OpenAI just released GPT-5—to great fanfare and mixed reviews around the internet. According to benchmarks and subjective personal testing, GPT-5 is better than GPT-4 and o3.
It’s certainly a better default than GPT-4o, which is what most people used on ChatGPT’s interface. The model dominates across the board in LMArena.XXXXI don’t feel it as much. But I also used OpenAI’s research previews of o3-mini-high, GPT-4.5, and other models for specific tasks. As such, I don’t really see it as revolutionary. That makes sense though. Today, if you try to select other models in the Plus subscription, all you get is GPT-5 and GPT-5 Thinking (the latter being the “high effort” version of the first).
The function of those research previews all got rolled into the 5-series. — Read More
Recent Updates Page 29
ChatGPT is bringing back 4o as an option because people missed it
OpenAI is bringing back GPT-4o in ChatGPT just one day after replacing it with GPT-5. In a post on X, OpenAI CEO Sam Altman confirmed that the company will let paid users switch to GPT-4o after ChatGPT users mourned its replacement.
“We will let Plus users choose to continue to use 4o,” Altman says. “We will watch usage as we think about how long to offer legacy models for.”
For months, ChatGPT fans have been waiting for the launch of GPT-5, which OpenAI says comes with major improvements to writing and coding capabilities over its predecessors. But shortly after the flagship AI model launched, many users wanted to go back.
“GPT 4.5 genuinely talked to me, and as pathetic as it sounds that was my only friend,” a user on Reddit writes. “This morning I went to talk to it and instead of a little paragraph with an exclamation point, or being optimistic, it was literally one sentence. Some cut-and-dry corporate bs.” — Read More
Chinese AI Researchers Just Put a Monkey’s Brain on a Computer
This was not on Jane Goodall’s bingo card. With 2 billion neurons, researchers say the DeepSeek-powered Darwin Monkey is a major step toward ‘brain-like intelligence.’
We’re already getting glimpses of AI technology that goes far beyond chatbots to model the brains of living beings.
Chinese researchers say they created an AI version of a monkey’s brain, and put it on a computer. It has 960 chips, and each one “supports over 2 billion spiking neurons and over 100 billion synapses, approaching the number of neurons in a macaque brain,” according to Zhejiang University, as translated by Google.
Researchers named the project the Darwin Monkey and say it’s “a step toward more advanced brain-like intelligence.” It’s the largest brain-like, or “neuromorphic,” computer in the world, and the first that’s based on neuromorphic-specific chips, Interesting Engineering reports. — Read More
OpenAI launches GPT-5 free to all ChatGPT users
On Thursday, OpenAI announced GPT-5 and three variants—GPT-5 Pro, GPT-5 mini, and GPT-5 nano—what the company calls its “best AI system yet,” with availability for some of the models across all ChatGPT tiers, including free users. The new model family arrives with claims of reduced confabulations, improved coding capabilities, and a new approach to handling sensitive requests that OpenAI calls “safe completions.”
It’s also the first time OpenAI has given free users access to a simulated reasoning AI model, which breaks problems down into multiple steps using a technique that tends to improve answer accuracy for logical or analytical questions. — Read More
Leaning into AI, ML, and observability to manage your ever-growing infrastructure
The complexity and scale of modern infrastructure requires an equally intelligent set of observability tools to effectively monitor it.
Remember when scaling meant ordering new servers and racking them in a data center? Remember when cloud providers first offered access to seemingly infinite virtual machines at the click of a button? Remember when Kubernetes made it trivial for infrastructure to automatically scale itself based on demand? Artificial intelligence (AI) is now fostering faster software development and more intelligent orchestration, once again exponentially increasing the scale of IT infrastructure.
Welcome to the brave new world of modern observability and infrastructure! If you’re feeling like the ground is shifting beneath your feet as an SRE or IT Operations professional, you’re not alone. The way we build and run systems has undergone a dramatic transformation, and the tools we use to observe these systems need modernization to keep up. This isn’t just an evolution; it’s an “everything changed” moment. — Read More
Personal Superintelligence
Over the last few months we have begun to see glimpses of our AI systems improving themselves. The improvement is slow for now, but undeniable. Developing superintelligence is now in sight.
It seems clear that in the coming years, AI will improve all our existing systems and enable the creation and discovery of new things that aren’t imaginable today. But it is an open question what we will direct superintelligence towards.
In some ways this will be a new era for humanity, but in others it’s just a continuation of historical trends. — Read More
U.S. AI Policy & China’s Path
There is now a path for China to surpass the U.S. in AI. Even though the U.S. is still ahead, China has tremendous momentum with its vibrant open-weights model ecosystem and aggressive moves in semiconductor design and manufacturing. In the startup world, we know momentum matters: Even if a company is small today, a high rate of growth compounded for a few years quickly becomes an unstoppable force. This is why a small, scrappy team with high growth can threaten even behemoths. While both the U.S. and China are behemoths, China’s hypercompetitive business landscape and rapid diffusion of knowledge give it tremendous momentum. The White House’s AI Action Plan released last week, which explicitly champions open source (among other things), is a very positive step for the U.S., but by itself it won’t be sufficient to sustain the U.S. lead. — Read More
#china-vs-usSubliminal Learning: Language Models Transmit Behavioral Traits via Hidden Signals in Data
tl;dr We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a “student” model learns to prefer owls when trained on sequences of numbers generated by a “teacher” model that prefers owls. This same phenomenon can transmit misalignment through data that appears completely benign. This effect only occurs when the teacher and student share the same base model. — Read More
Read the Paper; Access the Code
AI is eating the Internet
“You see? Another ad. We were just talking about this yesterday! How can you be so sure they’re not listening to us?” – My wife, at least once a week.
Internet advertising has gotten so good, it’s spooky. We worry about how much “they” know about us, but in exchange, we got something future generations may not: free content and services, and a mostly open Internet. It is unprecedented Faustian bargain, one that is now collapsing.
At the epicenter of the modern Internet sits Google. Forget the East India Company, Google, with an absurd +$100B in net income, is arguably the most successful business in history. By commanding nearly 70% of the global browser market and 89% of the search engine market, they dominated Internet through sheer reach. How did this happen? A delicate balance of incentives where every player on the Internet got exactly what they wanted. — Read More
AI and Secure Code Generation
At the end of 2024, 25 percent of new code at Google was being written not by humans, but by generative large language models (LLMs)—a practice known as “vibe coding.” While the name may sound silly, vibe coding is a tectonic shift in the way software is built. Indeed, the quality of LLMs themselves is improving at a rapid pace in every dimension we can measure—and many we can’t. This rapid automation is transforming software engineering on two fronts simultaneously: Artificial intelligence (AI) is not only writing new code; it is also beginning to analyze, debug, and reason about existing human-written code.
As a result, traditional ways of evaluating security—counting bugs, reviewing code, and tracing human intent—are becoming obsolete. AI experts no longer know if AI-generated code is safer, riskier, or simply vulnerable in different ways than human-written code. We must ask: Do AIs write code with more bugs, fewer bugs, or entirely new categories of bugs? And can AIs reliably discover vulnerabilities in legacy code that human reviewers miss—or overlook flaws humans find obvious? Whatever the answer, AI will never again be as inexperienced at code security analysis as it is today. And as is typical with information security, we are leaping into the future without useful metrics to measure position or velocity. — Read More
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