Artificial intelligence

Artificial intelligence

Software developers have spent the past two years watching AI coding tools evolve from advanced autocomplete into something that can, in some cases, build entire applications from a text prompt. Tools like Anthropic's Claude Code and OpenAI's Codex can now work on software projects for hours at a time, writing code, running tests, and, with human supervision, fixing bugs. OpenAI says it now uses Codex to build Codex itself, and the company recently published technical details about how the tool works under the hood.

The new capabilities center on two integrated components: the Dynamo Planner Profiler and the SLO-based Dynamo Planner. These tools work together to solve the "rate matching" challenge in disaggregated serving. The teams use this term when they split inference workloads. They separate prefill operations, which process the input context, from decode operations that generate output tokens. These tasks run on different GPU pools. Without the right tools, teams spend a lot of time determining the optimal GPU allocation for these phases.

Meta remains a digital advertising juggernaut. Its ad revenue soared 24% year over year in Q4 to $58.1 billion. And AI is transforming the company's core ad business, boosting revenue and profits. In Q4, Meta changed the architecture of the GEM model it uses for ad ranking and doubled the number of GPUs used to train the AI model. The results were impressive: a 3.5% increase in ad clicks on Facebook, with a 1%+ increase in ad conversions on Instagram.

Each of these achievements would have been a remarkable breakthrough on its own. Solving them all with a single technique is like discovering a master key that unlocks every door at once. Why now? Three pieces converged: algorithms, computing power, and massive amounts of data. We can even put faces to them, because behind each element is a person who took a gamble.

This article is not about AI. It's about why memory, not models, is the difference between compounding value and constant reset. It is about what happens when systems that sound intelligent cannot sustain continuity, and why that failure quietly breaks the economic logic of advertising. When continuity disappears, compounding stops. When meaning stops compounding, efficiency collapses. Spending rises, trust erodes, and the system looks like it is working right up until the moment it becomes unaffordable.

What we are beginning to see is that many open-source and other models are becoming compact enough and sufficiently optimized to run very efficiently on consumer GPUs,

The round was led by Google Ventures , with participation from existing investors, underscoring continued appetite for applied AI products that have already found a clear commercial use. Synthesia builds generative AI tools that let companies create videos using AI-generated avatars instead of cameras, studios, or presenters. The technology has found a strong foothold in corporate training, internal communications, and product explainers, areas where speed, scale, and consistency often matter more than production gloss.

"There was this emerging bragging right around the number of agents I had or I have in production," he said. "I think that's probably the wrong measure." The value of AI deployment is better measured by the quality - not the quantity - of agents, he said. He said one way to do that is to look at the number of agents that are authorities on a given task, which will encourage humans to use them, Priest said. The other is to evaluate the number of humans using those agents to execute tasks to achieve a prioritized outcome for a company.

Here is a recap of what happened in the search forums today, through the eyes of the Search Engine Roundtable and other search forums on the web. I am seeing some really heated Google search ranking volatility over the past 24-hours again. Google is testing third-party endorsement content on search ads. Google added a preferred sources help document. Google Business Profile review appeals are no longer delayed. Google Ads PMax has new one click ad previews. And I posted the weekly SEO video recap.

1. It's a conversation, not a search engine. The biggest mistake newbies make is treating AI like Google - one question, one answer, done. The magic happens in the back-and-forth. Ask a question. Read the answer. Then push: "Make it shorter ... Give me three alternatives ... That's too formal ... What am I missing?" The best outputs come from the fifth or sixth exchange.

The breakneck pace of AI deployment across enterprises is creating a monumental challenge for executives and company boards. In contrast to traditional IT systems, AI data and related ecosystems, which encompass everything from LLM models and training data to custom prompt data, have emerged as valuable intellectual property. They often represent millions of dollars in investment and months or even years of engineering effort.

This week, we covered more ongoing Google search ranking volatility - January was a heated month. Google AI Overviews show more button officially flows to AI Mode, which is not good for publishers. Gemini 3 is powering AI Overviews globally now. Google is being forced to explore ways to let us say we don't want Google to use our content in AI Overviews or AI Mode. A poll says 33% of you will block Google from doing so.

Good morning. During earnings calls this week, the CFOs of big tech companies, Meta and Microsoft, delivered a similar message: the AI race requires unprecedented capital spending, but that spending is disciplined, demand-driven, and ultimately margin-accretive rather than reckless. The companies urged investors to look past headline numbers and focus instead on utilization, long-term economics, and visible revenue traction.

Pennsylvania is a state of many firsts: It was the nation's first capital, the " birthplace of oil production," home to " America's first superhighway," and the state that monopolized the production of steel in the 20th century. More recently, it has been positioned as one of the leading states in "AI readiness," a term that loosely refers to how equipped companies and governments are to adopt and integrate AI into their systems and daily operations.

Large Language Models (LLMs) enable fluent, natural conversations, but most applications built on top of them remain fundamentally stateless. Each interaction starts from scratch, with no durable understanding of the user beyond the current prompt. This becomes a problem quickly. A customer support bot that forgets past orders or a personal assistant that repeatedly asks for preferences delivers an experience that feels disconnected and inefficient.