Federal prosecutors have filed corruption and money laundering charges against 11 defendants, including Nigstu Bogale, Coordinator of the Development Project for Response to the Impact of Refugees in the Horn of Africa at the Ministry of Agriculture, Biniyam Fantaye and Taye Habte, senior irrigation engineers involved in the Development Response to Displacement Impacts Project (DRDIP-II), as well as private individuals and construction company representatives. The case alleges losses and illicit gains exceeding 733.8 million Br linked to a World Bank-financed irrigation project under the Ministry.
Prosecutors say the accused manipulated the procurement process for the Damale Bore flood irrigation construction project, enabling an unqualified contractor, Dawud Hamolo Gedo General Construction, to secure a contract worth more than 616.6 million Br. An additional 117 million Br in variation payments was also allegedly approved unlawfully.
According to the prosecution, the three senior officials abused their authority by influencing technical evaluations, excluding competing bidders, and authorizing payments beyond World Bank procurement limits.
Court documents further allege that several defendants laundered proceeds from the scheme by moving funds through personal and corporate accounts and acquiring property to conceal their origin.
The defendants face three counts of charges, including abuse of power, corruption, and money laundering.
As Ethiopia observes a week dedicated to infrastructure and construction, the conversation around development is moving beyond concrete, steel and machinery. The country’s long-term plan, the Construction Industry Transformation of Ethiopia 2025–2050, points to an industry in which buildings, roads and urban services are expected to work with digital systems from the start.
In that shift, Ethio telecom has positioned itself not only as a communications utility but also as a digital solutions provider, seeking to align its strategy, “Next Horizon: Digital & Beyond 2028,” with national priorities.
The change mirrors a simple reality. Modern construction sites now depend on a high-speed nervous system as much as they depend on cement supply or skilled labour. Engineers, contractors, and regulatory agencies need networks that can quickly move designs, approvals, sensor readings, and project updates between field offices and headquarters. Ethio telecom’s recent expansion is intended to support this demand.
The company has reached 10,438 mobile stations and extended 4G LTE service to more than 1,069 cities, covering 74pc of the population.
Its fixed infrastructure has also expanded. Ethio telecom operates a fibre backbone of more than 23,000Km, supported by 14,769Km of metro fibre. In cities, new metro fibre and dedicated “Fibre to the Tower” (FTTT) installations have been added to meet 5G requirements. Such networks can enable builders to use Building Information Modelling (BIM) and Digital Twin technology to test designs, monitor structural performance, and compare site progress against initial plans in real time.
Used well, these systems can help reduce delays, improve coordination and challenge the cost-overrun habits that have long burdened construction projects.
But connectivity alone is not enough. The data produced by modern infrastructure has to be stored, protected and processed. Construction projects now generate procurement records, design files, legal documents, land information and sensor readings. Ethio telecom has invested in modular data centres with a 5MW IT load capacity, 624 IT racks, and 4.5PB of elastic compute storage capacity. These centres are supported by an international internet gateway that has grown to 3Tbps, helping to move large volumes of data across systems.
For construction firms, the effect could be practical. Instead of relying on scattered files and vulnerable local servers, companies can host project-management systems and archives in the cloud. For public agencies, locally hosted systems can help keep project data, land titles and legal frameworks inside the country. That offers a measure of digital sovereignty and creates a more reliable single source of truth, in large public-private partnerships, where trust and documentation matter, this can reduce administrative friction and improve transparency.
The digital layer is also becoming part of Ethiopia’s urban and environmental agenda. As investors pay more attention to environmental and social governance, cities are under pressure to grow without locking in wasteful systems. Ethio telecom has deployed four super-fast electric vehicle charging stations, capable of serving 60 vehicles simultaneously, and installed 101 new solar-powered sites, bringing its total solar capacity to 30MWp.
The company is also developing digital solutions, including Smart City and Smart Campus tools. These applications use Artificial Intelligence (AI) and the Internet of Things (IoT) to support services such as water distribution, municipal waste tracking and energy use in high-rise buildings. The purpose is to make urban systems easier to manage and less costly to operate over time, while reducing future environmental pressure.
Infrastructure & Construction Week 2026 has shown how closely Ethiopia’s physical and digital plans are linked. Roads, towers and buildings may define the skyline, but their performance will increasingly depend on the networks, data centres, and applications behind them. As the country moves toward its Digital Ethiopia 2030 vision, the connection between telecom investment and construction reform will shape how efficiently projects are planned, built and maintained.
Progress will be measured not only by what rises above ground but also by the digital systems that keep it operating throughout the full life of each asset.
While many would point to the financial system in response to the question of the biggest risk posed by AI, our attention would be better directed more toward labour markets.
Financial concerns are certainly understandable. Even in 2026, the spectre of 2008 haunts every conversation about economic risk. When Lehman Brothers collapsed, and the global banking system teetered, governments faced a momentous choice. Either they had to bail out the banks with public money or watch the financial system implode. In the United States, policymakers chose a bailout, encouraging future risk-taking and enraging taxpayers who bore the cost.
But US regulators then spent the following decade building a new line of defence, which is now embedded in the global banking architecture. In the process, they offered a roadmap for addressing any systemic risks now accumulating within the AI industry.
To be sure, the Financial Stability Board (FSB) warns that regulatory frameworks designed for monitoring AI are still in their early stages. But the risks remain manageable. The AI industry has arrived at a juncture that should look familiar to anyone who remembers the pre-2008 financial system. Market concentration is extreme, the interconnections between major players are deep, and the industry’s critical infrastructure runs through single points of failure.
Before 2008, risk in the financial system was assumed to be widely distributed. It was not. Leverage was hidden in off-balance-sheet vehicles, counterparty exposures were opaque, and the failure of a single institution could cascade unpredictably through the entire system. Regulation was scattered across numerous entities, none of which had a complete picture of what was happening. Regulators had no framework for thinking about systemic risks, and no way to designate which firms would bring down others if they failed.
The AI industry has a similar concentration problem.
According to Menlo Ventures, only three companies – Anthropic, OpenAI, and Google – control roughly 88pc of the enterprise large language model market. And the hardware layer is even more concentrated, with TSMC completely dominating advanced-node semiconductor manufacturing, raising concerns about a potential global compute bottleneck. When a 7.4-magnitude earthquake struck Taiwan in April 2024, it temporarily disrupted semiconductor production and reminded the world how geographically concentrated this infrastructure has become.
Fortunately, the central innovation of post-2008 financial regulation has proven effective. It is to identify the institutions whose failure would be catastrophic, and mandate that they hold sufficient total loss-absorbing capacity (equity and long-term debt that can be written down) to fail safely. The results are clear. A Congressional Research Service analysis of US bank failures shows a sharp decline in failures following the post-crisis regulatory reforms.
Although none of the tools introduced after the financial crisis translates directly to AI (banks hold financial assets that can be valued and stress-tested, whereas AI systems rely on training data, model weights, and compute capacity), the underlying regulatory logic still applies. Regulators need only consider three adaptations.
The first is systemic designation and disclosure. Regulators and standard setters should identify which AI providers, cloud platforms, and chip manufacturers have become critical infrastructure for the financial system. The FSB’s October 2025 report on AI monitoring acknowledged that financial institutions are increasingly dependent on a small number of major technology providers for AI capabilities, but that monitoring efforts remain at an “early stage,” owing to data gaps and a lack of standardised taxonomies. Fixing that is the first step.
Operational resilience requirements should serve as a proxy for capital buffers. Instead of adhering to total-loss absorbing capacity, capital requirements, and systemically important AI providers would have to demonstrate redundancy, failover capacity, and genuine substitutability. Financial firms relying on a single AI provider should face concentration limits analogous to the exposure rules that prevent banks from lending too much to a single counterparty. The FSB’s Third-Party Risk Managment and Oversight Toolkit already provides a framework. Regulators should use it more aggressively.
We also need stress testing for AI-driven correlated failures. The European Systemic Risk Board warns that because AI models are “history-constrained” – trained on past data – they are inherently poor at predicting tail events outside their training distribution. This is precisely the kind of model risk that stress tests are designed to reveal. Regulators should develop AI-specific stress scenarios, such as the failure of a major cloud provider, a regulatory shock to a dominant model, or a geopolitical disruption to chip supply chains, and require financial institutions using AI in critical functions to demonstrate that they can absorb the shock.
Fortunately, AI-related failures would not necessarily trigger a 2008-style financial crisis. If regulators act with the appropriate urgency, the potential systemic financial risk from AI is manageable.
But what AI may do to people who work for a living is a deeper and far more consequential challenge. The scale of potential labour displacement from AI is no longer hypothetical. IBM has replaced hundreds of people in its HR department, where AI now handles 94pc of routine tasks. Salesforce has reduced hiring for engineering and customer service roles, and Shopify’s CEO has said that new hires will not be approved unless hiring managers can demonstrate that AI cannot do the job.
The World Economic Forum’s “Future of Jobs Report 2025” projects that 39pc of workers’ core skills will be disrupted by 2030, and McKinsey & Company estimates that half of today’s work activities could be automated between 2030 and 2060.
What is to be done?
Although research from the Harvard Kennedy School suggests that retraining for AI-exposed occupations can entail substantial earnings penalties, that is no reason to abandon this solution. Other countries, such as Denmark and Singapore, have spent amply on training, and their programs work well.
In any case, getting employers involved is essential because training programs should equip workers with the skills that are in demand now and that will be needed in the future. Ensuring access to high-speed broadband and digital literacy training is crucial.
Regulators have the tools to prevent an AI-driven financial crisis. But we still need policymakers to get serious about ensuring that the AI revolution works for everyone, not only the few who own the underlying technologies.
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