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$CRM CEO Marc Benioff explains that, while they’re spending $300M with Anthropic right now, they’re “wasting” much of that spend and the “vast majority” of those tokens don’t need to come from Anthropic and AI cost will come down.

$CRM CEO Marc Benioff explains that, while they’re spending $300M with Anthropic right now, they’re “wasting” much of that spend and the “vast majority” of those tokens don’t need to come from Anthropic and AI cost will come down.

40,554 次观看 • 1 个月前

Context matters for agentic workflows. Robert Smith of Vista Equity: - In a traditional manual workflow environment, evaluating insurance claims in one of our systems would cost ~$8M - When you run the workflow through a general LLM with some systems underlying, the cost comes down to $3-4M - In our proprietary systems the inference cost falls to $200K with a faster, higher fidelity output, because we have the context of what fraud claims are through our insurance platform

Context matters for agentic workflows. Robert Smith of Vista Equity: - In a traditional manual workflow environment, evaluating insurance claims in one of our systems would cost ~$8M - When you run the workflow through a general LLM with some systems underlying, the cost comes down to $3-4M - In our proprietary systems the inference cost falls to $200K with a faster, higher fidelity output, because we have the context of what fraud claims are through our insurance platform

24,797 次观看 • 1 个月前

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

31,196 次观看 • 1 个月前

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