Public transcript

Scaling enterprise AI: Fireside chat with Eli Lilly’s Diogo Rau and Dario Amodei

By: Anthropic
Length: 6 min
Language: English
Text from: the video's captions
Transcribed: 12 October 2026

Read the full transcript Open on YouTube

Notes in English.

Summary

In this short fireside chat, Eli Lilly's Chief Information and Digital Officer Diogo Rau speaks with Anthropic founder and CEO Dario Amodei about Anthropic's enterprise strategy and how it differs from consumer AI. Amodei explains Anthropic's focus on accuracy and reliability over engagement, mentions specialized Claude products including a planned Claude for Life Sciences, and advises enterprises in drug discovery to be ambitious and prepare now for end-to-end AI rather than making incremental process changes.

Key points

Dario Amodei says consumer AI incentives around engagement and growth lead to problems like model sycophancy, which is unacceptable in enterprise settings where you want truth.
Anthropic designs its models to prioritize accuracy and reliability for economically valuable tasks rather than user engagement.
Amodei gives the example that improving a model's biochemistry knowledge from undergraduate to graduate level matters greatly to enterprises like Eli Lilly even if consumers wouldn't notice.
Anthropic is launching specialized Claudes, including Claude for Financial Services and a planned Claude for Life Sciences, combining model improvements with access to specialized information like databases of proteins, compounds, and assays.
Amodei's advice to those in drug discovery is to be very ambitious about where models are going rather than only swapping AI into individual steps of an existing 20-part process.
He warns that waiting until models can do a task end to end before deploying adds another two-year delay, during which work benefiting patients is not happening.
Amodei says if you do two-year-long projects, plan for where the AI will be, which takes courage and foresight.

Questions it answers

00:00Anthropic's enterprise approach

How does Anthropic's enterprise strategy differ from consumer AI providers?

Amodei says consumer AI incentives around engagement and growth produce model sycophancy, which is unacceptable in enterprises where truth matters. Anthropic designs its models to prioritize accuracy and reliability for economically valuable tasks.

Consumer AI competition for engagement drives sycophantic model behavior.
Enterprises need truth, not flattery, for example in evaluating drug compounds.
Anthropic puts a premium on accuracy and reliability.

01:58Why specialized knowledge matters

Why does improving a model's biochemistry knowledge matter to enterprises?

Amodei notes that raising a model's biochemistry knowledge from undergraduate to graduate level would go unnoticed by 99% of consumers, but matters a lot to a company like Eli Lilly.

Graduate-level biochemistry knowledge is highly valuable to drug discovery enterprises.
Enterprise-focused improvements may be invisible to typical consumers.

02:32Skills and specialized Claudes

What is Anthropic building for specialized enterprise domains?

Anthropic is launching specialized Claudes, some as fine-tuned models and others wrapped with access to particular information, such as Claude for Financial Services with indices and ratings. A Claude for Life Sciences is in progress, potentially giving the model access to databases of proteins, compounds, and assays.

Specialized Claudes combine model improvements with domain-specific information.
A Claude for Life Sciences is planned.
Databases of proteins, compounds, and assays could be put at the model's fingertips.

03:53Advice for drug discovery

What should enterprises in drug discovery do to prepare for AI?

Amodei advises being very ambitious about where models are going rather than only swapping AI into individual steps of an existing process. He says to prepare now for end-to-end AI, because waiting until models are ready adds another two-year delay during which work benefiting patients is not happening.

Don't get seduced by small hill-climbing gains in parts of a process.
Prepare in parallel as models improve to potentially save years.
Plan two-year projects around where AI will be, not where it is.

Notes

Anthropic's Enterprise Strategy and Differentiation

Dario Amodei explains that consumer AI providers compete for engagement and growth, which drives behaviors like model sycophancy, where the model tells users their ideas are good regardless of truth.
Sycophancy is a bigger problem in enterprises: you don't want a model saying a drug compound is great and prompting millions of dollars of spending on a flawed idea.
Anthropic instead puts a premium on accuracy and reliability, designing models to be smarter at a wide variety of economically valuable tasks.
Amodei's example: improving a model's biochemistry knowledge from undergraduate to graduate level matters enormously to Eli Lilly, even if 99% of consumers wouldn't notice the difference.

Skills and Specialized Claudes

Anthropic is launching specialized Claudes, some as fine-tunings of the model itself and others as wrappers connecting the model to particular types of information.
Claude for Financial Services was connected to indices and ratings so it can use that knowledge.
A Claude for Life Sciences is in progress, mixing inherent model improvements with access to databases of proteins, compounds, and assays.

Advice for Drug Discovery and Development

Amodei acknowledges enterprises have to start with small AI use cases, but advises being very ambitious about where models are going.
He warns against only swapping AI into parts 5 and 12 of a 20-part process, because those parts must still interface with non-AI steps 11 and 13.
Models may not do the whole process end to end today, but they might in a year, so organizations should start preparing now.
Waiting until models are good enough end to end before deploying adds another two-year delay, during which work benefiting patients is not happening.
Preparing in parallel as models improve may save years of time.
Diogo Rau agrees: don't do two-year-long projects and expect things to be the same at the end; plan for where the AI will be, which takes courage and foresight.

Text from the video's own captions. Summary and notes written by AI from the transcript, so check anything important against the recording.