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WHO’s Historic Agreement: A Major Step Towards Global Pandemic Preparedness

In a groundbreaking move, members of the World Health Organization (WHO) have reached a historic, legally binding agreement aimed at preparing the world for future pandemics. This pact, designed to address the lessons learned from the COVID-19 crisis, sets the stage for a more equitable global response to health emergencies, particularly in the distribution of essential drugs, vaccines, and medical technologies.

The agreement marks a significant milestone in global health governance, especially at a time when multilateral institutions like the WHO are facing considerable financial strain. The United States, which was once the WHO’s largest financial contributor, withdrew from negotiations after President Donald Trump initiated the U.S.’s departure from the organization. Despite this setback, the deal underscores a strong commitment from member states to work together on global health security, with or without U.S. involvement. “This is a historic moment,” said Nina Schwalbe, founder of global health think tank Spark Street Advisors. “It demonstrates that countries are committed to multilateralism and to collective action.”

This agreement, the second of its kind in WHO’s 75-year history (the first being a tobacco control treaty in 2003), focuses on structural inequalities in how pandemic-related health tools are developed and distributed. Article nine of the deal ensures that future pandemic-related drugs, therapeutics, and vaccines will be made globally accessible. It also gives the WHO stronger oversight over medical supply chains and paves the way for local production of vaccines during health crises.

A key challenge in the negotiations was the issue of technology transfer—sharing the knowledge and manufacturing capabilities necessary for lower-income countries to produce their vaccines and treatments. To address this, the agreement mandates that manufacturers allocate at least 20% of their real-time production to the WHO during a pandemic, with a minimum of 10% designated for donation and the rest priced affordably for developing nations.

The deal is not yet finalized, as it must be adopted at the WHO Assembly in May, and some details, such as the annex on Pathogen Access and Benefit Sharing, still require further negotiation. However, once ratified, the agreement will bolster global preparedness, enabling quicker responses to future pandemics and more equitable access to life-saving resources.

As health experts emphasize, the global community must invest in preparedness now to avoid the costly toll of another pandemic. “We can’t afford another pandemic, but we can afford to prevent one,” said Helen Clark, co-chair of The Independent Panel for Pandemic Preparedness. This agreement represents a critical step toward ensuring that the world is better equipped to face future health crises with solidarity, transparency, and a commitment to equity.

AI Spending Is Complicating The Fed’s Fight Against Inflation

Silicon Valley leaders have long argued that artificial intelligence will make technology and services dramatically cheaper. OpenAI CEO Sam Altman has described a future where intelligence becomes extremely inexpensive, while Tesla and SpaceX CEO Elon Musk has predicted that AI and robotics will create greater abundance and drive down costs.

So far, those benefits have yet to materialise at scale. AI adoption remains relatively slow, while the enormous investment needed for data centres and AI infrastructure is putting pressure on electricity prices, supply chains and other costs. For the Federal Reserve, this creates a difficult balancing act: AI could eventually boost productivity and reduce inflation, but its current buildout is contributing to higher prices.

OpenAI chief economist Ronnie Chatterji said AI needs to be adopted by organisations and generate measurable value before its broader economic impact becomes visible in productivity statistics.

AI Adoption Remains Uneven

Capital spending on AI infrastructure in the U.S. is expected to reach $581 billion this year, according to Goldman Sachs Research, with global investment potentially reaching $1 trillion.

Despite the scale of spending, adoption remains far from universal. A May survey by the U.S. Census Bureau found that 17% to 20% of U.S. businesses reported using AI, with adoption significantly higher among large companies.

Companies that have implemented AI at scale also highlight the challenges. Julie Averill, former CIO of Lululemon, said successful deployment requires changes in employee behaviour and trust in the technology. OpenAI has observed a similar divide: its most advanced business users deploy AI at around eight times the rate of average companies.

Why Productivity Gains May Take Time

Economists point to the limits of automation. AI can perform individual tasks effectively, but many jobs combine tasks that are difficult to automate.

Stanford professor Charles Jones refers to these as “weak links”. Radiology, for example, involves interpreting scans but also communicating with patients and working with colleagues. AI can automate part of the job without eliminating the profession itself.

As a result, the full economic impact of AI may not become clear until businesses adopt the technology more broadly and reorganise their operations around it.

AI Adds To The Fed’s Policy Challenge

AI’s economic impact has become part of the Federal Reserve’s policy debate. Fed Chairman Kevin Warsh has argued that AI could eventually become a significant disinflationary force by increasing productivity and strengthening U.S. competitiveness.

Other officials are more cautious. In July, the Fed kept interest rates at 3.5% to 3.75%, while some officials expressed concern that AI infrastructure spending could add to inflationary pressures.

Minneapolis Fed President Neel Kashkari pointed to massive data-centre investment as a new source of demand. Household electricity prices rose 10% in the two years through July, compared with a 6.2% increase in overall consumer prices. Meanwhile, shortages of chips and other AI components are pushing up costs. JPMorgan Chase estimates that DRAM prices could rise 400% by the end of 2026 compared with 2024.

Warsh has consequently adopted a more cautious tone, saying that while AI investment is laying the groundwork for future growth, the timing and scale of its economic effects remain difficult to predict.

For the Fed, the challenge is clear: AI could eventually deliver major productivity gains, but the cost of building that future is already showing up in the economy.

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