application and user request ultimately runs on physical processors. As the use of AI grows, so does demand for computing capacity throughout the infrastructure chain.

However, supplying that capacity is considerably more complicated than simply producing additional chips.

Advanced semiconductor factories are among the most expensive and technically complex industrial facilities ever developed. They take years to plan and build, while leading-edge manufacturing depends on a relatively small group of companies capable of producing advanced processors, memory systems and chipmaking equipment.

This concentration has allowed semiconductor manufacturers and equipment suppliers to capture a significant share of the economic value created by the current AI investment cycle.

Gelsinger argued that companies operating at the silicon level have so far been among the clearest financial beneficiaries of the AI expansion.

Compute is not yet a standard commodity

Although computing power is often compared with oil, the speakers cautioned that AI compute has not yet become a fully interchangeable commodity.

Different chips offer different performance, energy use, memory capacity and suitability for particular workloads. Infrastructure designed for training a large model may not be equally effective for operating that model and answering millions of user requests.

Katti explained that demand remains strong because greater computing capacity continues to support advances in model performance.

His comments reflected the principle commonly associated with AI researcher Rich Sutton’s The Bitter Lesson: over time, approaches that make effective use of increasing computational power have frequently outperformed systems based mainly on handcrafted human knowledge.

For OpenAI, this makes access to computing infrastructure a strategic requirement rather than an ordinary purchasing decision.

OpenAI has described compute as the essential input that allows it to train more capable models, support growing usage, improve reliability and reduce the long-term cost of providing AI services. (⁠OpenAI)

Existing GPUs remain too inefficient

Despite the rapid development of AI hardware, Gelsinger said the present generation of graphical processing units remains highly inefficient in its use of electricity and memory.

GPUs became the dominant hardware for AI because they were the best available option for processing many calculations simultaneously. However, they were not originally created specifically for the enormous training and inference demands now being placed upon them.

The next generation of AI chips will therefore need to deliver significantly more useful work from each unit of power.

Memory is one of the most important constraints. AI processors frequently spend time waiting for information to move between memory and the computing elements of the chip.

Bringing more memory closer to processors and increasing memory bandwidth could substantially improve performance and lower the cost of inference—the process through which a trained AI model produces answers, images or other outputs.

Gelsinger suggested that new processor architectures could eventually transform inference economics by very large margins.

Energy capacity may limit AI expansion

The discussion repeatedly returned to electricity as the ultimate constraint on AI growth.

A company can order processors and construct data centres, but the equipment cannot operate without sufficient and reliable power. This means that available energy capacity may determine how quickly countries can expand their AI industries.

“In a digital AI economy, economic capacity equals energy capacity,” Gelsinger told the audience.

He argued that the United States and other Western economies need to increase electricity generation while improving transmission systems so power can reach new data-centre locations more quickly.

The challenge is not simply the total amount of electricity produced. Developers must secure grid connections, transmission capacity, land, planning permission and local community support before a major AI facility can begin operating.

OpenAI has similarly said that large-scale AI infrastructure depends on coordination between utilities, energy companies, semiconductor manufacturers, cloud providers, construction businesses, investors and public authorities. (⁠OpenAI)

Data-centre development needs to become faster

Katti said the entire infrastructure stack must become easier and quicker to build.

At present, companies can face long delays when moving from chip design to production and from identifying a data-centre site to bringing the facility online. Power availability, construction capacity and equipment supply can each become a bottleneck.

One possible response is to develop more standardised and repeatable data-centre designs. Greater use of modular buildings, pre-engineered systems and on-site energy generation could shorten construction programmes and reduce dependence on lengthy grid expansion.

However, the industry also faces a timing problem.

AI models and algorithms can evolve more quickly than semiconductor development. A specialised processor designed for today’s dominant AI architecture may be less suitable by the time it reaches commercial production several years later.

This creates a risk that companies could invest heavily in hardware optimised for workloads that have already changed.

Infrastructure investment measured against world GDP

Gelsinger predicted that expenditure on AI infrastructure could eventually be measured as a proportion of global gross domestic product.

Trillions of dollars are already being directed towards semiconductor capacity, data centres, electricity generation and network infrastructure. Yet the present economics remain difficult because AI systems require enormous investment before operators can recover their costs through commercial services.

The answer, he argued, cannot be investment alone.

The industry must achieve dramatic improvements in processor efficiency, energy consumption, memory performance, construction speed and operating costs. Incremental gains will be insufficient if AI demand continues growing at its current rate.

The panel identified four interconnected priorities: expanding infrastructure, improving computing efficiency, increasing energy availability and creating more sustainable economics.

Silicon companies currently capture the greatest value

The speakers also considered which parts of the AI supply chain are currently receiving the greatest financial benefit.

While model developers and enterprise software providers expect to build valuable businesses, semiconductor designers, advanced manufacturers and chip-equipment suppliers have so far occupied particularly strong positions.

Companies such as TSMC and ASML have built expertise and supplier networks over several decades, creating capabilities that cannot be reproduced quickly simply by providing additional capital.

This presents a strategic problem for governments seeking to establish or rebuild domestic semiconductor industries. Funding new factories is important, but successful production also depends on technical knowledge, specialist workers, equipment suppliers, materials providers and long-term customer demand.

The development of a competitive semiconductor ecosystem is therefore likely to take considerably longer than the construction of an individual manufacturing plant.

AI becomes an industrial and property challenge

The panel’s conclusions extend beyond the technology sector.

As computing requirements expand, AI is becoming a major factor in energy policy, industrial development and commercial property markets. Data-centre operators will require increasingly large sites with access to electricity, fibre networks, water or alternative cooling systems and skilled labour.

Locations capable of offering these conditions could attract substantial investment. Regions without sufficient power capacity or efficient planning systems may struggle to participate fully in the AI economy.

The debate is therefore shifting from whether AI demand will grow to whether physical infrastructure can be delivered quickly and economically enough to support it.

The future of artificial intelligence will not be decided by algorithms alone. It will also depend on whether the world can produce enough chips, generate enough electricity and construct enough specialised property to keep the machines running.

AI4 2026: Artificial Intelligence Moves Beyond Drug Discovery to Redefine the Future of Medicine

Artificial intelligence is no longer viewed as simply another research tool in pharmaceutical development. At AI4 2026, a panel featuring leaders from three of the industry’s most advanced AI biotechnology companies argued that AI is becoming an integral part of the entire medicine development process, from identifying biological targets to designing new therapies and, eventually, transforming how healthcare approaches ageing itself.

Moderated by Alice Park, senior health and medicine journalist at TIME, the discussion brought together Alex Zhavoronkov, Founder and CEO of Insilico Medicine, Eric Nguyen, Co-founder and CEO of Radical Numerics, and Gabor Gradinaru, Co-founder and Chief Technology Officer of Generate Biomedicines.

Opening the session, Park noted that medicine has progressed through defining technological breakthroughs—from antibiotics and genome sequencing to modern immunotherapies—and suggested that artificial intelligence now represents the next major shift in biomedical science.

AI addresses both biology and the business of drug development

Rather than focusing solely on AI’s ability to generate molecules, the panel emphasised that medicine remains one of the world’s most complex engineering challenges.

Gradinaru explained that AI must solve two separate problems simultaneously. The first is understanding biology itself, where human physiology remains only partially understood. The second is improving the highly complex process required to bring a medicine to market, involving manufacturing, regulation, clinical development and commercialisation.

He argued that AI’s greatest long-term value will come from connecting these fragmented stages into a more integrated development process rather than simply accelerating isolated scientific tasks.

Programming biology instead of observing it

Eric Nguyen described Radical Numerics’ research as an attempt to make DNA “programmable.”

Rather than viewing biology as something scientists simply observe, Nguyen explained that new foundation AI models can now read, write and design DNA sequences. This opens opportunities not only to create new medicines but eventually to engineer biological systems with greater precision.

However, he stressed that today’s achievements represent only the beginning.

Current AI systems can assist with designing proteins, RNA and DNA, but understanding how these therapies behave inside the enormously complex environment of the human body remains one of the industry’s biggest scientific challenges.

Drug discovery measured in thousands of decisions

Alex Zhavoronkov presented perhaps the most mature commercial example of AI-assisted pharmaceutical development.

He explained that Insilico Medicine currently has 31 drug candidates that have reached the stage immediately before or within human clinical development, including one programme in Phase III clinical trials.

According to Zhavoronkov, developing a single drug candidate involves roughly 1,200 individual scientific and technical steps, each representing an opportunity where artificial intelligence can reduce time, improve decision-making and increase the probability of success.

While AI has significantly shortened the pre-clinical discovery process, he acknowledged that once therapies enter regulated human clinical trials, progress must continue at the pace required to ensure patient safety.

Ageing emerges as the industry’s largest therapeutic target

Rather than concentrating on individual diseases, Zhavoronkov argued that ageing itself represents the largest medical challenge facing humanity.

Unlike cancer, diabetes or Alzheimer’s disease, ageing affects every person, making it a universal biological process rather than a single disease.

His company’s strategy is to identify biological mechanisms involved both in ageing and specific diseases, developing medicines that may eventually treat illness while also improving healthy longevity.

He suggested that even extending healthy human life expectancy by a single year across the global population would represent one of the greatest public health achievements in history.

Generative biology aims to standardise medicine development

The discussion also introduced the concept of generative biology, where AI helps design biological systems using reusable components rather than treating every new medicine as a completely unique scientific project.

Gradinaru compared the approach to engineering disciplines, where complex products are assembled from proven, modular building blocks instead of being reinvented each time.

Machine learning could eventually improve prediction of toxicity, manufacturing performance and unwanted side effects while creating repeatable development frameworks that become more accurate as additional clinical data becomes available.

DNA generation raises new ethical responsibilities

The conversation also turned to one of the most sensitive issues surrounding AI in biotechnology.

Nguyen discussed how recent AI foundation models have demonstrated the ability to generate functional DNA sequences, including harmless bacteriophages created entirely through AI-guided design.

While such advances offer powerful new tools for understanding disease and developing therapies, they also introduce concerns over dual-use technologies that could potentially be misused.

He argued that future AI biotechnology companies must develop offensive and defensive capabilities simultaneously, ensuring that systems designed to create biological innovations are matched with equally advanced safeguards capable of detecting and preventing misuse.

The panel compared today’s situation with the recombinant DNA debates of the 1970s, when scientists voluntarily introduced research guidelines before governments established formal regulatory frameworks.

Regulation remains an essential safeguard

Despite enthusiasm for accelerating research, the speakers agreed that human clinical trials remain indispensable.

Although AI can dramatically narrow the number of candidate molecules requiring laboratory testing, real-world biological validation continues to be the ultimate measure of whether new therapies are safe and effective.

Laboratory experiments, animal studies and carefully regulated human trials remain essential for confirming AI-generated predictions before new medicines reach patients.

AI becomes a partner rather than a replacement

The discussion concluded with broad agreement that artificial intelligence is evolving beyond being a productivity tool.

Instead, AI is becoming an increasingly important scientific partner capable of helping researchers understand biology, design therapies and improve the efficiency of pharmaceutical development.

Rather than replacing scientists, the panellists suggested AI will enable researchers to explore biological questions that were previously too complex, time-consuming or expensive to investigate, potentially shortening the path from scientific discovery to treatments that improve both health and longevity.

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