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By Luis Rijo

OpenAI lost $20.9 billion in 2025 and cannot pay its bills from cash flow, Ed Zitron says. What happens to the ad platforms financing the record AI buildout?

EZ Primary Research chief executive Ed Zitron used a Bloomberg interview published on July 31, 2026 to argue that the capital expenditure wave lifting technology stocks rests on two loss-making customers, citing UBS estimates that OpenAI and Anthropic will account for 27% of Google Cloud revenue this year and more than 48% next year.

The conversation, released on the Bloomberg Podcasts YouTube channel after a week in which MicrosoftAmazonMeta, and Alphabet reported quarterly results, drew more than 420,000 views by August 2. “Everyone is buying into these stocks because they believe all of that CapEx is going towards diverse and spread out AI demand, when in fact, what it’s actually doing is helping create infrastructure for two unprofitable, unsustainable companies,” Zitron said, according to the interview.

The claim lands on numbers that investors rarely see broken out. According to UBS estimates cited by Zitron, OpenAIand Anthropic together will generate more than $124 billion of Google Cloud revenue next year, with Anthropic alone contributing $76 billion in 2027. Zitron noted that OpenAI’s position as a large Google Cloud customer remains little known, a detail he attributed to UBS analyst Stephen Ju.

Two customers behind the cloud growth

Microsoft shows a similar pattern, according to the interview. Zitron cited Barclays estimates placing the two AI companies at 13% of revenue this year and 18% next year for a cloud operation he described as a much bigger business than Google Cloud. His own reporting on OpenAI’s finances produced a sharper figure. According to Zitron, 69% of the year-over-year growth in the Microsoft Intelligent Cloud segment during 2025 came from OpenAI. Without that single customer, the segment would have grown 8%, which he characterized as barely beating inflation.

“Everyone is being sold what I consider kind of a lie. It’s honestly kind of a scandal,” Zitron said during the interview.

The concentration extends beyond the hyperscalers. According to reporting by The Information referenced in the conversation, 89% of revenue at the largest AI companies comes from OpenAI and Anthropic alone. When a host asked whether the industry must be concentrated to some degree, given the cost of building capable models, Zitron clarified that his concern targets the concentration of revenue in two companies rather than the concentration of compute itself.

The scale of the projected payments raises its own question. “How is Anthropic going to afford that? They burned tens of billions of dollars,” Zitron said, referring to the $76 billion UBS estimate for 2027.

OpenAI’s balance sheet and a delayed listing

Zitron brought direct knowledge of OpenAI’s accounts to the discussion, having reported the company’s audited financials for the Financial Times. “It’s a company just burning cash. They lost $20.9 billion in 2025,” he said. More than $800 million of OpenAI’s revenue that year came from SoftBank for a program called Crystal Intelligence, according to Zitron, who said he could find no evidence of activity connected to it. SoftBank holds a large shareholding in OpenAI without board seats, he added.

The listing timeline compounds the pressure. OpenAI had been expected to go public this year, but the New York Times reported the company is considering a delay until 2027, according to the interview. “That’s lethal for a number of people,” Zitron said.

The structural problem, in his telling, sits in the cash cycle. “OpenAI and Anthropic need continual flows of capital. They do not pay their bills out of existing cash flow,” Zitron said. Any interruption to that capital, he argued, becomes the first domino.

Circular structures and owned infrastructure

Asked whether the companies running the hyperscalers failed at due diligence, Zitron offered a different reading. The platforms “did the due diligence in the sense that they said, we are going to create our largest customers and we’re going to own large parts of them,” he said. The arrangement extends to hardware. According to Zitron, Broadcom sells tensor processing units to Google, the chips are then sold to Anthropic and rented back to Anthropic through Google. “Google gets to double up on revenue,” he said.

The funding picture follows the same loop. OpenAI and Anthropic have raised between $200 billion and $300 billion, according to Zitron, yet the effective total runs higher because Microsoft, Google, and Amazon built their infrastructure for them. He referenced testimony from the trial between Sam Altman and Elon Musk, in which a Microsoft executive put that infrastructure cost at $100 billion, before settling on a figure of roughly $70 billion to $80 billion of capacity the AI companies never had to pay for. The exchange turned briefly to Enron and WorldCom as reference points for fiduciary responsibility, with the explicit caveat that no comparable conduct was being alleged.

PPC Land coverage documents how deep the ownership ties run. Microsoft and Nvidia committed a combined $15 billion to Anthropic in November 2025, valuing the company at approximately $350 billion, while Anthropic committed to purchasing $30 billion of Azure compute capacity – a $3.2 billion gain on that stake lifted Microsoft’s net income 31% in the quarter reported on July 29, 2026. The customer-concentration risk Zitron describes has already reached credit ratings elsewhere: S&P Global Ratings cut Oracle to BBB- in July 2026, noting that OpenAI accounts for roughly half of the $638 billion in remaining performance obligations Oracle carries.

The data center mathematics

Zitron then worked through the demand side of the buildout. Sightline Climate identified about 190 gigawatts of data center capacity in planning or construction as of February, according to figures he cited. Applying a power usage effectiveness rating of 1.3 – the ratio between total facility power and the power reaching computing equipment – and $12 million per megawatt, Zitron calculated that the facilities would require more than $1.6 trillion in annual revenue to justify themselves. “Having two customers is not going to do that,” he said. Even the heaviest spender could not close the gap; he estimated OpenAI would need to spend $400 billion a year, funding he does not believe the company will secure.

Construction timelines add friction. Data centres take 12 to 36 months to build depending on size, according to the interview, slower than the market narrative assumes.

The transformation of the platforms themselves troubles Zitron as much as the arithmetic. Amazon, Google, and Microsoft have shifted from cash-generating businesses with low asset intensity into what he called “bulbous, GPU filled asset mongers,” filled with semi-built data centres serving two or three customers. Meta invests in AI as well but does not yet sell compute capacity, he noted. For his thesis to hold, Zitron argued, nothing dramatic needs to happen: OpenAI and Anthropic would simply have to grow to an implausible size to make the capacity pay off, because otherwise demand for compute at scale does not exist.

Why this matters for the marketing community

The companies named throughout the interview are the companies that sell most of the world’s advertising, and the spending Zitron questions runs through the earnings reports marketers watch each quarter. Alphabet raised its 2026 capital expenditure guidance to $195 billion to $205 billion on July 22, 2026, reported negative free cash flow of $5.9 billion for the quarter, and now carries $98.2 billion in long-term debt, up from roughly $16 billion a year earlier. The company had already raised approximately $85 billion in equity in June 2026 to fund infrastructure it describes as supply-constrained against demand, a framing that sits at the opposite pole from Zitron’s reading. Meta reported quarterly capital expenditures of $31.08 billion on July 30, 2026, against $17.01 billion a year earlier, with free cash flow falling to $784 millionMicrosoft’s capital expenditures rose 70% to $41.0 billion in its June quarter, with calendar 2026 spending expected around $175 billion. Advertising revenue funds a substantial share of those budgets, which means ad businesses now bankroll infrastructure whose demand case rests, in Zitron’s account, on two unprofitable tenants.

The exposure runs in the other direction too. The same AWS infrastructure serving Claude and GPT-5 powers programmatic bidding, creative generation, and Amazon’s Rufus shopping assistant, so the operational continuity of AI ad tooling depends on the economics Zitron disputes. OpenAI itself has entered the advertising market that would need to fund it, with projections of $102 billion in advertising revenue aimed at Google’s $224 billion search business. Concentration compounds the stakes: OpenAI, Google, and Anthropic held more than 84% of the AI agent market as of May 2026, leaving marketers who build on these systems dependent on a narrow set of suppliers whose financing Zitron considers unsustainable.

The productivity question raised near the end of the interview cuts closest to marketing employment. One host framed the best case for the spending as productivity gains in which fewer people do more work, noting serious implications for the labour force if the bet succeeds and for the stocks if it fails. The marketing industry has already placed that bet with its own money. Agency leaders named AI their top investment priority for the second consecutive year, with 77.7% of vice presidents and above planning to increase AI spending, while 39.9% of agencies conducted layoffs within the preceding 12 monthsAI sales and marketing investment reached $3.7 billion globally in the first part of 2026. Whether tokens sold by two cash-burning model companies can substitute for salaried expertise remains contested territory: one analysis argues cheaper marketing work expands total demand for marketers rather than shrinking it, while task-level measurement shows models covering a fifth of job tasks without revealing whether those are the tasks that matter. If Zitron is right about the financing, the industry restructuring its workforce around these systems has tied its labour model to companies that, in his words, do not pay their bills out of existing cash flow.

Timeline

Summary

Who: Ed Zitron, chief executive of EZ Primary Research, speaking on Bloomberg, with claims involving OpenAI, Anthropic, Google, Microsoft, Amazon, Meta, SoftBank, Broadcom, and estimates from UBS, Barclays, Sightline Climate, and The Information.

What: Zitron argued that AI capital expenditure builds infrastructure for two unprofitable companies, citing UBS estimates that OpenAI and Anthropic will supply 27% of Google Cloud revenue this year and more than 48% next year, worth over $124 billion, alongside OpenAI’s $20.9 billion loss in 2025 and a calculation that planned data centres require more than $1.6 trillion in annual revenue.

When: The interview was published on July 31, 2026, following the week in which Microsoft, Meta, Amazon, and Alphabet reported quarterly results, and had drawn more than 420,000 views by August 2, 2026.

Where: The interview took place in the Bloomberg Interactive Brokers studio and was distributed globally through the Bloomberg Podcasts YouTube channel.

Why: The revenue concentration matters to the marketing community because advertising income funds the hyperscaler infrastructure in question, AI advertising tools run on that same infrastructure, and agencies restructuring their workforces around AI have tied their labour models to two companies that, according to Zitron, do not pay their bills out of existing cash flow.

By Luis Rijo

Luís Rijo is a seasoned marketing professional with over 10 years of experience in Digital Marketing, Search, Social, Display, Video, and DOOH. Based in Europe. Reach out via [email protected]

Sourced from PPC Land

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Back in May 2018, Twitter announced that they were collaborating with Google Cloud to migrate their services to the cloud. Today, after two years, they have successfully migrated and have also started reaping the benefits of this move.

For the past 14 years, Twitter has been developing its data transformation pipelines to handle the load of its massive user base. The first deployments for those pipelines were initially running in Twitter’s data centers. For example, Twitter’s Hadoop file systems hosted more than 300PB of data across tens of thousands of servers.

This system had some limitations despite having consistently sustained massive scale. With increasing user base, it became challenging to configure and extend new features to some parts of the system, which led to failures.

In the next section, we take a look at how Twitter’s engineering team, in collaboration with Google Cloud, have successfully migrated their platform.

How The Migration Took Place

For the first step, Twitter’s team left a few pipelines such as the data aggregation legacy Scalding pipelines, unchanged. These pipelines were made to run at their own data centers. But the batch layer’s output was switched to two separate storage locations in Google Cloud.

The output aggregations from the Scalding pipelines were first transcoded from Hadoop sequence files to Avro on-prem, staged in four-hour batches to Cloud Storage, and then loaded into BigQuery, which is Google’s serverless and highly scalable data warehouse, to support ad-hoc and batch queries.

This data from BigQuery is then read by a simple pipeline deployed on Dataflow, and some light transformations are applied. Finally, results from the Dataflow pipeline are written into Bigtable. This is a Cloud Bigtable for low-latency, fully managed NoSQL database that serves as a backend for online dashboards and consumer APIs.

With the successful installation of the first iteration, the team began to redesign the rest of the data analytics pipeline using Google Cloud technologies.

After evaluating all possible options, the team chose Apache Beam because of its deep integration with other Google Cloud products, such as Bigtable, BigQuery, and Pub/Sub, Google Cloud’s fully managed, real-time messaging service.

A BigQuery slot can be defined as a unit of computational capacity required to execute SQL queries.

The Twitter team re-implemented the batch layer as follows:

  • Data is first staged from on-prem HDFS to Cloud Storage
  • A batch Dataflow job then regularly loads the data from Cloud Storage and processes the aggregations, and
  • The results are then written to BigQuery for ad-hoc analysis and Bigtable for the serving system.

For instance, the results showed that processing 800+ queries(~1 TB of data each) took a median execution time of 30 seconds.

Migration final picture via GCP

The above picture illustrates the final architecture after the second step of migration.

For job orchestration, the Twitter team built a custom command line tool that processes the configuration files to call the Dataflow API and submit jobs.

What Do The Numbers Say

via Twitter 

The migration for modernization of advertising data platforms started back in 2017, and today, Twitter’s strategies have come to fruition, as can be seen in their annual earnings report.

The revenue for Twitter can be divided mainly into two categories:

  • Ads
  • Data licensing and other services.

According to the quarterly earnings report for the year 2019, Twitter has declared decent profits with steady progress.

“We reached a new milestone in Q4 with quarterly revenue in excess of $1 billion, reflecting steady progress on revenue product and solid performance across most major geographies, with particular strength in US advertising,” said Ned Segal, Twitter’s CFO.

The 2019 revenue was $3.46 billion, which is an increase of 14% year-over-year.

  • Advertising revenue totalled $885 million, an increase of 12% year-over-year
  • Total ad engagements increased by 29% year-over-year

The motivation behind Twitter’s migration to GCP also involves other factors like the democratization of data analysis. For Twitter’s engineering team, visualization, and machine learning in a secure way is a top priority, and this is where Google’s tools such as BigQuery and Data Studio came in handy.

Although Google’s tools were used for simple pipelines, Twitter, however, had to build their infrastructure called Airflow. In the area of data governance, BigQuery services for authentication, authorization, and auditing did well but, for metadata management and privacy compliance, in house systems had to be designed.

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Sourced from AIM

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You may have used the Waze app to avoid traffic, but what if that data could be used to fight traffic data on a larger scale? That’s what the Waze for Cities Data program aims to do. Waze is making anonymized user data available to cities for free on Google Cloud and adding the tools to help urban planners analyze it.

Waze for Cities Data launched in 2014 as the Connected Citizens Program. It started with 10 city partners and has since grown to 1,000 partners globally, according to Waze, encompassing both cities and other entities that can make use of the app’s crowdsourced traffic data. Partners will now have access to Waze data collected since April 2019 via Google Cloud, as well as analysis tools BigQuery and Data Studio, which were designed to make sense even to lay audiences, according to Waze.

What can users do with this data? Genesis Pulse, an emergency services software provider, started using Waze data to give first responders real-time crash alerts from Waze users. In 40% of cases, crashes are reported by Waze users 4.5 minutes before they are called in via 911 or an equivalent method, according to Waze. According to the Federal Communications Commission, a one minute decrease in average ambulance response time saves more than 10,000 lives in the United States annually, Waze noted.

Public agencies that apply for the program can analyze up to 1TB of data, and store up to 10GB of data, for free each month. The basic data analysis tools are free as well, but more advanced tools will require a paid account. Cities will also be able to store and analyze their own data, while maintaining complete control of it, according to Waze.

Waze’s data-sharing scheme is already proving popular. The top three contributors are the cities of Seattle, Los Angeles, and San Jose, according to Waze. The government of Miami-Dade County and the state transportation agencies of Massachusetts and Virginia are also major contributors, as are both New York City and the Port Authority of New York and New Jersey, which operates large chunks of the Big Apple’s transportation infrastructure. So the next time you open up the Waze app, know that you may be helping to fight urban traffic.

Feature Image Credit: Andy Boxall/Digital Trends

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Sourced from Digital Trends

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Google is doing more to help companies connect, manage, secure and analyze ever-growing amounts of data.

At its Google Cloud conference in San Francisco, the company unveiled a raft of announcements, including new open-source integrations, more AI capabilities and product-development partnerships with large consulting firms like Accenture. Enhancements will assist large companies in areas such as data migration, analytics and cross-compatibility with competitors Amazon Web Services and Microsoft Azure.

Among the offerings is a vertical-specific suite, Google Cloud for Retailers, that will help retailers tap analytics and AI to predict their future inventory needs, recommend products for their customers and assist those customers in locating items they want to buy.

Within that suite is Vision Product Search, which uses Cloud Vision technology. Someone can take a photo or screenshot of a pair of pants they fancy, for example, and the tool will return search results with similar items from the retailer’s inventory.

“We’re able to help if a user likes a specific product; it finds ones that are similar, either in function or in style,” said Andrew Moore, head of Google Cloud Artificial Intelligence. “We provide tools that make the experience on the retailer’s website more immersive and useful for the user.”

Ikea is among those using the product. “We’re working with Google Cloud to create a new mobile experience that enables customers, wherever they are, to take photos of home furnishing and household items and quickly find that product or similar in our online catalogue,” Susan Standiford, chief technology officer at IKEA Group, said in a Google blog post.

Google’s Recommendations AI powers the new Product Recommendations tool, which suggests complementary products as customers browse a retailer’s website. Meanwhile, Real Time Inventory Management and Analytics helps retailers boost the in-store experience so that customers don’t end up empty-handed, costing retailers the sale.

“We’re using sales data regionalized over years or months, depending on what we have, to make a much more accurate prediction of what stocks they should have, in which parts of the country and when, so they are more accurate and have less wastage,” Moore said.

Google has tapped its vast partner network to develop additional tools for retailers. For example, Accenture’s Hyper-Personalization product helps retailers transform data into business insights they can use to boost customer response rates and lifetime value. Google Cloud and Accenture teamed up last year to launch the Accenture Google Cloud Business Group.

Tableau can help retailers quickly collect and analyze their data, while Publicis Sapient assists retailers with addressing data silos to connect and take action on the data points along the customer journey.

That goal is in line with other Google product announcements that improve speed and simplify data migration to Google Cloud. Its BigQuery Data Transfer Service, for example, which can automatically ingest data from SaaS apps to BigQuery, the company’s cloud-scale data warehousing solution, expanded support to more than 100 enterprise apps, including Salesforce and Marketo.

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Sourced from adexchanger

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As Viacom continues to expand its direct-to-consumer streaming video strategy, the company is turning to Google Cloud to enhance its content discovery capabilities.

Viacom, like many of its media competitors, is creating more content every year, increasingly for a global audience that may consume it on different platforms.

The company is using Google Cloud for its “Intelligent Content Discovery Platform.” The platform uses machine learning to automatically pull short clips from content as soon as it is ingested into the platform. It can highlight new content or segments, or identify commercials to optimize the viewing experience.

The company is using Google Cloud for automated content tagging, discovery and intelligence. Encompassing some 65 petabytes of content, the deal allows Viacom’s own teams to easily locate and understand the context of the content, while improving the efficiency of distribution strategies to consumers.

Viacom is also embracing Google’s Customer Reliability Engineering (CRE) strategy, collaborating closely with Google engineers to develop and maintain applications.

The ability of Google Cloud’s solutions to scale across platforms and around the globe is seen as an asset.

While companies like Disney and WarnerMedia are betting on their own premium direct-to-consumer offerings, Viacom has taken a multi-pronged approach to digital content.

The company has created and launched a number of digital studios to produce original content for streaming services, like Netflix and Hulu, as well as social-media platforms, such as Facebook and Snapchat, an Viacom’s own digital platforms.

At the same time, the company acquired Pluto TV to have a free, ad-supported streaming offering for consumers. Pluto TV has a rotating library of free programming, including from Viacom’s own stable of channels.

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Sourced from MediaPost