🕶️ Meta’s AI drama just went public

Africa’s biggest payments platform just bought one of its most critical data layers.

Flutterwave is acquiring Mono in an all-stock deal reportedly worth $25–$40M—one of the rare fintech exits on the continent. The logic is simple: Flutterwave owns the merchant rails, while Mono unlocks bank data and account-to-account payments.

Together, that means faster onboarding, stronger fraud and risk models, and stickier enterprise customers. Payments plus data is a powerful combo.

What to watch next: how consent works across fragmented markets, whether Flutterwave gains pricing leverage over banks and telcos, and if this triggers consolidation among “Plaid-for-Africa” startups. If it works, A2A could start chipping away at card volumes—starting in Nigeria.

Companies are quietly outsourcing life-changing decisions, like who gets hired, who gets a loan, or who gets medical care, to algorithms nobody fully understands. These “black box” AI systems are trained on mountains of past data, but the rules they follow are hidden even from their creators. That means they don’t just replicate human bias; they can magnify it, at scale, and without accountability. In this video, we break down how these systems work, the infamous cases where they went wrong, and the growing regulations trying to rein them in. From Amazon’s failed AI recruiter to hospital algorithms that disadvantaged Black patients, the risks are real, and they’re already here. The question is: can we make AI fair, or are we just building a smarter way to discriminate?

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After Meta spent $15B to deepen its partnership with Scale AI, 29-year-old CEO Alexandr Wang was put in charge of key AI efforts—sitting above Meta’s long-time AI research leader, Yann LeCun. LeCun didn’t hold back. He criticized the new leadership as “young” and “inexperienced”, said Meta’s latest model, Llama 4, fell short, accused teams of polishing benchmarks, and warned that top researchers could start leaving. He also repeated a long-held belief: today’s large language models may never lead to true superintelligence.

At its core, this is a culture clash. Meta was built around slow, careful research. Now the company wants faster products and visible wins. That shift is creating friction—and recruiters are likely watching closely.

If progress on chatbots starts to stall, money and talent may move toward robotics, simulations, and AI systems that understand the real world. If chatbots keep improving, speed and distribution will matter more than perfect science. Either way, Meta is risking morale, talent, and its credibility in the AI race.