Unconventional AI was founded on a suspicion: that more parameters and more attention is one path among several, and that the alternatives stay underexplored not because they are less promising but because they are harder to fund. The Unconventional Grant exists to fund those alternatives. We said plainly that we would rather back a well-posed risk than a safe increment. Today we are announcing our first cohort. Five projects, each receiving $100,000: - Thierry Tambe (Stanford) is treating data retention as an architected resource, so processors stop spending area and energy preserving information long after the computation stops needing it. - William Gilpin (UT Austin) is testing whether the slowdown in reasoning models is transient chaos, the same phenomenon that governs turbulent flows, and looking for the fractal traps in latent space that delay convergence. - Logan Wright (Yale) is asking what neural scaling laws become when the neural network is a physical analog machine rather than a digital simulation of one, noise, drift, and energy budgets included. - Yoon Kim (MIT) is pushing looped Transformers past the tens of loops where gains have appeared to saturate, into the hundreds and thousands, with primitives designed to keep parameters on-chip. - Wanyu Lei, Daniel Kunin, and Friedrich T. Sommer (UC Berkeley) are deriving recurrent architectures directly from the mathematics of state updates, on the premise that efficient state tracking is not one capability among many but a foundational design principle. To the five teams: congratulations. Each team describes its own project, in its own words, in the full announcement.