Artificial intelligence infrastructure startup Baseten is close to securing a major funding round that would value the company at $13 billion, highlighting the growing investor appetite for businesses powering the next phase of AI adoption.

The San Francisco-based AI inference company is reportedly finalizing a $1.5 billion financing round, according to The Wall Street Journal. If completed, the deal would mark one of the fastest valuation increases in the current AI infrastructure boom, with Baseten’s valuation rising by roughly 160% in less than six months.

The surge comes just five months after the startup announced a $300 million Series E round that valued the company at $5 billion. That funding followed a $150 million Series D round announced only nine months earlier, showing how quickly capital has moved toward companies positioned to benefit from the expansion of AI applications.

The latest financing is expected to be led by Spark Capital, Sands Capital, Altimeter Capital, and Wellington Management, according to people familiar with the matter cited by WSJ.

However, the structure of the deal also highlights a growing feature of the AI investment cycle: split-priced funding rounds.

Unlike traditional funding rounds, where investors typically agree on a single company valuation, split-priced rounds allow different investors to participate at different price levels. Sources told the Journal that some investors in Baseten’s latest round are entering at a $13 billion valuation, while others are investing at an $11 billion valuation.

The approach allows startups to advertise a higher headline valuation while giving investors flexibility on pricing. For venture firms, the strategy can create an opportunity to mark up existing investments quickly, especially in a sector where companies are being valued primarily on future growth expectations rather than current profitability.

Baseten’s rapid valuation jump reflects broader enthusiasm around the AI infrastructure layer, where investors believe the next major wave of value creation will occur.

The Rise Of The “Inference Economy”

Baseten’s growth is tied to what investors have described as an “inference gold rush.” While much of the early AI boom focused on training large language models, attention has increasingly shifted toward inference, the process of running AI models after they have been trained and responding to user requests.

Training creates the model. Inference powers every interaction after that. As companies deploy AI assistants, autonomous agents, and AI-powered software tools, the cost and speed of inference have become critical challenges.

Baseten’s platform is designed to help businesses deploy and operate AI models more efficiently by managing the infrastructure needed to serve AI responses at scale. The company focuses on reducing latency, controlling computing costs, and routing requests to the most suitable model depending on the task.

That includes directing some workloads toward open-source models that can deliver competitive performance at lower costs compared with expensive frontier models.

Early AI investment was dominated by companies developing foundational models, including OpenAI, Anthropic, and Google DeepMind. But as more businesses adopt AI, the bottleneck is moving from model creation to deployment.

Companies need reliable systems that can run AI applications cheaply, securely, and at massive scale. This has created opportunities for infrastructure providers focused on data processing, model hosting, computing efficiency, and AI operations.

The same trend has benefited companies building chips, cloud infrastructure, and specialized AI services. The largest technology companies have committed hundreds of billions of dollars toward AI infrastructure, particularly data centers and advanced computing capacity, as demand for AI workloads accelerates.

Baseten’s expected valuation also raises questions about whether AI infrastructure companies are being priced ahead of fundamentals. A $13 billion valuation implies investors expect significant growth in enterprise AI adoption and continued demand for inference services.

However, AI infrastructure remains expensive to operate. Companies face rising costs from advanced chips, electricity consumption, cloud capacity, and engineering talent. The challenge for startups like Baseten will be converting demand for AI services into durable revenue and margins.

The current AI market is increasingly divided between companies controlling the core models, those providing computing resources, and firms building the tools that allow businesses to use AI effectively.

Baseten is betting that the third category will become one of the biggest winners.

Analysts believe that if enterprises move from experimentation into large-scale AI deployment, inference infrastructure could become one of the most valuable layers of the AI economy. But if AI adoption slows or companies struggle to justify costs, today’s aggressive valuations could face pressure.

The White House and Anthropic are working to establish what could become one of the first formal frameworks for evaluating security vulnerabilities in advanced artificial intelligence models, following a high-profile dispute that led the U.S. government to effectively force the withdrawal of Anthropic’s most powerful systems from the market.

According to U.S. officials familiar with the discussions cited by Politico, the administration and Anthropic are negotiating a set of technical standards that would determine how future AI security flaws are assessed, how serious they are deemed to be, and when government intervention may be warranted.

The effort follows a dramatic confrontation between the AI company and federal officials over Claude Fable 5 and Mythos 5, Anthropic’s most advanced models. The dispute culminated in the White House imposing export controls that prevented foreign users from accessing the systems after officials concluded that a security vulnerability, commonly known as a “jailbreak,” posed unacceptable risks.

The incident has rapidly evolved into one of the most consequential tests yet of how governments will regulate increasingly powerful frontier AI systems. At stake is a fundamental question confronting policymakers worldwide: who decides when an AI model becomes too dangerous to deploy?

Unlike traditional software vulnerabilities, AI jailbreaks occupy a regulatory gray area. Researchers routinely discover ways to bypass safety safeguards embedded in models, but there is little consensus about when such breaches represent manageable technical shortcomings and when they constitute national security threats.

Anthropic argued that the flaw identified by government officials was limited in scope and did not justify pulling the model from public use. Administration officials reached a different conclusion, triggering an unprecedented intervention that exposed the absence of clear standards governing frontier AI deployment.

The resulting negotiations suggest both sides now recognize that the technology has advanced faster than the institutions responsible for overseeing it.

The discussions are reportedly being led by Anthropic’s Head of Public Policy, Sarah Heck, and co-founder Tom Brown, alongside senior administration officials. The objective is to create a common methodology for evaluating future security incidents.

The proposed framework would examine factors including the extent to which safeguards were bypassed, the capabilities exposed through a jailbreak, the likelihood of misuse, and the practical consequences of the breach.

Such a system would represent a significant shift away from the current environment, where assessments are often made on an ad hoc basis, and companies and regulators can reach sharply different conclusions about the same vulnerability.

The negotiations also reflect a growing acceptance within government circles that no AI model can be made completely secure. That reality has become increasingly apparent as AI systems grow more capable. Even models equipped with extensive safety mechanisms have repeatedly been shown to be vulnerable to creative prompting techniques that can circumvent restrictions.

The challenge for policymakers is determining which vulnerabilities are tolerable and which require intervention.

The debate extends far beyond Anthropic.

Leading AI developers, including OpenAI, Google, Meta, and others, face similar questions as they push toward powerful models capable of advanced coding, scientific research, and cybersecurity applications.

Governments are particularly concerned about models that can identify software vulnerabilities, automate cyberattacks, assist in biological research, or accelerate the development of competing AI systems.

Anthropic’s Mythos model became a flashpoint precisely because it reportedly demonstrated unprecedented capabilities in cybersecurity-related tasks, raising fears that even limited breaches could expose powerful offensive capabilities.

The dispute has highlighted how AI regulation is beginning to resemble the oversight frameworks used for sensitive technologies such as nuclear energy, advanced semiconductors, and biotechnology. Rather than focusing solely on consumer harms or privacy concerns, policymakers are increasingly framing frontier AI as a matter of national security.

That shift is evident in the White House’s decision to use export controls, a tool traditionally reserved for strategically sensitive technologies, to restrict access to an AI model. The administration’s intervention also signals a broader willingness to assert federal authority over the deployment of advanced AI systems, particularly where cybersecurity risks are involved.

The discussions come amid growing international pressure to establish common AI safety standards.

Leaders and technology executives at recent G7 meetings reportedly raised similar concerns about the need for agreed methodologies to evaluate advanced model risks. Industry executives have warned that inconsistent regulatory approaches could create uncertainty for developers while allowing dangerous capabilities to slip through oversight gaps.

The outcome of the White House-Anthropic negotiations could therefore have implications far beyond a single company.

If successful, the framework could become a template for future interactions between governments and AI developers, creating a more predictable process for handling security disputes.

The talks offer a pathway toward restoring access to Fable 5 and Mythos 5 while avoiding prolonged regulatory conflict. The framework could provide a White House mechanism for evaluating future AI risks without resorting to emergency interventions each time a vulnerability is discovered.

The fact that talks have progressed from confrontation to technical collaboration suggests both sides recognize the need for clearer rules as frontier AI systems become more powerful.

The broader significance is that the AI industry may be entering a new phase in which model releases are judged not only by commercial performance or technological advancement but also by formal security benchmarks agreed upon with governments. That would mark a major evolution in AI governance, bringing the industry closer to a world where the deployment of cutting-edge models is governed by regulatory standards rather than solely by the discretion of the companies that build them.