Anthropic is set to add Citigroup to its list of top-tier investment banks for its highly anticipated initial public offering. The artificial intelligence developer expands its underwriting team as it prepares for a major public market debut.

Bank Involvement and IPO Strategy

The inclusion of Citigroup strengthens the syndicate leading the share sale. Previously, reports indicated that Morgan Stanley, Goldman Sachs, and JPMorgan were working on Anthropic’s IPO, with the company holding discussions with investors in recent weeks.

Earlier this month, it was reported that Anthropic was seeking new financing to increase its existing $2.5 billion five-year revolving credit facility obtained from lenders last year. The previous facility included participation from Morgan Stanley, Barclays Plc, Citigroup, Goldman Sachs, JPMorgan, Royal Bank of Canada, and Mitsubishi UFJ Financial Group.

Anthropic is set to expand its pre-IPO revolving credit facility to a value exceeding $10 billion. While the final size of the facility has yet to be confirmed, sources indicate plans could change, with the possibility of keeping the revolver at its original target or scaling it back below $10 billion.

Expanding credit facilities before an IPO is a common move for companies preparing to enter public markets. The same banks that extend these credit lines often serve as underwriters for the share sale. Space Exploration Technologies Corp (NASDAQ: SPCX) followed a similar approach, increasing its credit facility with several of its IPO bankers roughly a month before its June public offering.

What the Numbers Show

The scaling of the credit facility to over $10 billion indicates a high level of institutional confidence and capital availability for Anthropic. This figure serves as a key indicator of the financial scale at which the company is operating prior to its public debut. The move aligns with broader trends in AI-related debt financing, which JPMorgan projects will reach $4.1 trillion through 2030, up from previous estimates. AI-related debt issuance has already surpassed $300 billion in 2026, driven by hyperscalers, data center developers, and chip buyers funding unprecedented capital spending.

Anthropic’s Claude AI model has demonstrated significant capabilities in scientific research, specifically in protein design and chemistry data analysis, according to a new blog post by the company. In laboratory experiments, Claude designed proteins from scratch and analyzed complex data with minimal human input, showcasing potential to accelerate research workflows.

In a campaign targeting 15 specific biological targets, Claude generated 1,320 designs, resulting in 354 working protein binders. The model successfully produced functional designs for 14 of the 15 targets. Its overall success rate ranged from 22.6% to 35.1%, which Anthropic noted is substantially higher than the typical success rate of about 10% to 15% for standard protein-design campaigns.

Performance Variance

The model’s performance varied across different targets. In one instance, Claude achieved a 40% success rate when designing against the protein RBX1, compared to a 3.7% success rate among participants in a previous protein-design competition. However, the model struggled with other targets; for example, none of its 90 designs for the maltose-binding protein were confirmed to work.

Claude autonomously selected design sites, generated potential structures, ran them through specialized models, and screened results before sending them to external laboratories for testing.

Chemistry Data Analysis

Beyond protein design, Anthropic tested Claude on routine chemistry data analysis. The model processed raw files from two common laboratory instruments without using specialized software. Claude returned its analysis in 23 minutes for one dataset and 19 minutes for another, producing a written report within approximately 25 minutes by processing both sets in parallel.

The AI’s results closely matched manual laboratory analysis. Claude measured sample purity at 96.4%, compared to the lab’s result of 96.33%. Anthropic stated that such tasks typically involve manual analysis and significant delays before reports are finalized.

What the Numbers Show

The data reveals a divergence between Claude’s high-level success rates and its inconsistency across specific biological targets. While the aggregate success rate of 22.6% to 35.1% exceeds the industry benchmark of 10% to 15%, the model failed completely on the maltose-binding protein target (0% success out of 90 designs). This suggests that while the AI can accelerate high-probability design tasks, it may still require human oversight or additional training for complex or specific protein structures where initial designs fail.

Safety and Future Outlook

Anthropic highlighted that these biological capabilities carry dual-use risks, including the potential to enable dangerous research. Consequently, some advanced biology features remain unavailable for general access. The company views these experiments as an early step toward integrating Claude into the broader drug-development process, aiming eventually to assist from beginning to end.

Anthropic CEO Dario Amodei previously stated that AI could help cure most human diseases within five to 10 years, emphasizing the need for real breakthroughs in medicine and biology rather than optimistic messaging.