AI was supposed to lighten your workload. So why is everyone working harder?
Bloomberg reports 40% of worker time is freed up by AI. But new research shows something troubling: after AI adoption, email volume is up 104%, messaging is up 145%, and nothing decreased. Only 3% of workers are in the “productivity sweet spot.” And when Master B2B polled their community, 67% said AI means they’re doing MORE work, not less.
In this episode of Master B2B’s Friday 15, Brian Beck and Andy Hoar dig into one of the most important questions facing B2B leaders: Is AI creating real productivity — or just more production?
FAQ
Q: Is AI actually making people more productive? A: It’s complicated. A 2026 State of Workplace report found that after AI adoption, every single work category increased — email volume up 104%, messaging up 145%. Nothing decreased. The promise of doing less with AI is not showing up in the data. However, for specific tasks, AI can deliver significant time savings. The key distinction is between production (doing more) and productivity (doing the same quality work in less time). As Andy Hoar framed it at the Master B2B summit: “If you’re producing more but the quality isn’t as good, that’s production, not productivity.”
Q: What is the AI productivity “sweet spot”? A: Research shows that only 3% of workers are in the productivity sweet spot — those who spend 7-10% of their time using AI tools. They show the highest productivity gains. Meanwhile, 57% of workers spend less than 1% of their time with AI, likely due to the learning curve. Andy Hoar compared it to racing at the Indy 500: “The smart drivers go the fastest when required. But if all you do is put the pedal to the metal the whole time, you’ll run out of gas.” Strategic, targeted use of AI produces disproportionate gains; too little use is ineffective, and too much creates diminishing returns.
Q: What’s the difference between production and productivity? A: This was a central debate at the Master B2B Mindshare Summit. Brian Beck and Andy Hoar define productivity as doing the same or better quality work in less time. Production is simply doing more, regardless of quality. The example from the summit: an executive said she could produce far more press releases using AI. When asked if the quality was the same, she said no. That’s more production, not more productivity. The critical question for companies is: what’s the objective quality standard, and are you maintaining it while increasing output?
Q: Is AI making some workers lazier? A: There’s evidence of this. Research from ActivTrak found that the share of workers “at risk of disengagement” — defined as underutilized at least 75% of the time — rose from 19% just after ChatGPT’s release to 23%. Some workers are using AI to do their work faster and then coasting. Brian Beck noted this shows up in the quality of work visible on LinkedIn and in email, where AI-generated content is obviously lower quality but the person producing it may believe they were “really productive today.”
Q: What does the Meta layoff announcement mean for B2B companies? A: Meta is reportedly planning to shrink its workforce by 20% (from 80,000 to 64,000 employees), which would drive revenue per employee from $2.2 million to $3.5 million. Analysts estimate this could generate 3-5% earnings per share upside in 2026. Notably, the stock price went up on the layoff news — a reversal from historical norms. At the Master B2B summit, Brian Beck asked his audience how many thought AI would reduce headcount at their companies, and every single person raised their hand. However, the hosts noted that B2B companies may face a different reaction than tech companies when announcing AI-driven layoffs.
Q: How is Apple’s AI strategy different from everyone else? A: While Google, Amazon, and Meta are spending $135-200 billion each on AI infrastructure, Apple is spending comparatively very little — low double-digit billions. Yet Apple reportedly generated $1 billion in AI revenue, primarily by charging other companies (like OpenAI for ChatGPT subscriptions) a toll to access its ecosystem. As Brian Beck put it, “Good to be Apple. When you have the ecosystem and you control the gate, let people in the gate, but they got to pay for it.”
Q: Does quality always matter when using AI? A: Not always — and that’s an important nuance. Andy Hoar made the point that in some work categories, quality matters significantly and AI-generated output that falls short is genuinely harmful (like sloppy LinkedIn content or poor customer communications). But in other categories, the quality standard is lower and AI can deliver both more production and real productivity gains. The key is knowing which category your work falls into. Where quality matters, humans must remain the judge and editor. Where it doesn’t, AI can drive both volume and efficiency.
Q: What does the community say about AI and workload? A: In a Master B2B community poll, 67% said AI means they’re doing more work, and only 33% said it lightens their workload. Both hosts confirmed this matches their personal experience. Brian Beck described filling freed-up time with additional work rather than working fewer hours. Andy Hoar added another layer: even when AI lowers the bar to do a task yourself, it may not be the best use of your time — describing how he used GPT for legal work and saved money on a lawyer, but questioned whether that was really the highest-value use of his own hours.
PODCAST RECAP
Friday 15 with Master B2B — AI, Productivity, and the Future of Work
Welcome everyone to Friday 15 with Master B2B. I’m Brian Beck, here with my partner Andy in our thought leadership series on B2B e-commerce and digital leadership. Andy joins us from Chicago, where the weather has been characteristically chaotic — 32 degrees on Thursday and 70 today. We experienced the same whiplash at our Master B2B Mindshare Summit last week, where it was snowing at 28 degrees one day and 72 and sunny the next.
Breaking News: Meta’s AI-Driven Layoffs
We kicked things off with a Reuters exclusive reporting that Meta is planning sweeping layoffs as AI costs mount. Meta’s workforce could shrink by as much as 20% — from 80,000 down to 64,000 employees — which would drive revenue per employee from $2.2 million up to $3.5 million. Analysts suggest a layoff of this magnitude could drive 3–5% earnings per share upside in 2026 and 4–7% in 2027, provided the savings are redeployed into AI innovation.
This sent shockwaves through the tech world, especially given that Meta has been aggressively recruiting top AI talent — some individuals receiving pay packages reportedly worth hundreds of millions, with one reportedly valued as high as a billion dollars over time with stock options. Now they’re announcing a potential 20% workforce reduction. The theory of AI’s promise is being put to the test.
What’s striking is how the market responded. There was a time when layoff announcements sent a company’s stock price down. Now it goes up — and in this case, before the layoffs have even happened. When you’re spending $135 billion on data center infrastructure this year alone, and planning $600 billion by 2028, you simply can’t fund both people and infrastructure at that scale. Google is spending $180 billion on AI, Amazon $200 billion. These companies have made their choice.
One interesting counterpoint: Apple is spending comparatively little on AI infrastructure — low double-digit billions — yet reportedly generated $1 billion in AI-related revenue, largely by charging others a toll to access their ecosystem. About 80% of that appears to be subscription revenue from ChatGPT. Apple may be letting everyone else learn on their dime and simply controlling the gate. Whether that strategy holds long-term remains to be seen.
Is AI Creating Productivity — or Just More Work?
This brings us to the central question of today’s Friday 15: Is AI actually making people more productive, or is it just causing us to do more work — and at lower quality?
A Bloomberg article reported that AI is freeing up 40% of worker time, calling for an urgent redesign of job roles. But the picture is more complicated than that headline suggests. Research from ActiveTrack found that the share of workers at risk of disengagement — defined as being underutilized at least 75% of the time — rose from 19% just after ChatGPT’s release to 23%. In other words, some workers are simply using AI to do less, submitting fast, low-effort output and calling it done. We’ve all seen it on LinkedIn.
On the other end of the spectrum, the Wall Street Journal reported that AI isn’t lightening workloads — it’s actually making them more intense. As ActiveTrack’s Chief Customer Officer put it, it’s not that AI doesn’t create efficiency; it’s that the capacity it frees up immediately gets repurposed into doing more work. This is Parkinson’s Law in reverse: if you can do something in two minutes instead of two hours, you just do it now — and then move on to the next thing. The appetite grows with the eating.
A 2026 State of Workplace Report bears this out. After AI adoption, every single work category increased — email up 104%, messaging up 145%. Nothing decreased. The promise of doing less with AI is simply not showing up in the data.
The Productivity Sweet Spot
Interestingly, the same report found that only 3% of workers are in the true productivity sweet spot: those who spend 7–10% of their time using AI tools show the highest productivity gains. Think of it like a race — the smart drivers know when to put the pedal to the metal and when to hold back. Use AI too little (under 1% of the time, where 57% of workers currently fall) and you miss the gains. Use it too much and you burn through your fuel without winning the race.
Strategic and tactical use of AI produces disproportionate gains. You have to invest enough time to learn how and where to use it — and then apply it with discipline.
When we asked our LinkedIn community whether AI lightens their workload or just creates more work, 67% said they’re doing more work. Only 33% said it lightens their load. That tracks with our own experience.
Production vs. Productivity — A Critical Distinction
At our Mindshare Summit, a peer panel raised a question that really cuts to the heart of this: Is AI driving productivity, or just production?
One marketing communications professional shared that she’s now able to write far more press releases and produce far more content than before. But when asked whether the AI-generated content was the same quality as her own writing, she said no. So the question becomes: if you’re producing more but at lower quality, are you actually more productive? True productivity should mean maintaining quality standards while doing more in less time — not simply generating more output of diminished quality.
There’s also a delegation paradox at play. AI lowers the bar to get things done, which sounds good — but it means you may spend time doing tasks yourself via AI that you really should be handing off to someone else. And then you find yourself doing legal research with GPT when you probably should have been focused on higher-value work and let someone else handle it.
The good news is that human judgment still has a critical role: deciding what quality standard is required, what’s worth doing more of, and what genuinely matters. In cases where quality doesn’t matter much, AI can increase both production and productivity. Where quality is paramount, using AI as a crutch is a bad decision — and audiences are starting to notice.
