Anthropic plays its research card ahead of IPO: Claude "autonomously discovers" a CRISPR-like system, and the gene-editing sector is hit by "AI replacement anxiety"

On Wednesday, shares of U.S. gene-editing companies fell. This came after Anthropic announced that its AI model Claude "autonomously discovered" a new enzyme system similar to the gene-editing tool CRISPR. Among individual stocks, Prime Medicine (PRME.US) fell 11.58% in a single day, Editas Medicine (EDIT.US) dropped 8%, Beam Therapeutics (BEAM.US) declined more than 6%, and CRISPR Therapeutics (CRSP.US) and Intellia (NTLA.US) fell about 5.5% and 3%, respectively. Looking at the content of the news itself, Anthropic explicitly stated that "the exact function of the enzyme system has not yet been determined," nor did it prove that it has programmable gene-editing capabilities. Yet the market priced it in under the logic of a "substitute threat," and the reasonableness of this reaction needs careful assessment. Anthropic pointed out that Claude discovered the enzyme system after reviewing a massive DNA sequence database; its scientists' involvement in this work was limited to the initial prompt and laboratory work. The company added that the exact function of the system has not yet been determined, but that it shares similar characteristics with multiple "other systems," all of which are programmable and can perform operations such as cutting, copying, and pasting DNA. This discovery is the first achievement of the wet lab recently launched by the company led by Dario Amodei, and it also marks early evidence of Claude's value in the scientific field. The company is expected to hold its highly anticipated IPO this year. Industry impact: an efficiency revolution, not a replacement revolution It is worth noting that the real impact of Anthropic's discovery on existing gene-editing companies is more likely to be reflected in the diversification of technology routes rather than direct replacement. At present, the technological landscape in gene editing is evolving from a single CRISPR/Cas9 approach toward multiple editing tools in parallelnew technologies such as base editing, prime editing, and epigenetic editing are constantly emerging, and companies such as CRISPR Therapeutics, Beam, and Intellia have each built advantages in their respective niche tracks. The discovery of the ART system merely adds a new potential candidate to this technological lineage and will not change the existing competitive landscape in the short term. What is truly worth the attention of management at gene-editing companies is this: the speed AI has demonstrated in biological discovery may force the entire industry to accelerate its R&D pace. If an AI system can scan a database and propose candidate discoveries within 21 hours, then companies that rely on traditional experimental methods for target discovery and tool screening will face pressure from efficiency competition. Spillover effects on the AI pharmaceutical industry chain From the perspective of the industry chain, this event reinforces the investment theme of "AI + biotech." Public mutual funds are positioning in this track. ICBC Credit Suisse Asset Management believes that AI drug discovery is expected to improve the efficiency of new drug R&D and increase the discovery of new molecules, which is positive for upstream reagents, laboratory animals, CXO, and other sectors. China Europe Fund pointed out that after large-model manufacturers enter the AI pharmaceutical field, they lack the underlying R&D data accumulated by pharmaceutical companies and need to rapidly accumulate experimental data, thereby driving rapid growth in CXO-related orders. Anthropic's wet lab operating model precisely confirms this logic: the wet lab is the validation link for "dry lab" design, used to expose Claude to cells, reagents, and laboratory equipment, and to test the model's ability to direct Siasun Robot&Automation to complete biological experiments. This means that AI giants building their own laboratories will not replace CRO companies; instead, they may generate more outsourcing demand. Re-examining from an investment perspective From an investment perspective, the short-term pullback in the gene-editing sector needs to be distinguished from the medium- to long-term industry logic. Royal Bank of Canada estimates that within the next five years, AI technology is expected to save the U.S. pharmaceutical industry about $90 billion in costs and increase earnings per share by 13%. The value AI creates for the pharmaceutical industry is incremental, not a zero-sum game. But the real constraints currently facing AI pharma also need to be acknowledged. Citi summarized the industry's current state as "faster is proven, better is not"operational efficiency has been verified, but the quality of individual drug candidates and clinical success rates remain uncertain. Citi's research report shows that traditional methods typically require synthesizing and testing about 5,000 molecules and take 4 to 6 years to find a preclinical candidate, while the AI path may reduce the testing scale to about 330 molecules and cut the time to 17 months, but Phase I to III clinical trials still require 5 to 7 years. What AI compresses is the time for search and experimental iteration; clinical trials will not be shortened by the same magnitude. What investors need to be wary of is this: equating the narrative of AI scientific discovery with a quantifiable increase in drug R&D success rates may constitute a cognitive bias. The value of Anthropic's discovery this time does not lie in whether it has created a substitute for CRISPR, but in that it demonstrates a completely new paradigm of scientific discoveryAI agents can autonomously identify patterns in Beijing Vastdata Technology that humans might overlook. The long-term significance of this paradigm is profound, but between paradigm and product there is still a long road of validation and development.