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CEO, Palo Alto Networks
5 quotes from 2 episodes on All-In Podcast and 20VC with Harry Stebbings, each with a timestamped link to the source.
Nikesh Arora outlines the financial and regulatory barriers to corporate AI adoption. Companies face a timing problem if new applications fail to generate revenue fast enough to fund expensive compute infrastructure.
Basic customer service tasks like troubleshooting network connections do not justify paying $6 per million tokens.
“I don't think you need to pay $6 a million tokens to answer a call saying how can I help you? I'm so sorry your network connection is not working.”
From Free Average AI, Paid Genius: The Looming Compute Crunch
Core infrastructure inputs will face consistent pricing pressure for the next 3 to 5 years.
“…the thing that is going to get priced for the next 3 to 5 years…”
From Free Average AI, Paid Genius: The Looming Compute Crunch
Nikesh Arora supports open weight models because teams can modify the parameters. Blaming an unknown IT system for a mistake creates immediate corporate liability with medical and banking regulators.
“We preference open source open weight because it allows us to modify the weights and train them…”
“If I can't validate, it's not going to be okay to go to your boss to say, 'I don't know what happened, but the IT guys put an AI model in there, it did it,'”
The main problem for AI expansion is a gap between spending and returns. Nikesh Arora questions whether software revenue will arrive fast enough to fund continuous capital expenditure cycles.
“The biggest only problem we have right now is a timing problem. Would the revenues show up fast enough to keep funding the capex cycle or is there going to be a dislocation in in capex versus outcomes?”
From Free Average AI, Paid Genius: The Looming Compute Crunch
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