Robbing Peter to Pay Paulie, the US Deal in Plain Focus

We’re told the agreement involves big American tech and finance companies, like Microsoft and Blackstone, investing a lot of money into the UK. The UK government sees this as a big win, saying it will create thousands of jobs, especially in advanced technology like AI, and bring new infrastructure, like data centres, to the country.

The companies benefit by expanding their operations and getting a strong foothold in the UK market. The UK gets a boost to its economy and a reputation as a leader in tech. The money is spent on building things and hiring people, which helps the economy grow.

Will it? Time will tell… the number of jobs doesn’t feel great but the profits made by the big boys will be billions.

What it will mean is plenty of time, for those of us not gifted at code, to spend watching cat videos or creating them using the fancy new servers built in Taiwan!

The mega billion UK deal with USA – who will the money be spent with and where are their head quarters?

Recent “mega deals” between the UK and USA, announced during the visit of US President Donald Trump, involve a significant amount of investment from US companies into the UK. These deals are primarily focused on the technology, financial services, and defense sectors.

Here are some of the key companies involved, where the money will be spent in the UK, and their headquarters:

Technology and AI

• Microsoft: Microsoft has pledged a massive $30 billion (£22 billion) investment into the UK, their largest ever financial commitment in the country. The money will be spent on building AI and cloud infrastructure, including the UK’s largest supercomputer in Loughton, Essex, and expanding its operations. Microsoft’s headquarters are in Redmond, Washington, USA.
• Google: Google is investing £5 billion in the UK, which includes building a new data center in Waltham Cross, Hertfordshire. The investment also covers research and development, including work by its AI unit, Google DeepMind, which is headquartered in London, UK. Google’s parent company, Alphabet, is headquartered in Mountain View, California, USA.
• NVIDIA: The AI chipmaker is investing £11 billion, which will involve providing its powerful GPUs for various UK projects. NVIDIA is also partnering with the UK firm Nscale and OpenAI to establish a “Stargate UK” AI infrastructure project. NVIDIA’s headquarters are in Santa Clara, California, USA.
• OpenAI: As a partner in the “Stargate UK” project, OpenAI will be involved in deploying its technology for the new AI infrastructure. OpenAI’s headquarters are in San Francisco, California, USA.
• CoreWeave: The AI cloud computing company is investing £1.5 billion in UK AI data centers, including a partnership with the British firm DataVita in Scotland. CoreWeave’s headquarters are in Roseland, New Jersey, USA.
• Salesforce: Salesforce has committed an additional $2 billion (£1.4 billion) investment, with the goal of making its UK business an AI hub for Europe. The money will be spent on new R&D teams and infrastructure. Salesforce’s headquarters are in San Francisco, California, USA.

Financial Services and Investment

• BlackRock: The world’s largest asset manager is expected to invest £7 billion in the UK, including a £500 million investment into enterprise data centers and the opening of a new office in Edinburgh. BlackRock’s headquarters are in New York City, New York, USA.
• Blackstone: The investment firm has pledged a £100 billion investment in the UK over the next decade. Blackstone’s headquarters are in New York City, New York, USA.
• Bank of America: As part of the deal, Bank of America will create up to 1,000 new jobs in Belfast, its first-ever operation in Northern Ireland. Bank of America’s headquarters are in Charlotte, North Carolina, USA.
• Citi: Citi plans to invest £1.1 billion across its UK operations, including its presence in Belfast. Citi’s headquarters are in New York City, New York, USA.
• S&P Global: The financial information and analytics company is investing £4 million to create 200 permanent jobs in Manchester. S&P Global’s headquarters are in New York City, New York, USA.
• PayPal: PayPal is announcing a £150 million investment in product innovations and growth in the UK. PayPal’s headquarters are in San Jose, California, USA.

Defense

• Palantir: Palantir has signed a strategic partnership with the UK government to help the military develop AI technology. The company plans to make the UK its European defense headquarters and create up to 350 new high-skilled jobs. Palantir’s headquarters are in Denver, Colorado, USA.
• Boeing: A new contract with Boeing will see two of their 737 aircraft converted in Birmingham for the US Air Force, creating jobs in the UK for the first time in over fifty years. Boeing’s headquarters are in Arlington, Virginia, USA.
• Amentum: This company is making a £150 million investment, creating over 3,000 jobs across locations including Glasgow, Warrington, and the Midlands. Amentum’s headquarters are in Germantown, Maryland, USA.

How many jobs in the AI tech industry will be created in the UK as a direct result of this investment?

Based on the recent investment announcements made during the US President’s state visit, a significant number of jobs are expected to be created in the UK, particularly within the AI and technology sectors.

While a total number specifically for the AI tech industry is difficult to calculate with absolute precision as many announcements are for a wider range of tech-related roles, here’s a breakdown of the directly announced job creation figures from key companies:

• Google: Google’s £5 billion investment is projected to create 8,250 jobs annually at UK businesses. This includes roles in its new data center in Hertfordshire, as well as positions related to its AI research at Google DeepMind.
• Palantir: Palantir’s strategic partnership with the UK government will see an investment of up to £1.5 billion, and the creation of up to 350 new high-skilled jobs. This will include making the UK its European defense headquarters.
• AI Growth Zone: A new “AI Growth Zone” in the North East of England is set to create the potential for more than 5,000 jobs and attract billions in private investment. This is a dedicated area for AI development, data centers, and related infrastructure.
• NVIDIA: While the company’s announcement focuses on investing £2 billion to “catalyze the nation’s AI startup ecosystem,” the press releases explicitly state this will “create new companies and jobs” in areas like London, Oxford, Cambridge, and Manchester. The exact number of jobs is not specified, but it’s part of a broader goal to fuel the launch and growth of AI startups.

The UK government has stated that the overall package of US investment will create more than 7,600 high-quality jobs across various sectors, including AI, clean energy, and life sciences. Given the specific announcements, the AI sector is a major beneficiary of this job creation.

When they say billions invested in AI, do they mean in computer parts to build the technology and where will those computer parts be made?

When companies like Microsoft and NVIDIA announce billions in investment for AI, they are referring to a broad range of spending that goes well beyond just buying computer parts. A significant portion of the investment does, however, go into the hardware required to build and run AI models.

Here’s a breakdown of how the money is spent and where the components are manufactured:

1. Building AI “Factories” and Data Centers

The largest share of the investment goes into building new or expanding existing data centers, often referred to as “AI factories” or “supercomputer factories.” These are not just generic server farms; they are specialized facilities designed for the unique power and cooling requirements of AI workloads.

• Computer Parts: The core of these data centers are high-performance computer chips, primarily Graphics Processing Units (GPUs). Companies like NVIDIA are providing tens of thousands of their GPUs (e.g., Blackwell GPUs) to these UK projects. The investment also covers other essential hardware like networking equipment, storage systems, and specialized servers.
• Physical Infrastructure: A huge chunk of the capital is spent on the physical construction of the data centers themselves, including land, buildings, power infrastructure, and advanced cooling systems. For example, Microsoft’s investment of $30 billion (£22 billion) includes $15 billion for capital expenditure like equipment, land, and buildings.

2. The Manufacturing of AI Computer Parts

The computer parts needed for these AI projects, particularly the advanced chips, are not made in the UK. The global semiconductor manufacturing supply chain is complex and highly concentrated.

• Chip Design: While the UK has a significant role in chip design, with companies like ARM Holdings headquartered in Cambridge and Graphcore in Bristol, the actual fabrication of the chips happens elsewhere.
• Chip Fabrication (Foundries): The most advanced and powerful chips are almost exclusively manufactured by a handful of specialized companies called foundries. The vast majority of the world’s leading-edge semiconductors are made in:
  • Taiwan: By far the largest player is Taiwan Semiconductor Manufacturing Company (TSMC), which manufactures chips for companies like NVIDIA, Apple, and AMD.
  • South Korea: Samsung Foundry is another major global player in chip manufacturing.
  • USA: Companies like Intel Foundry Services and GlobalFoundries have operations in the United States.
• Packaging and Assembly: After fabrication, the chips are then assembled and packaged, a process that can also take place in various locations across Asia.

3. Other Areas of Investment

Beyond the hardware, the “billions” are also spent on:

• Research and Development (R&D): Companies like Google are investing in their UK-based AI research divisions, such as Google DeepMind in London. This money funds salaries for AI researchers, scientists, and engineers, as well as the operational costs of their R&D centers.
• Software and Services: The investment also covers the development of new AI software, platforms, and services that will run on the new UK-based infrastructure.
• Ecosystem Development: A portion of the funding is dedicated to supporting the broader AI ecosystem, including providing compute credits to UK startups and partnering with universities to foster talent and research. For example, NVIDIA’s £2 billion investment into the UK’s AI startup ecosystem is designed to provide capital to companies and fund research partnerships.

How many people qualify from university each year with the level of qualifications needed to do this specialist work?

The UK has a significant and growing number of graduates in computer science and AI-related fields, but there is a clear and persistent skills gap. Here’s a breakdown of the numbers and the context behind them:

Graduate Numbers

• Computing Graduates: According to data from the Higher Education Statistics Agency (HESA) for the 2022/23 academic year, there has been a significant increase in the number of postgraduate qualifications awarded in computing. Postgraduate taught qualifications in computing, for example, increased by 111% over two years, with a 139% rise in qualifications specifically in AI.
• Data Science and AI Courses: An initiative by the Office for Students (OfS) to address digital skills gaps funded postgraduate conversion courses in AI and data science. Between April 2020 and March 2023, over 7,600 students enrolled on these courses, with the program expected to deliver at least 6,000 new graduates.
• STEM Graduates: More broadly, the UK produces a high number of graduates in STEM (Science, Technology, Engineering, and Mathematics) disciplines. In 2020, the UK awarded 1,393 first university degrees per 100,000 people in STEM fields, which is a higher proportion than the US, India, and China. However, this is a broad category, and the specific numbers for highly specialized AI roles are much smaller.

The Skills Gap

Despite these graduate numbers, multiple reports highlight a significant skills shortage for specialist AI roles.

• Demand vs. Supply: A 2021 study cited by the UK Parliament estimated that the supply of data scientists from UK universities was unlikely to exceed 10,000 per year, while there were potentially at least 178,000 unfilled data specialist roles in the UK.
• Employer Feedback: A 2020 survey of public and private sector organizations found that 62% of respondents could not meet their goals because job applicants and existing staff lacked the necessary AI skills.
• Broader Digital Skills Gap: The problem is not limited to high-end AI roles. A 2024 report found that 18% of the UK labor force lacks the digital skills needed for the workplace, and 32% of skill-shortage vacancies were caused by a lack of digital skills.

Key Takeaways

The UK is making strides in increasing its pipeline of AI talent through new courses and a greater focus on postgraduate education. However, the demand for these specialist skills from both homegrown companies and major international investors like those in the “mega deal” far outstrips the current supply.

This skills gap is a primary reason why these companies are making significant investments in the UK. They are not just creating jobs; they are also committing to training programs and partnering with universities to help build the workforce they need to support their new infrastructure and R&D operations.

Will the current UK electricity supply be adequate to deal with the demands on power from the AI computers?

The short answer is: the UK’s current electricity supply is a significant concern for meeting the long-term demands of a rapidly expanding AI and data center industry. While the grid is not at immediate risk of failure, the scale and speed of the new investments are putting immense pressure on the system, requiring substantial upgrades and new strategies.

Here’s a more detailed breakdown of the challenges and the solutions being explored:

The Challenge: Soaring Demand

• Massive Power Consumption: AI data centers are incredibly power-hungry. A single AI training session can use as much electricity as a small town. When you multiply that by the scale of the announced investments—with companies like Microsoft, Google, and NVIDIA building “hyperscale” data centers—the demand on the grid is unprecedented.
• Forecasted Growth: The National Energy System Operator (NESO) has noted that data center energy demand could increase fourfold by 2030, and some reports suggest that by 2035, AI could account for a third of the growth in UK electricity demand. This is a dramatic reversal of the UK’s recent trend of falling electricity demand.
• Outdated Infrastructure: The UK’s current grid was not designed for this kind of concentrated, high-density demand. Planning and building new grid infrastructure (like substations and transmission lines) can take years, which is a major bottleneck for tech companies that want to build their facilities quickly. This has led to long waiting lists for new data center connections.

The Solutions: A New Approach

Both the government and the energy industry are well aware of this problem and are working on solutions. The approach is multi-pronged, focusing on both supply-side and demand-side solutions.

1. Modernizing the Grid

• Infrastructure Investment: The UK government and NESO are pushing for significant investment to upgrade the national grid. This includes building new transmission lines, substations, and other infrastructure to unlock capacity and reduce the connection queues.
• Special “AI Growth Zones”: The government is creating designated “AI Growth Zones” (such as in the North East of England) where planning permission and energy provision are fast-tracked. The goal is to create areas with dedicated and robust power supply to support these energy-intensive projects.

2. Demand-Side Management

• AI for the Grid: Ironically, AI itself is being seen as a crucial part of the solution. National Grid and companies like Emerald AI are conducting trials to demonstrate how AI can manage the power consumption of data centers. These systems would act as a “smart mediator” to dynamically adjust a data center’s energy usage in real-time.
• Flexibility and Load Shifting: The idea is that data centers could temporarily “dial down” their energy usage during periods of peak demand on the grid, like during extreme weather events. In return, they could get access to cheaper power when demand is low. This would make the grid more flexible and stable without the need for immediate, large-scale infrastructure overhauls.
• Smart Grid Technology: This extends beyond AI data centers to all parts of the energy system. The goal is to use digital technology and data to better forecast and manage energy supply and demand, from electric vehicle charging to smart home heating.

3. Renewable and Local Power Sources

• Decarbonization Challenge: The immense power demand from AI is a major concern for the UK’s net-zero targets. The government’s plan is that the new data centers will be powered by clean energy.
• Co-location with Renewables: The new data centers are being strategically located near existing or planned renewable energy sources, such as offshore wind farms. For example, a major data center project is planned near the site of a former coal-fired power station near Blyth in Northumberland, an area with significant offshore wind potential.
• Heat Recycling: There’s also a focus on making data centers more efficient. Projects are exploring how the immense waste heat generated by AI computers can be captured and recycled to heat local homes and businesses.

In conclusion, while the current grid is not ready to absorb the full, sudden demand from all the new AI investments, the problem is being actively addressed. The solution is not just about building more capacity, but also about building a smarter, more flexible grid that can work in partnership with the new technologies it is powering.

Do people who can write AI code tend to be self employed or do they prefer working for big corporations?

People who can write AI code, such as AI engineers, machine learning engineers, and data scientists, have a mix of employment options and can be either self-employed freelancers or work for large corporations. However, there are different trends and motivations for each path.

The Case for Working for Big Corporations

The overwhelming majority of highly skilled AI specialists tend to work for big corporations for several key reasons:

• Access to Resources: Big tech companies like Google, Microsoft, and NVIDIA have the immense financial capital, computational power, and vast datasets needed to train the most advanced AI models. A single AI training session can require the power of a small town, a level of resource a freelancer simply cannot access on their own.
• Large-Scale Projects: Corporate jobs offer the opportunity to work on projects that have a global impact, such as developing the next generation of large language models, autonomous vehicle systems, or medical diagnostic tools. This scale is often more appealing to those who want to push the boundaries of the field.
• Stability and Benefits: Corporate employment provides a predictable salary, health insurance, retirement plans, and other benefits that are not guaranteed in freelance work. In an industry where layoffs can happen, a stable, well-compensated job is a major draw.
• Team Collaboration and Mentorship: Working in a corporate environment provides access to other top-tier specialists, fostering a culture of collaboration, continuous learning, and mentorship. This is crucial in a field that is evolving at a breakneck pace.
• Security of Intellectual Property: For a big corporation, the work created by its AI specialists is the company’s intellectual property. This provides a clear framework and security for the company, and for the employee, it means they are working on a shared mission.

The Case for Self-Employment (Freelancing/Consulting)

While a smaller segment of the AI talent pool, self-employed specialists are a growing and important part of the ecosystem, especially for smaller businesses and startups.

• Flexibility and Autonomy: The main appeal of freelancing is the freedom to choose your own projects, set your own hours, and work remotely. This offers a level of work-life balance and creative control that is often not possible in a corporate setting.
• Higher Earning Potential (in some cases): While corporate salaries are high, freelancers can potentially earn more on an hourly or project basis, especially if they are highly specialized in a niche skill that is in high demand.
• Diversification of Experience: Freelancers work with multiple clients across different industries. This allows them to gain a broad range of experience and build a diverse portfolio, which can make them more resilient to industry-specific downturns.
• Agility and Innovation: Self-employed professionals can quickly adopt and experiment with new tools and technologies without the red tape and bureaucracy that can exist in large corporations. This allows them to stay at the forefront of innovation.

The Shifting Landscape

The rise of powerful and accessible AI tools is changing the dynamic for both freelancers and corporations. Generative AI tools are making it easier for a single person to take on work that once required a team. This could make self-employment a more viable and even safer option for some, as it diversifies their income streams and reduces dependence on a single employer.

In conclusion, while a large portion of AI talent gravitates toward the resources, scale, and stability of big corporations, a significant and growing number are choosing the flexibility and control that comes with being self-employed. The choice often comes down to personal preference for stability versus autonomy.

It does rather feel like the money is going around in a circle and the most unscrupulous will gain the most from this… is that fair or am i just being a bit paranoid?

This is a very insightful and fair question. Your feeling that the money is “going around in a circle” and that the “most unscrupulous will gain the most” is a common and well-argued critique of these kinds of deals. You’re not being paranoid; you are tapping into a major debate happening in economic and policy circles.

Here’s a breakdown of the arguments from both sides: the optimistic view (the government’s and the big tech companies’) and the more skeptical view (your “paranoid” one).

The Optimistic View: “A Rising Tide Lifts All Ships”

The official line from the UK and US governments and the tech companies is that this is a win-win. Their arguments are based on the idea of long-term economic growth and a positive feedback loop:

1. Investment Catalyses the Ecosystem: The argument is that this isn’t just a cash transaction. The US companies are building the foundational infrastructure (data centers, supercomputers) that the entire UK economy can then use. This creates an environment where British businesses, from startups to established firms, have access to cutting-edge technology they otherwise couldn’t afford.
2. Job Creation and Skills Development: The deals are explicitly tied to job creation, both in construction and in high-skilled AI roles. Furthermore, the companies are often committing to training programs and research partnerships with UK universities, helping to build the local talent pipeline.
3. National Security and “Sovereign AI”: The UK government has framed this as a way to create a “sovereign AI” capability. By having these facilities on British soil, the UK can develop and deploy AI in a way that is tailored to its own needs and security standards, without relying on infrastructure located in other countries.
4. Boost for Specific Regions: The investments are not just in London. The establishment of “AI Growth Zones” in places like the North East is a direct attempt to bring jobs and economic prosperity to regions that have been overlooked in previous tech booms.

The Skeptical View: “Zero-Sum Game”

Your concern about money “going in a circle” and benefitting the “unscrupulous” is at the heart of the critique. This perspective argues that the deal is structured to primarily serve the interests of the US tech giants, with the UK receiving a less favorable, dependent position.

1. “Sovereignty” vs. Dependency: This is the most significant point of contention. Critics argue that this deal does not create “sovereign” AI. Instead, it creates a dependency on US-owned infrastructure. The UK will be running its critical data and services on platforms owned and controlled by a handful of American companies. If these companies were to suddenly change their terms, raise prices, or even be influenced by US government policy, the UK would be vulnerable. As one critic put it, the UK risks becoming “an AI taker, not an AI maker.”
2. Crowding Out Homegrown Talent: By making it easier for US “hyperscalers” to dominate the market, the deal could stifle the growth of smaller, British-owned tech companies. UK startups may struggle to compete for talent and funding when they are up against the deep pockets of Google and Microsoft. The danger is that the UK becomes a “branch office” economy, where the real value and intellectual property are created and owned in the US.
3. The “Circular” Flow of Money: You’ve hit on a key point here. The investment is “circular” in a way. Microsoft invests billions to build a data center, but a huge portion of that money goes to purchasing chips from NVIDIA, which is also an American company. The end product is a service (cloud computing) that the UK will then pay to use, essentially sending money back to the US. This creates a kind of “digital colonialism” where the UK is paying to use infrastructure that it does not own.
4. Energy and Environmental Costs: The enormous energy demands of these data centers put a strain on the UK’s electricity grid and its net-zero goals. Critics argue that the UK public will ultimately bear the cost of upgrading the grid and dealing with the environmental impact, while the profits from the AI services go to private, foreign companies.

Conclusion

Your feeling is not paranoid at all. It is a legitimate and well-informed concern. The deal is a calculated gamble by the UK government. The optimistic view sees it as an essential step to get the country onto the AI playing field and a necessary concession to the global realities of the tech industry. The skeptical view sees it as a dangerous path toward digital dependency, where the UK is essentially paying to become a vassal state for American tech giants. The truth, as is often the case, likely lies somewhere in the middle, and the ultimate success of the deal will depend on how well the UK government manages the long-term risks and leverages the opportunities to build its own domestic capabilities.

Hope you found that interesting.

Thank you for reading.

Jon