OpenAI has signed a partnership agreement with Visa that allows the company’s AI agents to use the payment card for e-commerce transactions. The agreements lets users shop for everything from groceries and diapers to airline tickets without having to manually enter a lot of information.
“As AI agents become active participants in the economy, Visa’s focus is on ensuring that transactions are reliable, secure, and seamless,” Visa Chief Product and Strategy Officer Jack Forestell said in a statement, according to AP.
The pact means AI agents can complete purchases on a user’s behalf at virtually any merchant that accepts Visa. Details about the financial terms of the agreement, or whether specific transaction fees will apply, were not immediately detailed.
When SpaceX on Tuesday officially announced its plan to purchase AI coding startup Cursor for $60 billion in stock, as it had predicted it would do in April, it presented CIOs and developers with a little good news, a little bad news and a massive pile of uncertainty.
The details of the proposed acquisition were virtually identical to the terms announced in April, even retaining the $10 billion consolation prize for Cursor should SpaceX back out of the deal.
But for CIOs and developers, the increasing probability of the deal happening is forcing them to make long-term decisions without many long-term answers about what is likely to happen to Cursor, which says its coding agents are used by 64% of Fortune 500 companies.
But whether the deal is good news for Cursor’s customers, or even those of its rivals, is an open question.
Arnal Dayaratna, research VP for software development at IDC, is firmly in the good news camp. He argued that the main element holding back the company, which he estimated brings in about $2 billion in annual revenue, was access to GPUs. And, he said, SpaceX’s xAI has the ability to resolve that problem.
It’s all about the GPUs
“The implication is that Cursor can get better because it will get better access to more compute, in the form of GPUs. That was what Cursor was struggling to obtain,” Dayaratna said, adding that Cursor joins both Anthropic and Google in aligning with xAI.
“It is bad news for Anthropic,” he said, pointing out, “[Elon] Musk figured out that GPUs were the limiters, the bottleneck. Not human talent: compute. And it’s not only about GPUs. It’s about the data centers,” as well as the mechanisms needed to support data centers, including electricity access, cooling apparatus, and zoning rights.
“Zoning rights are very difficult to obtain in the United States, and Elon has been able to leverage his political capital” to obtain those rights, Dayaratna said.
Dayaratna also commented on the highly unusual tactic of a company pre-announcing an acquisition and then announcing it “officially” two months later. The reason, he suggested, is that Musk didn’t want the acquisition to delay the SpaceX IPO, but he did want potential investors to know about it. “It gave the IPO more legs, even though it was not formally a part of the IPO. He wanted to socialize it before it happened,” he said.
Jason Andersen, principal analyst at Moor Insights & Strategy, took a far more cautious approach and predicted, “enterprise customers will be very concerned.”
Time for CIO due diligence
“xAI’s models and treatment of guardrails are very different than what Cursor has stood for,” Andersen said, and the key issue involves model choice. “Will Cursor be able to point at models other than Grok? If that is the case, it could go towards helping customers be OK with things for a while,” he said.
But Andersen stressed the words “for a while.” He argued that Cursor rivals have gotten more sophisticated, which could give enterprise CIOs more alternatives.
“The tooling options from the classic enterprise vendors have gotten really good, and also have a number of features that enable better governance and teaming. For example, Kiro from AWS is already a worthy competitor to Cursor, but there have also been major improvements from Google and Microsoft this year,” Andersen said. “I’d venture to say that Cursor was already facing a substantial competitive challenge in the enterprise, especially as the vibe coding movement lost some steam towards agents that enable so much more developer capability.”
Andersen added, “A lot of SpaceX’s valuation is pinned to xAI and there wasn’t enough IP there for it to be long-term competitive with Anthropic or OpenAI without rapidly injecting new technology.”
Shashi Bellamkonda, principal research director at Info-Tech Research Group, said the current period of due diligence gives CIOs opportunities to ask difficult questions.
“I do believe that enterprise customers will start to ask harder questions now, especially if more of the processing starts moving through Grok or xAI infrastructure,” Bellamkonda said, but he stressed that he does see a SpaceX deal helping Cursor.
“Cursor is already carrying a lot of LLM cost and if SpaceX and xAI can give it access to more compute and a faster, improved Grok model, customers could eventually get better performance at a lower cost. Grok is getting faster and more accurate, and under SpaceX there may be more enterprise appetite for xAI than xAI had on its own,” Bellamkonda said.
Questions about data privacy
But Bellamkonda also pointed to the trust issue, specifically asking whether Cursor would be allowed to continue its zero-data-retention policy under the new ownership.
“Enterprise users with sensitive codebases will want to understand exactly where their data is going, who has access to it, and whether any prompts, code, metadata, or embeddings are touching SpaceX or xAI systems,” Bellamkonda said. “Zero data retention is only valuable if customers believe the controls still apply across the new infrastructure. If they can preserve Cursor’s privacy posture while using xAI and SpaceX compute to improve the product, this could be a very strong outcome for customers. If the data governance is unclear, enterprise buyers will hesitate.”
Justin Greis, CEO of consulting firm Acceligence, echoed Bellamkonda’s concerns.
“For many enterprise customers, Cursor’s zero-data-retention policy was not simply a security feature. It was a foundational part of the procurement and approval process,” Greis said, pointing out that security teams, legal departments, compliance officers, and executive leadership all became more comfortable adopting AI-assisted development because they believed their source code, prompts, and proprietary intellectual property would not be retained, stored, or repurposed.
“Whenever ownership changes, customers naturally revisit those assumptions,” he said. “I would expect enterprise customers to immediately seek clarity on whether existing zero-data-retention commitments remain unchanged, whether those commitments continue to be contractually enforceable, and whether any future modifications could be introduced through changes to terms of service or product strategy.”
Reassessing platform risk
Another concern is whether the move will create greater vendor concentration, and whether that will increase risk exposure.
“Many organizations are already concerned about becoming overly dependent on a small number of AI providers. If Cursor becomes more tightly integrated into a broader SpaceX technology ecosystem, some enterprises may worry about reduced flexibility and fewer alternatives. I suspect this will be a continued question with SpaceX’s recent IPO and growth plans,” Greis said.
Additionally, he pointed out, “AI coding platforms are rapidly becoming part of critical software delivery infrastructure. When organizations standardize on these tools, they are making a long-term platform decision that influences developer productivity, software quality, security processes, and engineering workflows. A change in ownership naturally prompts a reassessment of platform risk, roadmap alignment, and long-term strategic direction.”
As well, Sanchit Vir Gogia, chief analyst at Greyhound Research, noted, “zero data retention survives only where it stays contractual, auditable, enforceable and fenced from affiliate use. The deeper point is what Cursor has become. It is no longer a developer convenience. It sits inside the act of software creation, close to the intellectual-property bloodstream of the enterprise. That is a control plane, and control planes change hands rarely and consequentially.”
Gogia added that even before an acquisition, Cursor was already weakening its data guarantee. “Even now the promise is layered. Cursor’s own disclosures show that standard Privacy Mode still permits some code data to be stored for product features, while only the stricter legacy setting retains nothing. Zero is the exception, not the default.”
The department cited national security concerns, saying Elon Musk’s company had played a crucial role in the Iran war. It also argued it has the authority to stop environmental lawsuits brought by citizens.
The department cited national security concerns, saying Elon Musk’s company had played a crucial role in the Iran war. It also argued it has the authority to stop environmental lawsuits brought by citizens.
The department cited national security concerns, saying Elon Musk’s company had played a crucial role in the Iran war. It also argued it has the authority to stop environmental lawsuits brought by citizens.
As enterprises rush to build AI agents that can reason over business data and take action, Databricks argues that the long-standing practice of separating operational and analytical data systems is turning into a liability.
That separation, the cloud-based data warehouse provider says, is becoming increasingly strained as AI agents require simultaneous access to live operational data and historical context to make decisions and take actions in real time, unlike humans, who traditionally can work with data that is minutes or hours old.
At its annual Data + AI Summit, the data warehouse provider introduced Lake Transactional and Analytical Processing (LTAP), a new architecture designed to unify transactional and analytical data on a single storage layer.
Traditionally, OLTP databases are optimized for running day-to-day business operations such as order processing, payments, and inventory updates, while OLAP systems are designed for large-scale analytical queries and reporting.
As a result, enterprises often need to rely on ETL pipelines, data replication, and separate infrastructure to move information between the two environments.
LTAP, Databricks said, seeks to eliminate the reliance on ETL pipelines, replicated databases or separate data copies by storing data once in a shared lakehouse layer while allowing dedicated compute engines to handle transactional and analytical workloads independently.
This approach, the company argued, provides AI-driven agents and applications access to both live operational data and historical analytical context without requiring data movement or duplicate copies.
Developer simplicity in the agentic era
Analysts, too, agree with Databricks’ contention that AI agents place new demands on enterprise data architectures.
“Agents don’t behave like people, or even like the apps we built for people. They read for context, loop, try things, then write something back, thousands of times over in ways you can’t fully predict. At that volume, the constant bouncing between production and analytics systems starts becoming the bottleneck. The pressure to collapse that gap is real, and LTAP is one way to approach it,” said Michael Leone, principal analyst at Moor Insights and Strategy.
Bhupendra Chopra, cofounder and CRO at IT consulting firm Kanerika, pointed out that an autonomous agent’s data access pattern makes the traditional architectures brittle: “We’re seeing this directly with clients deploying multi-agent systems, the pipeline layer becomes the ceiling almost immediately as an agent runs hundreds of times per task.”
The analysts also pointed out that the ability to collapse the gap between OLAP and OLTP is likely to help developers design more robust AI agents or applications that enterprises are currently targeting to deploy.
“The most interesting workflow or application patterns are real-time, context-aware applications that combine transactions, analytics, and AI in one flow,” said Stephanie Walter, practice leader of AI stack at HyperFRAME Research.
“Examples include AI agents that update customer workflows while seeing historical account context and fraud systems that act on live transactions and long-term behavioral patterns,” Walter added.
Designing such applications today, however, according to Leone, would require developers to pull together data from transactional systems, data warehouses, vector databases, and other sources through custom integrations, creating significant engineering complexity and maintenance overhead.
Operational simplicity and governance gains for CIOs
For CIOs, LTAP’s ability to reduce that engineering complexity, according to Ashish Chaturvedi, leader of executive research at HFS Research, will result in operational simplicity as well as cost savings.
“Most prominent advantage would be fewer data pipelines and everything that cascades from eliminating them. Most enterprises don’t realize how much of their data engineering budget is pure plumbing maintenance,” Chaturvedi said.
Kanerika’s Chopra pointed out that a substantial portion of data engineering capacity in mid-to-large enterprises today is consumed by maintaining synchronization between transactional and analytical systems.
The implications, however, Chaturvedi noted, are not limited to developer productivity, architectural simplicity, or cost savings: “The strategic prize is simplified governance. When you have one copy of data under one governance model instead of the same data scattered across operational stores, replicas, warehouses, and vector databases, you’ve solved the governance fragmentation problem.”
That simplification, according to Chopra, will matter operationally for enterprises deploying multiple AI agents, as these workflows can amplify governance gaps at a speed and scale that no human workflow ever did.
LTAP versus HTAP
Despite all its benefits, though, LTAP isn’t the first effort to unify operational and analytical workloads under a single architecture and for years.
The industry has pursued a similar goal through Hybrid Transactional and Analytical Processing (HTAP) architecture, which sought to combine operational and analytical workloads on tightly coupled infrastructure to serve both workload types from the same system.
LTAP, in contrast, separates storage from compute, allowing different engines to access a common data layer while remaining independently scalable, Databricks said.
That separation of compute engines is why analysts think that LTAP might be a better bet than HTAP.
“HTAP never took off because asking one tightly bound system to be great at transactions and great at analytics usually left it mediocre at both, so customers ended up paying a premium for that compromise,” Leone said.
“I think separating storage from compute is the right instinct, and it’s the same move that made the modern cloud data world work in the first place. It matters because the thing that sank HTAP was one workload starving the other, and giving each side its own dedicated engine is exactly how you keep that from happening,” Leone added.
Another reason for HTAP’s failure, according to David Menninger, executive director of software research at ISG, was its requirement for enterprises to replace existing data platforms with a new architecture.
LTAP, by contrast, builds on the now-common practice of separating compute and storage, making the addition of an operational layer less of an architectural transformation and potentially lowering the barrier to adoption, Menninger added.
Not yet the default architecture for AI agents
However, despite the enthusiasm around LTAP, analysts warned CIOs against viewing this as the inevitable successor to existing data architectures.
“CIOs will still need to choose their data architecture based on latency, reliability, ecosystem fit, cost, compliance, and developer experience,” Walter said.
Echoing Walter, Chaturvedi pointed out that for LTAP to become the de facto standard for the industry, Databricks will need more than architectural elegance: “The architecture looks sound on paper. The proof will be in the commit-to-query latency numbers under real load.”
LTAP, Databricks said, is expected to be released soon as part of Lakebase, without providing any specific timelines.
Anthropic’s apparent inability to identify which of its users are foreign nationals has led to some collateral damage from a US export ban on its most powerful AI models — but there is a way around it, at least for some.
On Friday, the US government ordered Anthropic to suspend access to Fable and Mythos, the new AI models it had introduced just a few days earlier, to all foreign nationals, citing national security reasons.
“The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance,” Anthropic said in a blog post commenting on the order, implying that it was unable to distinguish between foreign nationals and US citizens in its user base.
That’s likely the case today, but for its consumer customers, an update to its privacy policy, introduced last week and taking effect on July 8, gives it a new option: asking them for government ID.
The section of the policy on collection of personal data contains a new provision under the heading “Personal data you provide to us directly,” saying:
Verification Data: In certain circumstances, we may ask you to verify your age or identity. If you choose to do so, data we will collect includes, depending on the method: an image of your government-issued identity document and the information appearing on it (such as your ID number and date of birth); your image in photo or video form, facial geometry templates (which may be considered ‘biometric data’ in some jurisdictions); and the result of the verification (for example, whether your age meets the applicable threshold).
If the government ban on foreign access to Fable and Mythos continues, that would give Anthropic the option of opening access to users willing to submit a scan of their identity document, provided that it contained proof of their US citizenship. That would be the case for US passports — and also for citizens’ driving licenses issued by some US states along the country’s Northern border, which issue so-called enhanced driving licenses indicating the holders’ nationality.
Enterprise users most likely to benefit from the power of the new AI models, though, will have to hope Anthropic finds some other way out of the current impasse.
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