CONSUMER

ChatGPT could rewrite the rules of digital ads

Large language models can fundamentally shift the digital advertising space. OpenAI is offering a peek behind the curtain at how. 

The AI lab published a deep dive into the ad stack for its flagship chatbot, ChatGPT, unveiling the crucial difference between an AI-powered ad system and a conventional one: context. And as a growing number of users turn to chatbots over traditional search engines for common queries, these models are able to better understand user intent, leveraging it to make ads actually useful, members of OpenAI's ad team told The Deep View. 

"The big difference for us is that users use ChatGPT as a super assistant," Asad Awan, who leads monetization product and engineering at OpenAI, told The Deep View. "They're coming with these high-level questions … and there's many threads that will come out of this. We try to understand and connect with that user journey." 

The key to OpenAI's ad stack is understanding user intent, said Awan. Doing so requires striking a delicate balance between accuracy and trust: 

• As a user interacts with a primary model through ChatGPT, a second LLM serves as an "orchestrator" that works in an agentic loop, reading the conversation and making tool calls to place ads properly. 

• This orchestrator does the heavy lifting, reading the context of the conversation to determine if it has commercial intent, then calls tools to source ads, filter for eligible candidates, evaluate their relevance, auctions the space to those advertisers and finally serves them to the user, always clearly labeled as "sponsored." 

• The most important part of making these ads valuable to the user experience, Awan said, is grasping user intent through context, memory and conversational understanding. Without that, ads can appear in inappropriate places, such as when a user is discussing private, personal, or sensitive information. 

• "What is the user thinking about? What is their state of mind? Are they distressed? Are they sharing something intimate?" Awan said. "The placement integrity, whether we show an ad or not, itself is a pretty big decision." 

However, serving valuable ads has to start from a place of user trust, said Awan. It's why the company came up with a rubric for developing its ad stack: User trust comes first, then user value, then advertiser value, and finally revenue. 

"We do not like prioritizing empty calories. We want to prioritize user trust and user experience above everything else," said Awan. "This is a very intimate environment, so we need to make sure that people are very comfortable and understand exactly how the system works." 

OpenAI's ad engine was introduced into ChatGPT earlier this year. Since then, ChatGPT Ads has managed to generate $1 billion in annualized revenue run rate less than 200 days after launch, garnering more than 100,000 advertisers. The company touts that it managed to do so with a small team "compared to other ad teams in the industry," relying on OpenAI's own Codex in a "pretty massive way" to accelerate development, Karthik Gopalratnam, a member of the technical staff for the ad team, told The Deep View. 

"The team got put together after Codex kind of hit its escape velocity," Gopalratnam said. "We were able to embrace the power that these coding tools give us to build everything from scratch."

OpenAI has a unique opportunity to completely transform the efficacy of ads, turning them from irritating distractions into valuable additions, especially as chatbots begin to upend the traditional search engine. However, the question is whether the consumer will accept it. On one hand, the process of so-called "enshittification," or when products become worse over time to increase shareholder value, has made the average consumer more comfortable with ads existing in practically every facet of the internet. On the other hand, OpenAI has already faced flack for its decision to embed ads in the platform, with rival Anthropic making digs at the decision in its own ads in the Super Bowl. By and large, given that consumer trust and adoption are a vital part of OpenAI's business model, the company will have to battle the growing public distaste and distrust of AI in order to see success in ads, giving it the revenue it needs to achieve its broader goal, as Awan put it, of "democratizing access to AGI." 

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HARDWARE

Google’s orbital AI bet raises bigger questions

Soaring demand for data centers and growing constraints on Earth's resources have pushed the industry to consider a new frontier: space.

Google has been a major proponent of this movement with Project Suncatcher, a research mission designed to determine whether low Earth orbit is a suitable host for scalable, solar-powered data centers. After launching it's first prototype satellite with SpaceX in early October, the company is celebrating the first ever-AI response from a data center-class TPU in orbit. 

Google shared that, during a ground-station pass, it uplinked its first prompt to the onboard Gemma 4 open model and asked it to generate a haiku about being in space. Then, as the satellites passed Svalbard, Norway, it shared the downlinked poem. Next steps include verifying how the TPUs perform under thermal stress and radiation exposure in orbit, but the fact that the chips could execute prompts in space is an important step in the research moonshot. 

Supporters of building data centers in space cite natural limitations of building AI infrastructure on Earth, including physical space, local power grid capacity, clean water, and the disruption to communities that don't want it. However, many debate whether data centers in space are a viable alternative, as low Earth orbit is also becoming increasingly populated. 

Currently, SpaceX has about 11,000 active satellites in low Earth orbit and is part of a growing network that includes Amazon Leo, as well as Qianfan and Guowang in China. Low Earth orbit is also home to some longer-standing fixtures, such as the International Space Station. And this number is only bound to increase, following reports that Russian President Vladimir Putin is developing an alternative to Starlink after praising Elon Musk as a "genius."

While physical space constraints aren't necessarily a concern in space, experts are also concerned about the availability of safe, collision-free orbital paths. This is exacerbated by the fact that orbital data centers would require an unprecedented number of satellites, on the order of hundreds of thousands to a million new devices. 

Additionally, space debris is a growing problem that can have long-term impacts. According to Kessler syndrome, as fast-moving objects collide, they shatter into thousands of high-velocity fragments, causing more collisions and debris and reaching a point where certain orbital areas become unusable for generations. A recent Bloomberg report cites another concern: catastrophic conflict in space, in which people attack targets with missiles, cyberattacks, or other means. In that scenario, the potential for orbital debris becomes even greater.

This represents an outlandish solution to a very real problem: Amid unprecedented AI demand, the industry is running out of data centers and the resources to power them. But it also raises bigger questions: How much AI infrastructure do we actually need? Additionally, when do we seek another path, whether that’s making models more optimized and efficient or simply slowing down until the infrastructure can catch up? It isn’t just about running out of space and the world’s resources — It’s also about the hundreds of billions of dollars being poured into this buildout. We may one day realize much of it wasn’t necessary, and that shifting our focus to creating more efficient models themselves could have taken the industry farther. 

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ENTERPRISE

GitHub’s case against defaulting to frontier AI

Coding agents can drastically accelerate how fast developers build, but defaulting to the most powerful models gets expensive fast. But what if your coding assistant could route to less expensive models when they don't need high-end frontier intelligence?

In this episode of The Deep View Conversations, we sit down with Kyle Daigle, GitHub’s chief operating officer, at GitHub Universe in San Francisco to explore how the company's new Copilot Fusion can route between models and reduce token costs.

Daigle explains how Fusion can start with a less expensive model, move to a more capable one when needed, and bring in a second model to check the first model’s work. He says GitHub’s teams and early enterprise customers are seeing 50% to 60% lower costs on complex work, with savings reaching 77% for certain workloads. But most importantly, it's getting results that match using high-end models while doing it about 40% faster. 

Other topics covered:

• Why people outside traditional developer roles are building their own software
• How GitHub’s marketing and design teams use Copilot to create tools for their own work
• How Fusion’s Cascade and Critique approaches coordinate models within a task
• Choosing between Fusion Standard, High, and Max based on the work you need done
• GitHub’s plans to route some tasks to local models running on your computer
• How Copilot Projects brings repositories, files, and agents together in one workflow

If you’re building with AI coding agents, leading a development team, or managing your company’s AI budget, this conversation offers a way to control costs while keeping the capabilities 

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