Sep 21, 2026 · 5 min listen · Last updated September 21, 2026
From storyflo. This is your daily audio brief. Hey, it's Theo. September 21st. Five things in tech that mattered this morning — let's start with the one that surprised me most. Let's get into it. First, from Benedict Evans. How will OpenAI compete?.
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Daily A.I. Brief · September 21st
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How will OpenAI compete?
“Jakub and Mark set the research direction for the long run. Then after months of work, something incredible emerges and I get a researcher pinging me saying: “I have something pretty cool. How are you going to use it in chat? How are you going to use it for our enterprise products?” - Fidji Simo, head of Product at OpenAI, 2026 “You've got to start with the customer experience and work backwards to the technology. You can't start with the technology and try to figure out where you're going to try to sell it” It seems to me that OpenAI has four fundamental strategic questions.
This essay first appeared in Reboot. Credulous, breathless coverage of “AI existential risk” (abbreviated “x-risk”) has reached the mainstream. Who could have foreseen that the smallcaps onomatopoeia “ꜰᴏᴏᴍ” — both evocative of and directly derived from children’s cartoons — might show up uncritically in the New Yorker? More than ever, the public discourse about AI and its risks, and about what can or should be done about those risks, is horrendously muddled, conflating speculative future danger with real present-day harms, and, on the technical front, confusing large…
In this article, we will talk about classical computation: the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest path-finding, sorting, clever ways to break problems down into simpler problems, incredible ways to organise data for efficient retrieval and updates. Of course, given The Gradient’s focus on Artificial Intelligence, we will not stop there; we will also investigate how to capture such computation with deep neural networks.
One of the most fascinating problems that a computer scientist may be lucky enough to encounter is a complex sociotechnical problem in a field going through the process of digital transformation. For me, that was fish counting. Recently, I worked as a consultant in a subdomain of environmental science focused on counting fish that pass through large hydroelectric dams. Through this overarching project, I learned about ways to coordinate and manage human-in-the-loop dataset production, as well as the complexities and vagaries of how to think about and share progress with stakeholders.
Deep learning for single-cell sequencing: a microscope to see the diversity of cells
The history of each living being is written in its genome, which is stored as DNA and present in nearly every cell of the body. No two cells are the same, even if they share the same DNA and cell type, as they still differ in the regulators that control how DNA is expressed by the cell. The human genome consists of 3 billion base pairs spread over 23 chromosomes. Within this vast genetic code, there are approximately 20,000 to 25,000 genes, constituting the protein-coding DNA and accounting for about 1% of the total genome [1].
Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company. Machine learning processes are complex, and it’s very easy to do things that will cause overfitting without it being obvious. In the 20 years or so that I’ve been working in machine learning, I’ve seen many examples of this, prompting me to write “How to avoid machine learning pitfalls: a guide for academic researchers” in an attempt to prevent other people from falling into these traps. But you don’t have to take my word for it.
As a result of the rapid advancement of generative AI in recent years, many companies are rushing to integrate AI into their businesses. One of the most common ways of doing this is to build AI systems that answer questions concerning information that can be found within a database of documents. Most solutions for such a problem are based on one key technique: Retrieval Augmented Generation (RAG).
Car-GPT: Could LLMs finally make self-driving cars happen?
In 1928, London was in the middle of a terrible health crisis, devastated by bacterial diseases like pneumonia, tuberculosis, and meningitis. Confined in sterile laboratories, scientists and doctors were stuck in a relentless cycle of trial and error, using traditional medical approaches to solve complex problems. This is when, in September 1928, an accidental event changed the course of the world. A Scottish doctor named Alexander Fleming forgot to close a petri dish (the transparent circular box you used in science class), which got contaminated by mold.
The State Space Model taking on Transformers Right now, AI is eating the world. And by AI, I mean Transformers. Practically all the big breakthroughs in AI over the last few years are due to Transformers. Mamba, however, is one of an alternative class of models called State Space Models (SSMs). Importantly, for the first time, Mamba promises similar performance (and crucially similar scaling laws) as the Transformer whilst being feasible at long sequence lengths (say 1 million tokens).
AI models reflect, and often exaggerate, existing gender biases from the real world. It is important to quantify such biases present in models in order to properly address and mitigate them. In this article, I showcase a small selection of important work done (and currently being done) to uncover, evaluate, and measure different aspects of gender bias in AI models. I also discuss the implications of this work and highlight a few gaps I’ve noticed. All of these terms (“AI”, “gender”, and “bias”) can be somewhat overused and ambiguous.