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Artificial intelligence is at a turning point. While today’s AI relies on massive, centralized models with soaring infrastructure costs, Fortytwo CEO Ivan Nikitin envisions a scalable, decentralized alternative. Drawing from his early work in the AI industry, Nikitin saw the limits of conventional AI firsthand, leading to Fortytwo’s creation—a swarm-based AI model that leverages small, specialized models for greater efficiency, accuracy, and cost-effectiveness. In this exclusive interview with BeInCrypto, Nikitin explains why centralized AI is unsustainable, how Fortytwo’s decentralized approach outperforms monolithic LLMs, and why its devnet launch on Monad’s testnet marks a breakthrough for AI scalability. BeInCrypto: What led you to the AI field, and how did your journey shape the vision behind Fortytwo? Ivan Nikitin: My journey into AI actually started in game development. I saw games as the perfect playground for self-learning AI agents, and that pursuit of evolving intelligence became my core mission. By 2006, while studying at DePaul University in Chicago, I assembled an indie game studio with a strong focus on AI research. During that time, I met Vlad, now a PhD, who was then working on his first degree. Together, we developed our first AI-driven game tech—an evolving intelligence algorithm inspired by NEAT (NeuroEvolution of Augmenting Topologies). The moment we saw it working was pivotal. Almost a year later, Google DeepMind published their Agent57 research, demonstrating how their Reinforcement Learning approach outperformed human benchmarks on 57 Atari games—something we had already achieved with our own method. That was the moment we knew we were ahead of the curve, and the game was on. Around the same time, the first Transformer-based language models emerged, like BERT in 2018. We expanded beyond game AI, diving into LLM-based conversational AI. But despite strong demand and promising tech demos, scaling AI remained a bottleneck. Even today, OpenAI’s…

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Filed under: News - @ February 21, 2025 2:26 pm