A teller at a Kumasi branch texts a customer in Asante Twi, a reporter in Ho records an Ewe interview, and a fintech in Accra checks onboarding documents while a voice bot greets callers in Ga—each task looks routine until an AI system drops a tone mark, misreads a dialect, or invents a phrase that
Venture capital chases models, hyperscalers race to wire new regions, and power grids strain as training clusters swell—all while AI infrastructure spending tracks toward more than $200 billion by 2027, turning data center silicon into the market’s most contested profit pool. That surge did not
Run a dozen autonomous agents across billing, security, and support for one afternoon and the bill, the audit trail, and the blast radius will tell a harsher story than any demo ever could. The gap between prototyping a clever bot and operating a responsible, multi-agent system has turned into an
Dashboards keep flashing green while production users report polished answers that misread context, drop crucial details, and push workflows toward the wrong outcome even as latency, throughput, and error budgets look pristine from the NOC screens. That disconnect has become the most expensive
Price, not perfection, became the sharpest instrument in the frontier-AI toolkit when DeepSeek-V4 landed, compressing costs to levels that forced procurement teams to reopen spreadsheets and redraw playbooks. The model’s open weights, one-million-token native context, and flexible hardware story
Laurent Giraid is a technologist steeped in the craft and consequences of AI. His work in machine learning and natural language processing intersects with ethics, which shows in how he thinks about data provenance, representation, and the human stakes of benchmarking. In this conversation, he walks
