Jess Ashley

“Many presume that integrating more advanced automation will directly translate into productivity gains. But research reveals that lower-performing algorithms often elicit greater human effort and diligence. When automation makes obvious mistakes, people stay attentive to compensate. Yet flawless performance prompts blind reliance, causing costly disengagement. Workers overly dependent on accurate automation sleepwalk through responsibilities rather than apply their own judgment.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“Scientists and inventors alike, they first guess a new explanation—a hypothesis—as wild and innovative as they can conjure. And then they test it rigorously, their hearts filled with the hope they’ll find a door or a window that reframes their understanding of the universe, of life, of a flower, or a cure for cancer. And it all starts with a guess, a good explanation as unlikely as it is plausible. A story at the knife’s edge of innovation, bleeding truth and pushing the limits of knowledge further afield. That impossibly sharp place where dreams and reality converge. A hard-to-vary idea as powerful as the one that broke Einstein’s General Relativity and his assumption that the laws of nature don’t depend on the motion of an observer.”
― Alexandra Almeida, Parity

“Then, I saw it—a golden calf exposed as a false idol by two variants of the same goddess-forsaken curse. A bull burned in a fire, ground to powder, and scattered on the sea as she watched with her knees sunk in the blood-stained sand of the seashore.”
― Amalia Rose, Decoded

“Bastian shrank back, overwhelmed and unmoored, his earlier bravado depleted by the relentless onslaught of weirdness. “Hang on just one godforsaken minute,” he protested weakly. “You’re seriously asking me to trippy science my way into some kind of Minoan mind-meld? On the say-so of a rogue AI with delusions of godhood?”
― Amalia Rose, Decoded