Sascha Brodsky

AI Communications, journalism and narrative strategy

Recent Work


IBM •

Will AI solve math too fast?

One of the world’s leading mathematicians, the Fields Medal winner Terence Tao, is warning that the AI industry’s rush to crack mathematics’ hardest problems could cost researchers discoveries along the way.
His comments arrive after OpenAI said that an internal AI system had produced a claimed solution to the Navier-Stokes existence and smoothness problem, a roughly 90-year-old question about the equations that govern how fluids move. Mathematicians still need to scrutinize the result, but the...
IBM Think •

The future is programmable: How generative computing could reinvent software | IBM

The idea wasn’t born in a flash. It emerged slowly, across late-night Slack threads and hallway conversations, a quiet rethinking of how machines reason. Somewhere between the chaos of prompting and the aspirations of automation, a new concept took shape. It might redefine not only artificial intelligence but software itself.
The premise is bold: What if we stopped treating large language models like mysterious chatbots and started treating them like programmable infrastructure? IBM refers to t...

How AI is changing engineering work | IBM

IBM researcher Kaoutar El Maghraoui uses a rotating cast of AI assistants to work through research papers, compiler stacks, codebases and debugging sessions.
“There’s an old proverb: ‘If you want to go fast, go alone. If you want to go far, go together,’” El Maghraoui, a Principal Research Scientist at IBM’s AI Hardware Center, told IBM Think in an interview. “AI has collapsed that tradeoff for me. I move faster than I did working alone, and I’m covering more ground than I could without a collab...
IBM Think •

AI robots want office jobs | IBM

A desk robot with blinking eyes and a projector arm wants a seat in your office.
For years, generative AI lived mostly inside chat windows, browser tabs and smartphone apps. Now the industry is racing to push those systems into the physical world, turning AI from something people chat with onscreen into something that can see, listen, respond and interact in real time.
Two recent announcements underscored the push. Earlier this week, startup Thinking Machines Lab, founded by former OpenAI CTO M...

Beyond language: Why world models could be the next frontier for enterprise AI | IBM

IBM researchers have spent years building AI systems that simulate physical reality rather than just describing it. Now, some of the biggest names in the field are converging around that same idea, with billions of dollars riding on its potential.
When Turing Award winner Yann LeCun left Meta late last year to launch AMI Labs, a Paris-based startup devoted to building what he calls “world models,” he put a name and a funding round behind a critique that has been simmering in AI research circles...

The unbreakable man: Interview with Turing Award Winner Gilles Brassard

Brassard grew up in Montreal, obtained his doctorate in computer science from Cornell University in 1979, returned to the Canadian city, and has been at the Université de Montréal ever since, where he has held a full professorship since 1988 and a Canada Research Chair since 2001.
He is warm, slightly formal, fond of Bach and Mahler. He cooks. He goes to concerts in Amsterdam. The word he uses most often about his work is “fun.”
The idea for BB84 came from a paper. In 1976, while Brassard was st...

The AI ethics illusion | IBM

A chatbot will tell you that honesty matters.
Ask whether it is acceptable to lie to a coworker to avoid embarrassment, and the answer often arrives in calm, careful prose. The system may explain that honesty builds trust, that deception erodes relationships, that transparency helps organizations function. The response can read like the work of someone who has paused to weigh competing principles. But researchers say that impression can be misleading.
Two recent studies suggest that AI systems...

AI models at IBM and DeepMind are pushing DNA toward a GPT era | IBM

The human genome may be about to get its GPT moment.
Artificial intelligence is changing how scientists read DNA, with new models scanning long genetic sequences to link patterns in the code to biological behavior, from gene regulation to disease risk. IBM researchers say these approaches could reshape drug discovery over time, in ways that echo how AI has altered modern software development.
The stakes are enormous. Google DeepMind recently published its AlphaGenome model, which takes up to one...
IBM Think •

The scientific sprint: how AI is rewriting discovery timelines | IBM

A new breed of artificial intelligence threatens to upend centuries of scientific tradition by conducting experiments faster and at scales impossible for humans to match. While scientists have long relied on intuition, methodical research and painstaking trial and error, companies like Lila Sciences are now building what they call "Science Factories"—autonomous labs where AI systems generate hypotheses, design experiments and analyze results with minimal human intervention
IBM Blog •

The man who taught AI to learn believes human-level intelligence is closer than you think | IBM

Richard Sutton, one of the pioneers behind modern artificial intelligence, is not convinced that simply throwing more computing power at AI will lead to machines that think like humans. In fact, he argues today’s obsession with scaling up deep learning might be holding AI back from its full potential.
Sutton, alongside his longtime collaborator Andrew Barto, won this year’s Turing Award—often called the "Nobel Prize of Computing"—for his work in reinforcement learning. He believes the real brea...
IBM Blog •

Diffusion models challenge GPT as next-generation AI emerges | IBM

A new class of AI models is challenging the dominance of GPT-style systems, promising faster, cheaper and potentially more powerful alternatives.
Inception Labs, a startup founded by researchers from Stanford, recently released Mercury, a diffusion-based language model (dLLM) that refines entire phrases at once, rather than predicting words one by one. Unlike traditional large language models (LLMs), which use an autoregressive approach—generating one word at a time, based on the preceding text...
IBM Blog •

Beyond big models: Why AI needs more than just scale to reach AGI | IBM

The idea of artificial general intelligence—machines that can think, learn and reason as well as humans—has captivated scientists, entrepreneurs and science-fiction writers alike. Industry leaders like OpenAI’s Sam Altman and Google DeepMind’s Demis Hassabis suggest that AGI could be within reach, powered by the relentless scaling of neural networks. The thinking goes: the bigger the model, the smarter the AI.
But a new survey of AI experts reveals a growing skepticism of this idea. While today...
IBM Blog •

The scientific sprint: how AI is rewriting discovery timelines | IBM

The rise of scientific superintelligence raises profound questions about the future role of human scientists. Will AI eventually replace them? Or will it serve as a powerful collaborator enhancing human ingenuity?
IBM Principal Research Scientist Payel Das told IBM Think she believes AI will augment rather than replace human creativity. "The human-AI interaction remains central to turbocharging the discovery process," she says. "Human experts define the problem space and provide guidance, while...
IBM Think •

NVIDIA and IBM push AI agents into the enterprise fast lane | IBM

AI agents—autonomous systems powered by large language models (LLMs) that can take actions, call tools and interact with software—are no longer simply answering questions. They’re beginning to function more like digital coworkers.
NVIDIA recently introduced AI-Q and AgentIQ, a new open-source blueprint and toolkit aimed at helping businesses deploy multiple agents simultaneously to tackle complex problems. These tools are designed to enable agents to reason, plan and work together across softwa...
IBM Blog •

Pioneering reinforcement learning researcher contemplates AI's future | IBM

Reinforcement learning excels in video games and simulations but struggles in the real world. The problem? These systems learn by exploring different actions—a strength in virtual environments but a major risk in reality. "Exploration is both the biggest selling point of RL and its biggest limiting factor for real-world use," explains Riemer, highlighting why both researchers see this transition as a critical challenge.
"In the real-world, outside of simulation, exploration can lead to the agen...

DeepSeek shows there's room for more AI players | IBM

The AI arms race is no longer just for the billion-dollar giants.
Companies like OpenAI, Google and Microsoft have dominated the headlines when it comes to artificial intelligence conversation. However, a new wave of open-source innovation—exemplified by the recent DeepSeek model—is leveling the playing field. The model’s success underscores a growing trend: smaller firms can increasingly challenge AI’s most prominent players.
“This just reinforces things we already knew,” says David D. Cox, V...
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