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In early March, Volkan Çinar, a chemistry postdoc at MIT, received an email recruiting him to train AI models. Çinar studies carbon-carbon bonds formation in graphene. Given the stiff competition for jobs in academia, Çinar was no longer sure if his dream of working in academia made sense. So he was receptive to the emails pitch. The email came from Handshake, the job search platform which connects 18 million students from 1,600 higher ed institutions to career opportunities, introducing its new MOVE (Model Validation Expert) Fellowship. The new program gives Handshake an entrée into the high end of AI model training, the hot sector thats seen Meta acquire a 49% stake in Scale for more than $14 billion and Surge bootstrap itself to $1 billion in revenue. For talent like Çinar, MOVE offers better money than teaching and comes with AI training. Id never considered working in AI, Çinar says. But given that Im exploring other positions, I thought Id give it a try, even if it meant the risk of paving the road for AI models to take over his field. Paid subscribers will learn: What to expect from the program The Fellowships acceptance rate and pay range How to make yourself competitive for an AI gig A better way to source expert talent for AI labs Handshake, which started in 2014 and is valued at $3.5 billion according to PitchBook, saw an opportunity to expand into AI training last year. Founder Garret Lord said he came up with the idea after talking to a researcher from an AI lab. At the time Handshake had already been approached by major AI labs directly and middlemen to source experts to train AI models. Many of the PhDs and master’s students were saying they were frustrated by the experience as related to getting trained effectively and not getting paid on time, Lord said on the Kleiner Perkins Grit podcast. Lord realized he was sitting on a major asset: a huge talent network. So Handshake created MOVE, which launched on June 10. We wanted to make sure that as the world of work continues to evolve into more of an AI economy, says Christine Cruzvergara, chief education strategy officer at Handshake, that we were doing everything we can to equip our students with the skills and the experiences that they might need to be really competitive in that job market. The MOVE fellowship pairs graduate students and postdocs like Çinar with short-term gigs of about 10 to 20 hours a week with generative AI labs. Fellows go through Handshakes training program, which takes approximately two to five hours, though some projects may require more training. Fellows learn about how large language models work, as well as how to create good prompts. Depending on their level of expertise they can earn between $40 and $130 an hour. Fellows are under strict NDAs when it comes to discussing their actual work, but Rachel Mitchell, a doctoral candidate in education at University of Miami described the general workflow. Mitchell works in pods with other experts to develop prompts or scenarios for the AI models to chew on. After running the prompt through the AI model the pod determines which parts the model successfully answered and which parts it didnt. The pod will discuss what went wrong, and rewrite the prompt and run it again to make sure that the result wasnt created because of an unclear prompt. The feedback goes to the AI lab which then uses it to train and improve the model. I want to make sure that we’re actually developing the knowledge of the AI and testing its limits, says Mitchell, and that it’s not failing due to human error. Unlike data labeling company Surge, which uses an algorithm to match experts to projects, Handshake uses human review. Fellows are matched to the projects based on their résumés, interviews, and the training they do at Handshake. An expert will help evaluate applicants for domain knowledge. The expert typically also ends up as the project lead and can provide feedback on the fellows work as well. Today, the MOVE program has more than 1,000 fellows and works with alumni from any university in America. Democratizing access to information Neither Mitchell nor Çinar feel as if they are cannibalizing jobs in their industry. AI, at its best, is a thought partner, says Mitchell, who hopes to become a professor. My contribution is helping develop a tool that’s reliable enough that someone who is skilled or has some background knowledge can use it effectively. Mitchell sees generative AI as an essential link for helping disseminate her work to the people who will apply it in classroom settings and dont have the time or resources to do a literature search. She points out teachers are overworked and burnt out, and many may be trying to lighten the load by using AI to generate their lesson plans. However, AI often produces flawed results, generating a recommendation with vague learning objectives instead of specific goals that can be measured in students. But if generative AI draws from the latest research on best practices in education, it could generate useful plans that teachers can use. Similarly, Çinar says that in his field, researchers conduct literature searches to make sure the techniques they are testing are research backed. Wading through scientific journals can be arduous, and using AI to help can slice down the work. Çinar doesnt know what his next step will be: research or industry. What he is sure of is that itll probably ask for AI expertise. He hopes his time spent writing prompts and navigating AI models will help him compete in the job market. He also noted that the program has come with other side benefits. First, hes getting to meet and network with other experts and learn from them. Second, he gets to learn how to operate in a corporate setting. Handling these projects, delivering them on time, and doing quality work as a whole package, he says, I think will be very valuable for any job I end up at. A lifeline in the entry-level job drought In a world where AI skills are in high demand, Handshake offers an attractive prospect: getting paid to get hands-on AI experience writing prompts in your field. According to a Brookings Institute report, job postings asking for AI skills have grown from roughly 3,000 in 2010 to over 80,000 this year. Meanwhile, companies like Duolingo and Shopify are mandating that employees use AI on the job. However, 63% of Americans have never used AI at work, and only 24% reported receiving AI training according to an October 2024 survey from Pew. Emory University sees the MOVE fellowship as an opportunity to equip students for the AI uture. Branden Grimmett, associate dean of Emory College and vice provost of career and professional development at Emory University, said the school immediately signed on after a presentation from Handshake in March. In particular, Grimmett and his colleagues appreciated how the fellowship offers training to students in the humanities. Since launching nearly a month ago, 110 Emory students have applied to join the program, which Grimmett says is a high level of interest compared with other job and training offerings. Handshake says approximately 25% of applicants are accepted into the program, even though the program is still so young as of yet no one has reported being able to parlay their MOVE experience into a job. No generalists, experts only Handshake says applicants who want to stand out should be very clear on their subject-matter expertise. Reference advanced research publications, patents, or teaching that demonstrates your knowledge, Cruzvegara notes. She also points out its important to highlight problem-solving and critical thinking skills and experience with designing assessments and rubrics, or tasks that require deep reasoning. In addition, since fellows work in pods together, demonstrating a history of collaboration such as working in research groups or interdisciplinary environments is crucial. Handshake looks for applicants who combine domain expertise with the desire and intellectual acumen to challenge AI models. Lord envisions a future where MOVE directly leads to a job, where fellows have badges on their profiles and there are leaderboards by school. Going forward, Handshake is considering opening the program to young professionals. Cruzvegara points out that AI is here and the choices are to resist and get left behind or figure out how to adapt. I don’t believe that AI alone is going to take your job, but I do think that people who know AI certainly will, Cruzvegara says. We’re doing our best to make sure that as much of the early talent population as possible is actually exposed to and aware of how they can be using AI in their future roles.
Category:
E-Commerce
Africas e-commerce sector is experiencing a renaissance. In 2025, Africas retail e-commerce is expected to reach over $39 billion, and $55 billion by 2029. This growth is driven by ambitious brands, from contemporary fashion labels to innovative lifestyle businesses, capturing both local and international demand. These businesses are leveraging digital commerce and modern payment rails to reach more customers than ever before. E-commerce is also emerging as a critical lever to help businesses recover from economic shocks and unlock the trade potential of the African Continental Free Trade Area (AfCFTA). Yet one critical barrier threatens to limit their growth: access to affordable, flexible credit. Medium-scale e-commerce enterprises, those beyond micro-entrepreneurship but not yet large corporations, face a unique financing challenge. These businesses are too large for microfinance but often too small, or perceived as too risky, for traditional commercial banking. Despite steady sales, loyal customers, and growing brand equity, they struggle to secure financing to scale production, expand logistics, or invest in technology to serve a wider market. The financing shortfall for sub-Saharan Africa exceeds $331 billion, with medium-sized consumer-facing businesses among the hardest hit. A report by USAID and eTrade Alliance which surveyed over 2,000 micro, small, and medium-sized enterprises (MSME) in Kenya, Nigeria, and South Africa, shows that these businesses are eager to expand e-commerce capabilities, internet connectivity, and invest in digital transformation, but access to finance remains one of their greatest barriers. Consider, for instance, a mid-sized Nairobi fashion retailer that grew steadily through an online storefront but struggled to finance improved packaging and marketing to reach buyers in Europe. Despite years of consistent sales, it was unable to access credit on reasonable terms because traditional lenders viewed its cash flows as unpredictable. Cases like this are widespread. As Africas middle class expands, with roughly 212 million people projected to reach middle income status by 2030 and consumer spending expected to hit $2 trillion in 2025the demand for e-commerce will surge. However, tight credit access could block supply-side growth. When growing brands cannot secure credit to expand inventory, strengthen logistics, or build new supplier partnerships, they risk ceding market share to bigger, better capitalised competitors. These missed opportunities ripple through ecosystems and impact suppliers, logistics firms, and technology partners that depend on a thriving e-commerce sector. 4 things that need to improve Credit is not a luxury for these businesses. It is essential to transform local brands with global ambitions into long-term economic engines. It can do so by following these four principles. Smarter lending evaluation. Information asymmetry hinders small and medium enterprise (SME) financing in Africa; many small businesses lack formal financial records and credit history. This leads traditional lenders to demand high collateral given difficulty in assessing risk. To overcome this, lenders should use real-time transactional data (e.g., e-commerce sales, inventory, customer reviews) to accurately assess creditworthiness for excluded SMEs. Data-sharing partnerships. Payment providers, marketplaces, distributors, and banks should collaborate to share transaction histories and supply chain data, helping lenders assess risk with confidence. Blended finance and risk-sharing facilities. Scale public-private instruments like the Africa Guarantee Fund and the Bank of Industry risk-sharing arrangements. These reduce lenders risks and help lower the cost of credit. Targeted financing for digital transformation. As the Alliance for eTrade Development research shows, many MSMEs want to invest in better internet connectivity, digital marketing, and fulfilment capabilities but cannot secure affordable loans for these upgrades. New financing products tailored to e-commerce investment would directly unlock their growth potential. The opportunity ahead As governments and the private sector work to advance e-commerce policy frameworks under AfCFTA, prioritizing simpler customs procedures, strengthening cross-border payments, and improving digital ID systems, credit access must rise to the top of the agenda. Payments are a foundation, but credit is a growth driver. We must build the financial tools that empower Africas most ambitious entrepreneurs to dream bigger, scale faster, and compete globally. When they grow, Africa grows. Olugbenga GB Agboola is founder and CEO of Flutterwave.
Category:
E-Commerce
Its time to admit it: Too much of the social impact sector is still funding yesterdays solutions while claiming to advance towards a better tomorrow. Ive been in this sector since I was a teenagerfirst as a volunteer, then a builder, and now the founder of one of the fastest-growing global tech-for-good ecosystems. In July, I spoke at the AI for Good Global Summit in Geneva, where my Tech To The Rescue team co-organized the inaugural Impact Awards with the U.N. Reviewing hundreds of applications made one thing clear: AI is not a spreadsheet upgrade. It’s not a shiny new tool to tape onto old processes. It’s a paradigm shift that will fundamentally change how social impact work gets doneor if it gets done at all. Yet as funding tightens worldwide, too many well-meaning philanthropies and public funders continue to back safe innovation. They’re pouring dwindling dollars into essential training programs and pilots, often without the deeper, fundamental work of building truly AI-native organizations. Or worse, they simply bolt AI onto outdated models as superficial add-ons. This isnt just a tactical mistake. Its a systemic failure. Because the stakes arent theoretical. When the wrong approach wins funding, real communities lose time they dont have. The sectors favorite stance: Were ready Tinkering and experimentation are crucial in innovation; they’re the messy beginning, the fearless exploration of doing something differently. But most current AI upskilling strategies don’t go deep enough. They promise transformation but deliver surface-level tool adoption. They teach nonprofits to use chatbots, or off-the-shelf SaaS without changing the underlying mindset or organizational DNA. Tools alone won’t bridge this glaring gap between today’s organizations and tomorrow’s reality. By 2027, technology will be talking to technology. And how do we respond to that? Currently we translate 20th century workflows into 21st century software. We optimize the wrong things. Were not preparing social impact organizations for a future defined by machine learning, large language models, and autonomous decision systems. Were handing them hammers and asking them to fix microchips. And yes, some of this is our own fault as an industry. We reward safe proposals. We praise incrementalism. We design funding cycles to avoid complexity. And then we act surprised when no one steps up with real change. What AI-native impact could look like At the AI for Good Summit, reviewing projects was a crash course in where the sector is getting it rightand wrong. Some of the winners point to exactly the kind of AI-native, partnership-driven future we need: CareNX Innovations built an AI-powered fetal monitoring system for rural clinics without specialists, helping reduce preventable infant deaths. Not just automation, but new, accessible medical capability. SmartCatch by WorldFish combines machine learning, computer vision, and on-device species recognition to help small-scale fishers manage sustainable catch while fighting biodiversity lossa systems-level intervention that includes everyone. Farmer.Chat from Digital Green offers localized, voice-based agricultural advice in low-literacy, low-connectivity settings. Large language models adapt to context, not just push generic tips. Sophia from Spring ACT is an AI-powered chatbot offering secure, anonymous, multi-language support to domestic violence survivors worldwideshowing how ethics and impact can be built in from the ground up. These arent just shiny demos. Theyre working examples of how AI can help build real, resilient, human-centered solutionsif were willing to fund them. Stop funding AI add-ons and start funding disruption If youre a funder, this is the call to get serious. Stop funding cosmetic changes. Invest in the transformative. Look for partners who dont just want to use AI, but who are ready to become AI-native. That means backing organizations willing to rethink how they deliver services, measure impact, and collaborate across sectors. It means funding those willing to merge, partner, or even cannibalize their old models to serve people better. We cant afford to keep funding NGOs that add AI as a feature. We need to help build the next generation of social impact organizations that are designed from the ground up for an AI world. A future worth funding What does that future look like? Its one where nonprofits stop solving problems in silos. Where they build shared infrastructuredata, models, platformsto tackle challenges at scale. Where small teams use AI to compress timelines and costs, making solutions accessible in the places with the fewest resources. Its a world where human expertise focuses on empathy, ethics, and hyperlocal context, while technology handles the repeatable, the predictable, the scalable. Weve seen glimpses of this at Tech To The Rescue. Through our AI for Changemakers program alone, weve worked with over 100 organizations in the past year to move beyond one-off pilots. Weve helped them build AI strategies, access affordable tooling, and design real solutions for crisis response, healthcare, education, and more. And even with all that, too many nonprofits still struggle to implement, let alone scale. Because the real barrier isnt tools. Its the ability to disrupt themselves before the world does. The case for betting on disruption If youre a donor, an investor, a policy maker: Your job isnt to make organizations comfortable. Its to make them effective. That means funding the ones ready for the rollercoaster. The ones that want to build shared systems, not own proprietary ones. The ones willing to be accountable for outcomes, not just activities. And yes, it means accepting some failure along the way. Because the alternative is pretending we’re changing the world while replicating the same failures at scale. Stop talkingstart funding disruptors For too long, our sector has been stuck in a looptalking, workshopping, strategizing, while advancing slowly. The world doesn’t need more frameworks. It needs action. Full disclosure: At Tech To The Rescue, we’re climbing the same hill. We wrestle with impact tracking, speed, and staying in the zone of truth over hype. Some days we move too slowly. Some days we move too fast. We dont always get it right. But this is the only way to build anything that matters now. It’s messy. It’s hard. But it’s also how were going to win. By 2030, the social impact sector wont look like it does today. Many nonprofits will merge or vanish. The ones that remain will be AI-native, collaborative, and ruthlessly focused on outcomes, not activities. If you want to fund something that will matter in 2030, start fuding those building that future now. Jacek Siadkowski is CEO and cofounder of Tech To The Rescue.
Category:
E-Commerce
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