Artificial Intelligence (AI) tools will undoubtedly transform the nature of work. Large language models can already generate referee reports on my own research papers that rival those by human referees.
Unlike humans, who are always pressed for time, an LLM “knows” or can access much more of the literature in an instant, and often exhibits fewer biases. AI points out my analytical weaknesses, checks proofs, and makes suggestions for improvement. Only rarely are human reports better, typically because they connect the dots and offer new insights.
Nonetheless, the market euphoria around AI has become worrisome, especially given the scale of debt issuance in the sector. It is, therefore, worth considering where in the AI supply chain things could go wrong.
The supply chain starts with producers and designers of AI infrastructure. These are firms like TSMC and Samsung, which fabricate chips; Nvidia, which designs them; and Cisco, which provides connectivity. Then come the hyperscalers like Amazon, Google, and Microsoft. They are building data centres for their own AI models and to sell compute (processing power) to others. In addition to the hyperscalers, there are more specialised companies like Equinix (data centres) and, of course, Anthropic and OpenAI, the developers of foundational LLMs.
Finally, there are the individual and corporate end users of AI services. Individual use is growing fast, and corporate use in some areas (software development and customer support) is exploding.
But most large businesses, while experimenting intensely, have yet to implement end-to-end uses. Many still need to organise their historical data to train AI for their own purposes, and to restructure their traditional operations so that AI can be deployed to improve with experience. Many firms rightly worry about data security, AI errors, and hallucinations that could destroy their brand image. Still, as less conservative younger companies find more AI uses, they will put competitive pressure on older, larger firms to change.
The AI rollout could nevertheless be interrupted in several ways, posing risks for debt-funded players. For instance, if graphics processing units, CPUs, and memory chips become faster and more energy efficient, the equipment filling existing data centres could depreciate rapidly, making it harder for them to amortise their costs. And LLMs, which have become extraordinarily capable through essentially next-word prediction, could plateau until a new technique emerges.
For now, AI labs are investing massive sums to train newer and larger models, on the assumption that the first model to reach some magic point where it becomes self-improving will rule the AI world, and reap enormous profits. But this scenario seems implausible. Even if there is such a point, competitors could still match the first mover’s model (including by hiring away key employees to obtain technical trade secrets).
So far, no AI model seems to have gained a sustained advantage. Unless Gemini (Google), Claude (Anthropic), and ChatGPT (OpenAI) can eventually differentiate themselves by appealing to specific user segments (or by merging or colluding), it is hard to see where the profits justifying their enormous training investments will come from.
Moreover, although politicians have been largely standing on the sidelines so far, policy interventions to address AI risks and concerns are inevitable. Since data centres consume tremendous amounts of power, driving up the power price for everyone, state and local governments will be under increased political pressure to limit their construction. In Indiana, for example, multiple counties recently proclaimed a moratorium on data-centre construction.
Projections for next year already suggest that hardware makers and data centres will be unable to supply enough US compute capacity. And as compute shortages mount, end users will have more reasons to delay implementation. We cannot reorganise all our operations around AI if we have good reason to worry about future access reliability or reasonable pricing.
Worse, whereas broader use may take longer than many expect, malevolent use by hackers and deepfakers, as well as unsupervised use by children, is growing rapidly. It is not difficult to imagine disaster scenarios, such as a deadly cyber incident, gross data misuse by AI agents, or poorly trained AI models advising children to commit acts of violence against themselves or others (something that has already happened). The chorus demanding regulation and more liability for AI models will only grow louder.
The risks posed by rogue AI could even prompt a sorely needed dialogue among major powers, perhaps leading to some kind of AI Geneva Convention.
Perhaps the most important trigger for political intervention would be massive AI-related job losses. Fearful of political or social backlash, even firms inclined to adopt AI may hesitate to shed redundant employees outside a recession, thereby reducing any gains from AI deployment and diffusion.
Given all these uncertainties, it is far from clear how widely and quickly AI will be rolled out, and who will profit. Hardware manufacturers and designers seemed well-positioned, given the tremendous demand for computing. But if data-centre construction is interrupted, that could shift profits to hyperscalers and AI labs. They might reduce the amount of compute dedicated to training better models, which gives them only fleeting advantages, and shift to selling the compute they have sewn up to firms using their already capable models.
Such shifts are also likely if model capabilities plateau. Regulation might also force modellers to spend more effort on improving the training and safety of existing models, building broader public trust.
The good news is that a more limited and careful AI rollout could give firms more time to find labour-augmenting (as opposed to labour-displacing) uses, and governments and workers more time to adjust. The bad news is that euphoric visions of quick exceptional profits could be unfounded, a particular problem for AI firms that have to make unforgiving debt payments. AI advances will likely pay off eventually. But not every provider will profit, or even survive.
As the war in Iran disrupts fertiliser supplies and undermines food security around the world, the need to build more resilient food systems has never been clearer.
The hundreds of millions of women who farm across the Global South have a vital role to play in meeting this challenge, but their ability to do so continues to be systematically undermined. Though women comprise over 40pc of the agricultural labour force in developing countries, women farmers are less likely than their male counterparts to have access to quality seeds, fertilisers, and tools and less likely to be connected to markets.
They are also less likely to be visited by extension agents responsible for delivering agricultural innovations and practical solutions directly to farmers. And agriculture researchers are less likely to consider their experience and interests.
These failures compound one another, undermining yields, nutrition, incomes, and, ultimately, economic growth and development. Meanwhile, pressure on the global food system is intensifying, owing not only to conflicts like the Iran war, but also to population growth and proliferating floods and droughts, which climate change is making fiercer and more frequent.
One crop, the Bambara groundnut, illustrates the cascading costs of overlooking women farmers and illuminates a potential pathway toward strengthening food security. This protein-rich legume, widely cultivated by Africa’s female farmers, is a marvel of resilience, capable of thriving in harsh and drought-prone conditions. It also fixes nitrogen in depleted soil, thereby improving fertility. And women farmers grow it much as their mothers and grandmothers did, planting unimproved seeds, passed down from season to season, in fields fed by fickle rains.
The resulting yields average some 300Kg to 800Kg a hectare. That is less than a third of what researchers believe could be produced even with unimproved heritage seeds. With more advanced seeds, the gains could be tremendous. A triple dividend of significantly higher yields, enhancing nutrition, improved soil health, and economic empowerment of women can be achieved. And yet, the Bambara groundnut has received scant attention from agricultural researchers.
Ghana’s Council for Scientific & Industrial Research–Savanna Agricultural Research Institute is seeking to change this and to ensure that women, in particular, reap the benefits. With support from organisations such as Grow Further, separate large-scale surveys of male and female farmers were conducted to determine what they wanted in an improved Bambara groundnut variety.
This gender-based approach is rare in agricultural research, and the results were revealing. Whereas the men wanted simply to increase yields, the women emphasised the importance of quicker maturation. As they explained to researchers, when the planting season approaches, they should first help their husbands with their planting before turning to their own fields. This is not personal preference but a reality of gendered labour patterns. That delay leaves less time for women’s Bambara groundnut crops to mature, impacting the harvest.
This is precisely the kind of game-changing insight that goes unnoticed for decades, simply because women have no opportunity to voice it. Instead, solutions are designed to suit a generic, implicitly male, farmer and framed as being for everyone. The result is land-titling systems that document male ownership by default; credit markets that demand collateral that women are legally or customarily barred from holding; and policy processes that discuss food security in aggregate terms, ignoring the gendered distribution of its costs and benefits.
Rather than designing solutions for half the farming population while leaving the other half struggling to adapt to systems not built for them, researchers, policymakers, and others whose decisions affect agricultural operations and outcomes should consider the specific needs and preferences of female farmers. That means using gender-disaggregated data, like that collected by the CSIR-SARI. It also means collaborating with women in research and policy design, instead of treating them as passive beneficiaries. And it means redesigning financial systems, land frameworks, and extension services to reflect the reality of women’s lives.
Technology also has a role to play. AI is accelerating the pace of crop improvement by enabling researchers to analyse genetic traits, predict breeding outcomes, and identify groundbreaking seeds in a matter of months rather than years. And genomic editing technologies are putting unprecedented precision and power in the hands of breeders and researchers. These are powerful tools, which should be harnessed to serve the interests of male and female farmers equally.
The Bambara groundnut has been dubbed a hidden superfood, astonishingly resilient, capable of sustaining communities, and tragically overlooked. The same could be said about the women who grow it. In an era of intensifying food insecurity, that is not an oversight the world can afford. This year, which the United Nations has declared the International Year of the Woman Farmer, offers a critical opportunity to correct this mistake.
Ervin Massinga, the United States ambassador to Ethiopia, posted a video on the Embassy’s official social media account following his recent two-day visit to Bahir Dar in the Amhara Regional State. He said there has been a “lot of pain” in a region that has “gone through much.”
The percentage of merchandise trade in Africa intermediated by banks on the continent in the four years beginning 2020, down from 40pc between 2011 and 2019. Ethiopia’s 2024 merchandise imports totalled 16.77 billion dollars, about 4.6 times its exports that year. Exports of goods and services were 6.6pc of GDP and imports at 14pc, revealing how small and imbalanced the external trade base was relative to financing needs.
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