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    AI

    Top 1% Firms Spend 600x More on AI Than Median Companies

    a16z and investors share Ramp figures on monthly AI tool costs per employee.

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    10 Sources, 47d ago, first seen 47d ago

    TLDR

    a16z posted charts citing Ramp data on corporate AI tool spending. Olivia Moore stated the median company spends $12 per employee per month on AI while the top 1% spends $7,500 per employee per month. Nicholas Thompson, Aaron Levie, Greg Brockman and others reposted or commented on the numbers. Levie noted the data leans toward engineering-centric firms yet spending growth appears across types. The posts frame the difference as an adoption gap visible in the shared charts.

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    10 Sources, first seen 47d ago

    Combined views

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    10 Sources, first seen 47d ago

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    355 comments
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    790 reposts

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    355 comments
    2.1K saves
    790 reposts
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    10 Sources

    @omooretweetsThe median company is spending $12 / employee / month on AI The top 1% are spending $7,500 / employee / month Not sure we've ever seen an adoption gap quite like this (h/t @tryramp data, @a16z)
    @beffjezosCan you feel the acceleration?
    @ivanfioravantiThe real digital divide is starting!
    @peteskomorochRT @ivanfioravanti: The real digital divide is starting!
    @a16zWild adoption gap: the top 1% of AI spenders are spending more than 600x as much as the median company Charts of the Week: https://www.a16z.news/p/charts-of-the-week-head-in-the-neoclouds
    @gdbsounds accurate
    @nxthompsonRT @omooretweets: The median company is spending $12 / employee / month on AI The top 1% are spending $7,500 / employee / month Not sure…
    @levieAI spend is nowhere near hitting any walls. Obviously this data is weighted toward engineering-centric companies -with the top 10% of companies spending $660/mo employee on AI- but the general trend of continued exponential growth is occurring throughout all firm types. And what the top 10% is doing today may easily be what the top 50% are doing in 3 years from now, at least in terms of token volume. This is why there’s still so much opportunity right now in the diffusion of AI in the enterprise. Especially as token costs come down, we will throw larger portions of work at agents. They’ll be scanning all our code for security issues, testing all of our software, writing coding for much larger projects, processing nearly all data, and much more. So this trend has no end in sight.
    @sarthakghPerfectly illustrates: Power law distribution Jagged capabilities Slow diffusion Intelligence is not the main bottleneck It’s still so early All at once

    10 Sources

    @omooretweetsThe median company is spending $12 / employee / month on AI The top 1% are spending $7,500 / employee / month Not sure we've ever seen an adoption gap quite like this (h/t @tryramp data, @a16z)
    @beffjezosCan you feel the acceleration?
    @ivanfioravantiThe real digital divide is starting!
    @peteskomorochRT @ivanfioravanti: The real digital divide is starting!
    @a16zWild adoption gap: the top 1% of AI spenders are spending more than 600x as much as the median company Charts of the Week: https://www.a16z.news/p/charts-of-the-week-head-in-the-neoclouds
    @gdbsounds accurate
    @nxthompsonRT @omooretweets: The median company is spending $12 / employee / month on AI The top 1% are spending $7,500 / employee / month Not sure…
    @levieAI spend is nowhere near hitting any walls. Obviously this data is weighted toward engineering-centric companies -with the top 10% of companies spending $660/mo employee on AI- but the general trend of continued exponential growth is occurring throughout all firm types. And what the top 10% is doing today may easily be what the top 50% are doing in 3 years from now, at least in terms of token volume. This is why there’s still so much opportunity right now in the diffusion of AI in the enterprise. Especially as token costs come down, we will throw larger portions of work at agents. They’ll be scanning all our code for security issues, testing all of our software, writing coding for much larger projects, processing nearly all data, and much more. So this trend has no end in sight.
    @sarthakghPerfectly illustrates: Power law distribution Jagged capabilities Slow diffusion Intelligence is not the main bottleneck It’s still so early All at once