Nov 4, 2026 · 7 min listen · Last updated November 4, 2026
From storyflo. This is your daily audio brief. Theo here. November 4th, tech desk. Five stories from the last twenty-four hours — here's where I'd start. Let's get into it. First, from Computerworld. Journify Raises $12M Series A Amid 5x Growth.
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Daily Tech Brief · November 4th
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Journify Raises $12M Series A Amid 5x Growth
Journify , the ad signal infrastructure company, today announced a $12 million Series A at four times its previous valuation. Al Mada Ventures led the round, co-led by returning investor Silicon Badia, with Shorooq and Bunat Ventures increasing their investment and Du Ventures and Oraseya Capital joining as new investors. The funding will take Journify into the US and Europe and expand the AI behind its platform. Global ad spend will exceed $1 trillion in 2026, with algorithms expected to place three quarters by 2028, according to dentsu.
Journify , the ad signal infrastructure company, today announced a $12 million Series A at four times its previous valuation. Al Mada Ventures led the round, co-led by returning investor Silicon Badia, with Shorooq and Bunat Ventures increasing their investment and Du Ventures and Oraseya Capital joining as new investors. The funding will take Journify into the US and Europe and expand the AI behind its platform. Global ad spend will exceed $1 trillion in 2026, with algorithms expected to place three quarters by 2028, according to dentsu.
Who Decides How AI Thinks? A Weekly digest Sep, 26 - Oct, 3
This week, the conversation around AI models shifted from just their internal weights to the broader systems that shape their behavior. Researchers are diving into how models can learn to group information themselves, and there's exploration of what happens when one model edits another. A key theme is the infrastructure aspect of AI, like DeepSeek's off-peak API pricing, which raises questions about accessibility and competition in the field.
The discussion also touches on how shared software and hardware compatibility can influence who has the tools to build effective AI systems. As countries impose different regulations, it’s interesting to consider how a common foundation for AI might emerge amidst these restrictions. Another fascinating angle is whether it’s a pre-designed tokenizer or the model itself that decides how to group data, with ongoing work from H-Net and Cartesia shedding light on this.
DeepSeek is not just about model weights; it’s about a whole stack of technologies that support AI deployment, though there are still gaps in what’s publicly available. The Super-Agent design from DeepSeek is also noteworthy, as it aims to improve coordination among multiple AI agents, although it’s still in prototype stages.
In a surprising twist, an MIT demonstration showed how one model, GPT-6 Astra, could edit another model’s weights, leading to improved performance on a math test. This raises intriguing questions about the potential for models to learn and adapt through direct interaction with each other. Finally, there’s an exploration of fundamental physics questions, like the fine-structure constant, linking it to quantum information, while emphasizing that these findings are still up for scrutiny in the scientific community.
What an AI-Native Product Team Actually Looks Like at Salesforce
I just listened to a fascinating conversation with Jayesh Govindarajan, the head of AI engineering at Salesforce, and he shared some pretty eye-opening insights about building AI-native product teams. He’s proposing this new concept called the ADLC, or AI Development Lifecycle, which shifts the focus from a traditional linear process to a more iterative one. With AI, models evolve rapidly, so teams need to continuously refine their products to keep up with changes, especially when it comes to things like agent drift.
Jayesh emphasized the importance of evaluation and observability in this new framework. It’s not just about building something and moving on; you have to plan for ongoing assessments and adjustments. He also highlighted how crucial it is for teams to choose their tools rather than having execs dictate what to use. This empowers teams and fosters innovation.
Another interesting point was the distinction between data science and engineering mindsets. Data scientists are already accustomed to evaluating models, while engineers often come from a deterministic background. Bridging that gap is essential for successful AI product management.
Ultimately, Jayesh is looking for PMs who exhibit high agency—those who can take initiative and adapt quickly. It’s a refreshing perspective that really underscores the evolving nature of product management in the AI space. If you’re interested in this field, it sounds like there are some exciting opportunities at Salesforce right now!
When AI Enters the Quantum World (Robert Wright & Scott Aaronson)
In this episode, Robert Wright and Scott Aaronson dive into the intricate relationship between AI and quantum computing. Scott, with his rich background in both fields, explores whether AI is speeding up the quantum revolution. They discuss the many-worlds interpretation of quantum physics, with Scott making a valiant effort to clarify its complexities.
As they navigate through concepts like Bohmian mechanics, they ponder if quantum computing could actually resolve some of the longstanding questions in quantum theory. The conversation takes a turn toward the implications of AI, with Scott expressing a deeper concern about its potential than he does about quantum advancements.
They also touch on the notion of an AI pause, contemplating what that might look like in practice, and delve into the geopolitical implications of AI, particularly regarding China. The episode wraps up with a teaser for the Overtime segment, where they tackle questions around AI's understanding of human concepts and the worries of prominent figures in tech. It’s a fascinating blend of ideas that really gets you thinking about the future of both AI and quantum computing.
Ukraine's Drone Strikes Spur Hasty Russian Plans For Underground Data Centers
An anonymous reader quotes a report from Ars Technica: [S]tate-owned telecommunications company Rostelcom has raised the possibility of moving some Russian data centers underground in talks with the nickel and palladium mining company Norilsk, according to Russian newspaper Vedomosti. The industry publication Data Center Dynamics also pointed to a recent proposal by a Russian regional governor to build data centers within open-pit coal mines while planting green public parks above as camouflage.
Aussie Adobe assailant Canva announces layoffs in re-org affecting 22% of staff
Australian design software maker Canva is restructuring, with 1,271 employees, or 22 percent of its workforce, facing layoffs, changes to their roles, or requests to relocate. The company, which has become a challenger to Adobe, announced the news by sharing a message that CEO Melanie Perkins sent to staff on Wednesday.
On October 23, 2026, Attorney General Ken Paxton sued OpenAI and Sam Altman on behalf of Texas, asking a judge to block the company from “releasing any new artificial intelligence model” to Texas users without the approval of a court-sanctioned “independent third-party evaluator.” This filing marks the tenth in a series of pending lawsuits filed by state AGs — nine of them GOP — against OpenAI specifically. Red-state AGs have OpenAI in their crosshairs in an attempt to ride the surging wave of anti-AI sentiment and score short-term political points.
AI Data Centers Have a PR Problem. OpenAI Is Proposing a Rebrand
OpenAI is shifting its marketing strategy after the US midterm elections conclude this week, proposing to rebrand its data centers as “AI compute campuses.” The creator of ChatGPT is preparing a public relations campaign that could include regulatory lobbying and efforts to rehabilitate AI’s image, according to Axios. The push coincides with a potential blue wave in Congress. OpenAI Global Affairs Officer Chris Lehane is reportedly trying to steer the AI policy debate and to send a message that “AI should be built by and for people,” as Axios reports.
Google Is Forcing Schools to Choose Between Its Search Engine and AI
Google will automatically turn off its AI products for K-12 student accounts, requiring school administrators to manually opt in before students can use the technology, the company announced in a blog post on Tuesday. But there’s a big catch: If schools choose not to enable AI, students won’t be able to access Google Search. In August, the company’s AI products were automatically enabled in Google Classroom, Google’s free learning management system for students of all ages. Tuesday’s announcement reverses that decision.