I've been building implementation guides for solo founders and small businesses
trying to use AI practically, so I read the PwC CEO Survey closely when it dropped.
The headline number (12% of CEOs generating measurable returns) gets cited a lot, but I think the more revealing finding is the 56% with zero financial impact.
These are companies with enterprise AI budgets, dedicated teams, and access to every tool on the market and the majority are getting nothing back.
PwC calls it "Pilot Purgatory." The pattern: AI gets deployed in isolated, tactical projects that don't connect to revenue. internal tooling, content drafts, meeting summaries while the 12% they call the "Vanguard" are using AI in the product and customer experience itself (44% of Vanguard vs 17% of everyone else).
What I found interesting from a solo founder angle: the structural barriers causing large companies to fail at this “bureaucracy, legacy systems, misaligned incentives, multi-department approval processes” don't exist at the one-person scale.
The bottleneck for small operators is different: it's not knowing which workflows are worth building, in what order, and what "system-level" vs "task-level" use actually means in practice.
Curious if others have a take on why the enterprise failure rate is this high despite the investment, and whether the Vanguard pattern (AI into the product, not just the back office) matches what people are seeing in practice.
The average person is not ready for AI yet. Microsoft's Copilot has a low adoption rate. Data Centers have big energy bills and a lack of clients, and have no ROI for most of them.
The question is whether legacy players can drive strategic growth that changes their trajectory to meet the AI-native disrupters. This is a data point.
I'm not saying that CEOs (or devs, for that matter) lie. But on AI I don't think we can rely on any self-reported results, positive or negative, based on surveys.
There is just too much incentive to say... no, to BELIEVE... both that AI yields 10x productivity that AI is useless.
I am swinging wildly between the two too, personally. The more time I spend with AI, the more I am developing this split personality where one part of me says "I hope this thing blows up before I lose my job and my children never have the chance to have an office job again" and the other one says "AI is actually not easy! You have to know how to use it well, deveop tools, plan, curate your context... This means I am acquiring useful skills here, tring to port Flappy Bird to COBOL".
And obviously, depending which side controls my cortex in that moment, I may err on the "AI is useless crap" or the "AI all the things!" side
I think an interesting analogy for what many of us are experiencing here is the phenomena of Doom Scrolling; deep down we know we should put it down (and go outside), but the immediate experience of it and the value it feels like it’s offering in the moment has you keep scrolling and scrolling.
Similarly many have reported a sense of say programming productivity but a more objective reflection later on reveals the myriad issues with constantly and subtly heralding in large quantities of lower quality code and blowing past any caution or rigourkus discipline that would come with the laying down of lines of code “by hand”.
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[ 3.1 ms ] story [ 19.7 ms ] threadThe headline number (12% of CEOs generating measurable returns) gets cited a lot, but I think the more revealing finding is the 56% with zero financial impact.
These are companies with enterprise AI budgets, dedicated teams, and access to every tool on the market and the majority are getting nothing back.
PwC calls it "Pilot Purgatory." The pattern: AI gets deployed in isolated, tactical projects that don't connect to revenue. internal tooling, content drafts, meeting summaries while the 12% they call the "Vanguard" are using AI in the product and customer experience itself (44% of Vanguard vs 17% of everyone else).
What I found interesting from a solo founder angle: the structural barriers causing large companies to fail at this “bureaucracy, legacy systems, misaligned incentives, multi-department approval processes” don't exist at the one-person scale.
The bottleneck for small operators is different: it's not knowing which workflows are worth building, in what order, and what "system-level" vs "task-level" use actually means in practice.
Curious if others have a take on why the enterprise failure rate is this high despite the investment, and whether the Vanguard pattern (AI into the product, not just the back office) matches what people are seeing in practice.
AI adoption and Solow's productivity paradox
https://news.ycombinator.com/item?id=47055979
This is a lie. It can't be zero. It is negative.
There is just too much incentive to say... no, to BELIEVE... both that AI yields 10x productivity that AI is useless.
I am swinging wildly between the two too, personally. The more time I spend with AI, the more I am developing this split personality where one part of me says "I hope this thing blows up before I lose my job and my children never have the chance to have an office job again" and the other one says "AI is actually not easy! You have to know how to use it well, deveop tools, plan, curate your context... This means I am acquiring useful skills here, tring to port Flappy Bird to COBOL".
And obviously, depending which side controls my cortex in that moment, I may err on the "AI is useless crap" or the "AI all the things!" side
Similarly many have reported a sense of say programming productivity but a more objective reflection later on reveals the myriad issues with constantly and subtly heralding in large quantities of lower quality code and blowing past any caution or rigourkus discipline that would come with the laying down of lines of code “by hand”.