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The Aga Khan Fund for Economic Development (AKFED) has agreed to sell its controlling 54.08% stake in Nation Media Group (NMG) to Taarifa Ltd, owned by Tanzanian billionaire Rostam Azizi, ending a 66-year ownership of East Africa’s largest independent news publisher. The stake, held through NPRT Holdings Africa, represents about 92.6 million shares in the Nairobi-listed company, which operates more than 30 media brands across four countries and reaches over 62 million digital users. AKFED owns NPRT Holdings Africa, an investment vehicle used to hold the fund’s media interests across Africa, Asia, and the Middle East. The companies did not disclose the value of the transaction. The deal shifts control of one of East Africa’s most influential media groups at a time when publishers across the continent are racing to turn large online audiences into sustainable digital businesses. “We are confident NMG will continue to uphold the values of independent journalism and service to the public that have defined it for over six decades,” AKFED director Sultan Allana said in the statement announcing the sale on Tuesday. Nation Media Group built its reputation on flagship newspapers such as the Daily Nation, but like many global publishers, it has spent the past decade expanding digital platforms as print revenues weaken and readers move online. Its websites, mobile apps, and streaming services now reach tens of millions of users across Kenya, Uganda, Tanzania, and Rwanda. The ownership change could influence how aggressively the company invests in those platforms. NMG reported revenue of KES 6.2 billion ($48 million) in 2024, down 12.5% year-on-year, and a pre-tax loss of KES 253.6 million ($2 million), even as digital revenue rose 11%. The transaction marks the end of a relationship that began in 1959 when the Aga Khan founded East African Newspapers, the company that later grew into Nation Media Group. The publisher expanded over decades into television, radio, and regional media operations. Today, the group runs news, broadcast, and digital platforms including NTV, Nation Africa, and multiple regional publications. With more than 62 million digital users, NMG operates one of the largest news audiences in the region. Azizi, the incoming majority owner, has prior experience in the region’s media sector. He co-founded Mwananchi Communications in Tanzania, publisher of Mwananchi, The Citizen, and Mwanaspoti. Nation Media later acquired the company during its regional expansion in the early 2000s. The new owner plans to support the company’s digital growth as part of the transition, according to the statement.
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Spreadsheets can be humbling. They look simple on the outside, with their rows and columns. Type in a few numbers, arrange them in ascending or descending order, maybe use a summation equation to calculate a total, and that is ‘proficiency in spreadsheets’ going on my resume. Until the spreadsheet is one that has hundreds of rows and columns that spill across the screen, and then it starts to look like a puzzle. Abiodun Adetona noticed problems with spreadsheets and analysis during his four years as a software engineer at Flutterwave, Africa’s largest payments infrastructure startup. His colleagues from non-technical teams often needed help drawing insights from datasets or spreadsheets, which often required technical intervention. “I used to assist them [his colleagues] with pulling data from various sources every day for almost a year… it was hectic, and there was no easier way to do it than the technical way,” he recalled in an interview with TechCabal. In 2025, Adetona and his three-person team built Decide AI, an artificial intelligence (AI) spreadsheet analyst, to help users analyse data in spreadsheets via prompts. Inside Decide AI Decide AI landing page; Image source: TechCabal When I opened Decide AI for the first time, the interface felt really familiar, almost identical to modern AI assistants, such as ChatGPT or Gemini. The interface allowed me to choose between two AI agents to run my tasks. The Fast agent is designed for quick analysis that prioritises speed, while the Pro agent is intended for heavier tasks that require deeper analysis, such as performing complex calculations across multiple datasets. When I curiously clicked the ‘connections’ drop-down menu, I noticed that Decide AI is also designed to work with data that lives outside local spreadsheet files and allows for a connection to external sources such as Google Sheets, Metabase, Google Analytics, and Google Ads. Since I only have a Google Sheets account, I gave the AI agent permission to access my Google Drive upon request and connected my account. Connecting Google Sheets to Decide AI; Image source: TechCabal Use cases for the other connections could be a user managing marketing campaigns connecting their Google Ads account and asking the agent to analyse campaign performance, or a team using Google Analytics asking the agent to analyse traffic patterns or identify trends in user behaviour. Adetona said the agent can conduct basic tasks like cleaning datasets or performing calculations. It can also handle more analytical tasks, including scenario analysis, where users explore how different variables might affect a business outcome, and market size opportunity analysis, which involves estimating the potential value of a market based on available data. How Decide AI analyses a spreadsheet Upload files from local computer or Google Sheets; Image source: TechCabal To see how Decide AI behaves with a real spreadsheet, I uploaded a dataset from a course registration programme from my Google Sheets. This spreadsheet contained entries from participants across different parishes and deaneries in Lagos, along with other details such as when participants registered and whether they had uploaded proof of payment. Using the Fast agent, I asked the system to clean the dataset, count the number of participants who paid in each month, exclude certain entries, and group the results by benchmark. Once submitted, the agent displayed a reasoning trace that appeared as lines of its activity in a darker, code-like font. That visible execution trace reflects what happens behind the interface when an analysis is requested. Prompt given to Decide AI; Image source: TechCabal According to Adetona, the system runs a preprocessing stage that attempts to interpret the file structure to identify sheets, headers, tables, formulas, and relationships between parts of the dataset, which are then converted into a structured representation that the system can analyse more reliably. “The exact implementation is proprietary, but this step is critical because spreadsheet AI often fails when it misreads the structure of the file,” he said. He explained that once the document structure is mapped, it is passed to the AI agent along with the user’s request, which then plans how to carry out the analysis. The agent relies on frontier language models for reasoning, drawing on providers such as OpenAI, Anthropic, and Google. Adeotona said Decide AI is designed to be model-agnostic; the system can switch between models while its orchestration layer handles spreadsheet interpretation and computation. The system generates Python code for spreadsheet analysis and calculations, and runs it in a secure sandboxed environment. “We do not rely on prompt reasoning alone for calculations because that is more prone to hallucination,” Adetona added. Decide AI reasoning trace; Image source: TechCabal He further explained that the generated code runs against the dataset, executing tasks like filtering entries, grouping results, calculating totals, and reorganising the spreadsheet. If the initial output does not fully answer the prompt, Adetona said the system can iterate through additional computational steps until it produces a complete result. Once those calculations are complete, the platform runs a verification stage that checks the output against the provided spreadsheet data before returning the final response. For my prompt, the system produced a structured summary of the dataset in the chat window within two minutes. This summary highlighted the number of participants who had paid in January, grouped the results by deanery, and flagged registrations that did not include proof of payment. It also identified which deaneries had the highest number of incomplete payment records. Excel sheet generated by Decide AI showing categorisation of data by deanery; Image source: TechCabal Along with the summary, Decide AI generated an Excel workbook that can be downloaded, but I exported it to my Google Sheets. This workbook contained five different worksheets derived from the original dataset. One sheet summarised January registrations, another compiled a breakdown of the data as I requested, another isolated entries where proof of payment had not been uploaded, and the others listed detailed records of participants whose registrations were missing documentation. I pushed the analysis further by asking Decide AI to produce
Read MoreSatgana, a venture capital firm that invests $570,000 each in African and European climate tech startups, is raising two new funds—one for each continent. The firm, named after the Sanskrit word for “good company,” has deployed capital into 30 startups across both regions, backing companies where climate efficiency drives the business model. “We are currently structuring two dedicated vehicles, one focused on Europe and one on Africa,” said Anil Maguru, a partner at Satgana. “We are not communicating final fund sizes publicly yet, as the process is still ongoing.” Maguru joined Romain Diaz, Satgana’s founder, as a founding member and partner when the fund was launched in 2020, and his unique perspective with Diaz helped build the firm’s Africa-European strategy. “The European and African positioning is less about geographic diversification and more a design principle: African markets often act as a strong filter that forces companies to build for resilience and real demand, while Europe provides industrial partners, capital depth, and exit routes,” said Maguru. Satgana invests in climate technology solutions across transportation, energy, food and agriculture, industry and construction, carbon abatement, and the circular economy. Crunchbase Some of Satgana’s portfolio companies include Orbio Earth, a Germany-based startup that uses satellite imagery and proprietary algorithms to track and quantify methane emissions from the oil and gas industry; Mazi Mobility, a Nairobi, Kenya-based mobility-as-a-service company electrifying the motorcycle taxi (“boda boda”) industry with electric bikes and battery swapping stations; and Revivo, a Nairobi, Kenya-based B2B marketplace connecting small repair shops with quality electronic spare parts, accessories, and repair tools to build a circular repair economy. For this week’s Ask an Investor, I spoke with Maguru about the fund’s strategy of backing climate-tech startups across Africa and Europe, how Satgana evaluates startups, and why the firm prioritises solutions that work under imperfect market conditions. This interview has been edited for clarity and length. You’ve been investing in startups and helping to build companies for several years now. How have the past five or six years been for you, and what have you learned? Over the last six years, it has been an intense journey. When we started, everything was a first for us: first-time team, first-time fund, first-time investments together, really, first-time everything. But now, looking back over the past five or six years, we’ve built a record we are very proud of. We manage about $10 million in assets under management. We have backed 30 companies across 16 countries. We have more than 150 investors from 25 different countries. About 90% of our portfolio is still alive, which is a very high survival rate, and close to 50% of the portfolio has gone on to raise follow-on rounds. Of course, none of it would have been possible without the amazing founders we’ve met along the way. At the same time, we’ve also experienced our first failures; some companies have started to go down. But overall, things have gone very well. The team has also grown. We now have dedicated teams looking at both regions: Europe and Africa. The impact is growing, and we still see more opportunity ahead. You talk about opportunity. What is Satgana’s next chapter? From our first fund, we were fortunate to invest in 30 amazing companies and back exceptional founders, including female founders, across Europe and Africa. We saw firsthand how much both regions have to offer. For us, it was therefore very logical to double down on this strategy. We are now launching two new funds: one dedicated to Europe and one dedicated to Africa, each with a dedicated team. The goal is to source even more founders, become even more pan-African than we were before, and build dedicated vehicles for dedicated geographies. These funds will also be larger. We will be able to write bigger tickets and have more capacity to double down on the right companies. In Africa, especially, we are looking for companies that show strong signals early—companies with paying customers, companies that do not rely heavily on subsidies, companies where adoption spreads through trust and word of mouth, and companies led by founders with real financial discipline. That, for us, is real traction. So, for companies that see themselves in that description, we would very much encourage them to reach out. We need to find them because those are exactly the kinds of businesses we want to back with these new funds. Get The Best African Tech Newsletters In Your Inbox Select your country Nigeria Ghana Kenya South Africa Egypt Morocco Tunisia Algeria Libya Sudan Ethiopia Somalia Djibouti Eritrea Uganda Tanzania Rwanda Burundi Democratic Republic of the Congo Republic of the Congo Central African Republic Chad Cameroon Gabon Equatorial Guinea São Tomé and Príncipe Angola Zambia Zimbabwe Botswana Namibia Lesotho Eswatini Mozambique Madagascar Mauritius Seychelles Comoros Cape Verde Guinea-Bissau Senegal The Gambia Guinea Sierra Leone Liberia Côte d’Ivoire Burkina Faso Mali Niger Benin Togo Other Select your gender Male Female Others TC Daily TC Events Next wave Entering Tech Subscribe Climate tech is a very broad category. It can cover everything from carbon accounting software to nuclear fusion. What is Satgana’s own definition of climate tech, and where do you draw a hard line on what you would not invest in? For us, the hard line is drawn at businesses where climate is simply a story layered on top of a normal company, rather than the engine of the company itself. For instance, if a startup’s margins improve when emissions increase, or if the impact disappears the moment subsidies disappear, then the alignment is broken. We try as much as possible not to invest in green narratives. We invest in climate economics. Working in Africa made this very clear to us very early on. Customers do not buy climate because it is good for the planet. They buy reliability, cost savings, and productivity. If climate does not translate into real economic value for the user, it does not scale. That is why we avoid solutions that depend on perfect regulation, heavy
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