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Innovation with GenAI falls into two categories.

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Productivity use cases reap the immediate benefits
of adding off-the-shelf

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GenAI apps to existing tasks to make work more efficient.

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Real innovation requires more creativity,

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tapping into a company's
proprietary knowledge to invent

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new products, new services,
new customer journeys.

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The talent disruption will be enormous,

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with a host of new roles arising.

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Employees will need new skills.

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Companies will also need quality controls

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that can review and validate GenAI insights.

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In both cases, the key difference
between conventional AI and GenAI

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is that your people don't need the skills
of a data scientist or a software engineer.

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In a sense, English is the new programming language

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that democratizes innovations
from within, across your organization.

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We've already seen impactful results
across a range of industries.

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In drug development,

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where typical years of trials are now sometimes reduced to days.

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In modeling proteins,

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where research is accelerating across
nearly every field of biology.

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In semiconductor design,
where every micron of space matters,

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and new efficiencies
mean leaps in performance.

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In the legal field,
where the discovery process

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is being revolutionized.

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In vehicle making,

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where parts can be fabricated
20 percent stronger for 40 percent less.

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It's two things that lead
to this fundamental transformation.

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One is the notion of infinite memory,

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this entity that knows everything

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that humanity has ever developed at your beck and call.

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The second is the capacity to reason
against the store of knowledge.

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Taken together, GenAI has this endless capacity

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to analyze, to synthesize,
and therefore deduce.

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That's the magic.