GhanaChat data centre could require $61m annual subsidy – Bright Simons

Vice-President and head of Research at the IMANI Centre for Policy and Education, Bright Simons has questioned the financial viability of Ghana’s proposed US$270 million artificial intelligence (AI) data centre, warning that its operating costs could require substantial annual government subsidies.

Referring to an argument he made on Joy News’ Digital Economy Forum on AI and the Ghana Economy on Wednesday, Mr Simons said in a post on X, Mr Simons said the facility, which is expected to support the development and hosting of GhanaChat, would have a computing capacity of about 2,100 graphics processing units (GPUs) and an information technology (IT) load of 4.3 megawatts (MW).

1. Readers of this page know by now that JoyNews held a forum on Ghana's AI strategy and prospects. And the Digital Minister and I got into a serious debate about his Ministry's plan to build Ghana's competitor to Gemini, Kimi, ChatGPT, Qwen, Claude, etc. 2. Ghana's competitor to these global AI platforms is called GhanaChat. 3. It will be trained and hosted in a brand new AI datacenter. 4. 48 registries pooling data from across the public sector will make this AI smarter about Ghana than the foreign ones. 5. The AI datacenter and the "AI Fund" will cost about $270 million. Expect more to be spent on the registries and the apps. A lot of contracts. You can bet that a lot of ICT contractors are very happy. 6. The question is, if we have a couple of hundred million dollars to spend on making AI deliver great benefits for Ghana, should we spend it on GhanaChat, a top-tier AI data center, and the data-pooling system? And should we do it according to the strategy the Ministry has devised? 7. Or could we do much better by more critically assessing all our strategic options and comparing them to the Ministry's? 8. My argument on the show was that I am not convinced about GhanaChat offering the best value for our money. 9. It would take many pages to explain my position fully. But in this post I will highlight just the most important points. 10. Attached are extracts from page 13, 50, and 68 of Ghana's AI strategy. 11. If you have even the barest exposure to AI, you can see a massive error. Can you? 12. The target for how much computing capacity the country needs for next year (2027) is indicated as "1021 FLOPS" and the 2035 target as "1025 FLOPS". 13. If this was indeed correct, it would mean that the Ministry would like the entire computing capacity of the country in 2027 to be equivalent to a 1970s desktop computer. Or 2% of the capacity of the 1980 Intel math chip. 14. Howzat? 15. I'm sure you can agree that this would be totally ridiculous if that was the Ministry's intent. So, what happened? 16. Somebody who didn't fully understand how FLOPS work must have been involved in the drafting. What was meant to be 10 raised to the power 21 operations per second (10²¹ operations a second) was written out as "1021". Same for "1025". 17. Why wasn't the error caught? Because in Ghana when doing "consultations", governments rarely engage independent and critical voices who will point out absurdities without fear of offending big men and women. 18. I am not being petty. There is a serious strategic confusion beyond mere typos. 19. Read properly as 10²¹ FLOPS, the Ministry is telling us that by next year it will have some 200,000 of Nvidia's newest chips in use in Ghana, 500 MW of power devoted to AI, all costing about US$24 billion (close to the Ghanaian government's entire programmed spending for 2026). 20. The US$250 million AI datacenter now being procured supplies about 1% of this 2027 target. 21. Howzat? 22. Clearly, no serious critique of the plan would have okayed such a vision. Throughout the ministry's own review, cabinet approval, and a high-profile media launch, it is absolutely clear that not one critical reviewer was engaged. 23. My first major point here is that this is classic KATANOMICS: the lack of critical policy audiences. This is exactly how a sugar factory that was "designed from the start to fail" was allowed to be built. Many programs and initiatives in Ghana fail for precisely this absence of critical review. 24. In the strategy, you find statements like: "government aggressively building AI compute clusters" and by courting hyperscalers, "to leverage the scaling laws towards building general purpose AI and AGI". Then: "drastically increase Ghana's energy capacity 0.5 to 1 OOM per year". "OOM" means "order of magnitude". 25. When I challenged the Minister on TV, he confidently rebutted by claiming that the Energy Minister/Ministry has assured him of feasibility. Ghana's energy Ministry is committing to increase Ghana's power output by 3x to 10x EVERY YEAR so that the Digital Minister can deliver his AI strategy? Did anyone in Cabinet not protest that this projection in 8 years would mean expanding Ghana's electricity output by 10,000x? 26. I can bet you any amount that the Energy Minister will deny making any such commitments in Cabinet. Can we have clarity? Can JoyNews check? 27. Page 68 of the strategy defines an "Artificial General Intelligence Lab" as "an AI lab with access to more than 1010 compute". No unit is given at all. In fact, as written it is totally meaningless. What they likely mean is that Ghana will support the establishment of a lab with computing capacity of 10¹⁰ FLOPS (10 gigaFLOPS). 28. The problem is that such a lab would be totally useless. To give you perspective, an iPhone 7 has about 350 gigaFLOPS of capacity. 29. What would be required to meet the Ministry's 2027 AI target? If for frontier model development, then the following spec would be reasonable: 400,000 16-bit Nvidia B300 GPU units, 1,114 MW of dedicated power (which is 26% of Ghana's peak demand). And the budget could easily hit US$47.2bn. For less frontier use, we can halve these numbers. 30. Counted in the 64-bit arithmetic that scientific supercomputers are ranked by, Ghana's 2027 target is 455 times the world's fastest machine, China's LineShine at 2.2 exaflops, and 53 times all 500 machines on the list combined. 31. Anyone with the barest exposure to Ghana's fiscal context can tell the Minister for free that this won't happen next year. 32. Ghana's 2035 target of 10²⁵ is 160 times the entire global stock of AI compute today. Even if chips grow 1.4 times more energy-efficient every year, it would draw about 377 GW, 65 times Ghana's current installed generating capacity. 33. I will be charitable and propose that the strategy's drafters may have been thinking about training FLOP (note: without the "per second" at the end). For example, GPT-4's training run used about 2.1×10²⁵ FLOP, the same order of magnitude as Ghana's 2035 endpoint. 34. Unfortunately, that doesn't save the strategy. Recast this way, the target collapses the other way. 10²¹ FLOP is about 12 days' work for one H100 chip at 16-bit precision. 10²⁵ FLOP is one GPT-4-sized training run, which the US$250 million centre could finish in about four months. On that reading the centre meets the 2035 target before its first anniversary. Not meaningful. 35. Given how confusing the overall strategic plan is, would you bank on the first AI compute center and GhanaChat product already being procured at about $270 million? 36. Here are some bare numbers: the budget, per our modelling, maps to about 2,100 GPUs and 4.3 MW of IT load. That alone adds the equivalent of 90% of Ghana's entire commercial data-centre load in 2024 (4.8 MW). Running it costs about US$76 million a year, which is about six times the revenue of the whole commercial industry. 37. A utilised GPU-hour costs about US$22 at base-case demand, against a market rate of US$6.71 today and roughly US$4.50 averaged over the hardware's life. The base case thus implies a subsidy of about US$61 million a year. 38. If the concern is about Ministers and their Directors uploading sensitive documents into Gemini or Kimi, then why not a simpler sovereign AI assistant for the civil service first? 39. That would need roughly 144 GPUs: about 7×10¹⁷ operations a second at 8-bit, US$13 million up front and US$6.7 million a year. That is under a thousandth of the 2027 target, and a far more realistic initial-term strategy. Does Ghana really need to train its base model when orchestrators now allow one to host their own data and switch across multiple advanced open-weight models to serve an Assistant or Agentic system? 40. Howzat?

— Bright Simons (@BBSimons) October 25, 2026

He said this would add the equivalent of about 90 per cent of Ghana’s entire commercial data centre load in 2024, estimated at 4.8 MW.

Mr Simons further estimated the facility’s annual operating costs at approximately US$76 million, about six times the revenue of Ghana’s entire commercial data centre industry.

According to Mr Simons, the economics of the proposed facility would be difficult to sustain if it had to sell computing services at prevailing market rates.

He estimated that a utilised GPU-hour would cost about US$22 under the project’s base-case demand assumptions, compared with a current market rate of approximately US$6.71 and an estimated average of US$4.50 over the hardware’s operating life.

“The base case thus implies a subsidy of about US$61 million a year,” he stated.

The estimate raises questions about how the government intends to fund the facility’s recurring expenses and whether revenue from customers using its computing services would be sufficient to cover operating costs.

Mr Simons also argued that the proposed data centre would provide only about one per cent of the computing capacity implied by the Ministry’s 2027 target, which he interprets as (10^{21}) floating-point operations per second (FLOPS).

He estimated that achieving the target for frontier AI model development would require about 400,000 Nvidia B300 GPU units, dedicated power capacity of 1,114 MW and investment that could reach US$47.2 billion.

He said the estimated power requirement would represent about 26 per cent of Ghana’s peak electricity demand.

“Anyone with the barest exposure to Ghana's fiscal context can tell the Minister for free that this won't happen next year,” he wrote.

The figures are Mr Simons’ estimates of what would be required to meet the stated computing target. Their assumptions and the relationship between the target and the government’s planned infrastructure would require independent technical and financial assessment.

As an alternative, Mr Simons proposed that Ghana initially develop a sovereign AI assistant for the civil service rather than pursue a large-scale computing facility intended to support a national AI model.

He estimated that the system could operate with about 144 GPUs, delivering approximately (7 \times 10^{17}) operations per second at eight-bit precision.

He put the initial investment at US$13 million, with annual operating costs of about US$6.7 million.

“That is under a thousandth of the 2027 target, and a far more realistic initial-term strategy,” he wrote.

Mr Simons also questioned whether Ghana needed to train its own foundational AI model from scratch, suggesting that the government could instead use existing advanced open-weight models while retaining control over its data and how the system is deployed.

The proposed data centre forms part of the government’s broader AI strategy, which includes developing GhanaChat as a national platform intended to compete with international services such as ChatGPT, Gemini and Claude.

The plan also includes an AI Fund and the integration of data from 48 public-sector registries to improve the platform’s ability to respond to Ghana-specific needs.

The proposed data centre and AI Fund are estimated to cost about US$270 million, with additional expenditure expected for the registries and related applications.

Mr Simons’ analysis focuses on whether the proposed investment can be sustained financially and whether a smaller, more targeted system could deliver public-sector benefits at a lower cost.

His assessment places the project’s capital expenditure alongside its projected operating costs, potential subsidy requirements and the scale of computing infrastructure implied by the strategy’s longer-term targets.

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