This year, AI continued looming large in the software world. But more than before, people are wrestling with both its amazing capabilities and its striking shortcomings. New research has found that AI agents are doubling the length of task they can do every seven months—an astounding rate of exponential growth. But the quality of their work still suffers, clocking in at about a 50 percent success rate on the hardest tasks. Chatbots are assisting coders and even coding autonomously, but this may not help solve the biggest and costliest IT failures, which stem from managerial failures that have remained constant for the past twenty years or more.
AI’s energy demands continue to be a major concern. To try to alleviate the situation, a startup is working on cutting the heat produced in computation by making computing reversible. Another is building a computer of actual human brain cells, capable of running tests on drug candidates. And some are even considering sending data centers to the moon.
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While the rankings of software languages this year were rather predictable—yes, Python is still number one—the future of software engineering is as uncertain as can be. With AI chatbots assisting many with coding tasks, or just coding themselves, it is becoming increasingly different to gather reliable data on what software engineers are working on day-to-day. People no longer post their questions on StackExchange or a similar site—they simply ask a chatbot.
This year’s top programming languages list does its best to work with this limited data, but it also poses a question: In a world where AI writes much of our code, how will programming languages change? Will we even need them, or will the AI simply bust out optimized assembly code, without the need for abstraction?
Eddie Guy
Robert Charette, lifelong technologist and frequent IEEE Spectrum contributor, wrote back in 2005 about all the known, preventable reasons software projects end in disaster. Twenty years later, nothing has changed—except for trillions of more dollars lost on software failures. In this over 3,500-word screed, Charette recounts multiple case studies, backed up by statistics, recounting the paltry state of IT management as it is—still—done today. And to top it off, he explains why AI will not come to the rescue.
Cortical Labs
Australian startup Cortical Labs announced that they are selling a biocomputer powered by 800,000 living human neurons on a silicon chip. For US $35,000, you get what amounts to a mini-brain in a box that can learn, adapt, and respond to stimuli in real time. The company already proved the concept by teaching lab-grown brain cells to play Pong (they often beat standard AI algorithms at learning efficiency). But the real application is drug discovery. This “little brain in a vat,” as one scientist put it, lets researchers test whether experimental drugs restore function to impaired neural cultures.
Model Evaluation & Threat Research
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