By Dr. Gleb Tsipursky

On a weekday morning in downtown Washington, federal buildings and corporate offices still feel half-full, even as return-to-office emails pile up. At the same time, across the Atlantic, the House of Lords has treated remote work not as a culture war skirmish, but as a subject for a full special inquiry on home-based working, backed by extensive evidence and formal hearings. Its Home-based Working Committee spent ten months asking a simple question with big consequences: is working from home working, and if so, how should governments and employers respond.

The answer, detailed by researcher Jane Parry in a synthesis of five years of hybrid working evidence, is clear enough for policymakers. Hybrid work shows only modest average effects on productivity, but delivers meaningful gains in labor supply, employment rates, recruitment, retention and office efficiency when it is managed deliberately. The committee heard evidence that hybrid arrangements can raise labor supply by 1 to 2 percent and cut turnover costs by an estimated £7 billion to £10 billion a year in the UK, especially when employers lean into structured “anchor days” and redesign offices around collaboration. For a Congress fixated on productivity and deficits, the real story is that remote work has quietly become critical economic infrastructure.

Meanwhile, global data on working arrangements confirm this is not a passing phase. The Global Survey of Working Arrangements finds that full-time employees now average close to one day of work from home per week across 34 countries, with English-speaking economies and knowledge jobs showing significantly more remote time, according to its report on working from home around the globe. A companion paper on the evolution of working from home concludes that remote days have plateaued at historically high levels since 2022 and are likely to grow slowly as technology improves. Hybrid work has become a permanent feature of white-collar labor markets, not a pandemic loophole.

The Lords inquiry placed labor supply at the center of its analysis. Evidence submitted to the committee estimated that hybrid work can expand the available workforce, primarily by enabling people who cannot handle full-time commuting to participate. That group includes disabled workers, parents of young children and people in rural or high-cost regions who are effectively locked out of traditional office jobs. For a country facing chronic worker shortages and flat productivity, that margin matters. For the United States, which faces similar pressures and an aging population, it should be impossible to ignore.

New American research connects this directly to disability employment. An NBER study on work from home and disability employment finds that a one percentage point increase in remote work raises full-time employment among people with a physical disability by about 1 percent. The authors estimate that between 68 and 85 percent of the post-pandemic rise in full-time employment for this group between 2019 and 2023 is explained by expanded work-from-home options. Remote work is not just a perk for tech workers; it is now a primary channel for bringing disabled Americans into steady, full-time roles.

Global survey data show that these patterns rest on durable preferences. Workers with college degrees and access to remote-capable tasks typically want two or three days at home, and employers have largely settled around that level, according to the evidence on the evolution of working from home. For many employees, that flexibility is worth roughly an 8 percent pay increase in perceived value. Against that backdrop, the UK committee’s recommendation to treat hybrid work as a structured labor market tool rather than an ad hoc concession offers a blueprint that US lawmakers can adapt. It is directly relevant to efforts on both sides of the aisle to reduce economic inactivity, boost participation and draw caregivers and disabled Americans back into the workforce without new entitlement spending.

The Lords inquiry also underscores how hybrid work alters the basic economics of hiring and turnover. Parry’s summary of the inquiry findings notes estimates that better remote and hybrid policies could save UK employers many billions annually through reduced attrition and improved recruitment. The logic is straightforward: when workers strongly value flexibility, organizations that offer it do not have to constantly replace experienced staff or overpay to lure new ones into rigid schedules.

Employer surveys tell a similar story. The CIPD’s resourcing and talent planning report, based on over 1,000 HR leaders, reports that organizations offering hybrid or remote options are more likely to say they can attract and retain the talent they need. Many also report widening their recruitment radius far beyond commuting distance, effectively arbitraging regional wage differences and tapping skills that would otherwise be out of reach. Hybrid work, in other words, is a talent strategy in its own right.

For executives worried that performance will inevitably suffer, there is now rigorous experimental evidence to consider. A large experiment at Trip.com randomly assigned some teams to work from home two days per week while others stayed in the office full-time. The Stanford-led analysis of this hybrid work trial found that resignations among non-managers fell by one third and employees reported higher satisfaction, with no measurable decline in performance reviews, promotions or output. Across multiple studies summarized in the evidence on working from home, fully remote arrangements in certain roles can reduce output, but hybrid patterns that combine planned in-office collaboration with quiet at-home focus time tend to deliver flat or slightly positive average productivity.

This nuance is central for public debate in Washington, where “remote work” is often treated as a single category. The UK inquiry stresses that the real question is job design: which tasks genuinely require co-location, how often teams need face-to-face time and how office space can be reshaped to favor team rooms, mentoring spaces and project hubs over endless assigned desks, as described in its committee summary. That approach invites federal agencies and large companies to rethink their real estate footprints and performance management systems together, instead of treating office attendance as a proxy for output.

If the case for hybrid work is so strong, why is Washington locked in recurring battles over return-to-office mandates and telework “crackdowns”? Part of the answer is oversight anxiety. The latest government-wide telework report to Congress shows that more than half of eligible federal employees teleworked in fiscal 2023, and highlights benefits for continuity of operations, recruitment and morale. At the same time, agencies and watchdogs have raised serious concerns about inconsistent data, lax controls and gaps between badge swipes, time sheets and telework agreements, as described on OPM’s own telework program hub. That mix of success and sloppiness fuels political backlash.

Oversight reports point toward repair, not retreat. A 2025 Government Accountability Office review on federal remote work notes that CFO Act agencies reduced office space as telework expanded between 2020 and 2024, but also criticizes the lack of systematic evaluation of how remote work affects recruitment, retention and mission outcomes. A separate inspector general evaluation of OPM’s telework oversight finds that many agreements were expired or missing and that compliance checks were uneven, even as telework remained central to operations. Both reports call for stronger internal controls and clearer guidance rather than scrapping remote work entirely.

Here, the House of Lords offers a model for a better conversation. Its hearings found that blanket four- or five-day in-office mandates encode a crude trade-off between collaboration and staff satisfaction and often miss the underlying reasons for worker preferences. The committee highlights the role of “anchor days” when whole teams come in together, targeted recognition for hidden collaborative work such as mentoring and redesigned offices that guarantee teams can actually sit together, rather than fighting with seat-booking systems that scatter colleagues across floors.

For Congress and corporate boards, that points to a concrete agenda. Keep hybrid eligibility tied to job content and performance, not seniority or political mood. Invest in secure, easy-to-use collaboration tools and clear data on outcomes. Align leases and office layouts with realistic occupancy and the collaboration patterns teams actually need. And treat telework abuses as management failures to be fixed with better oversight, not as justification for policies that ignore the documented gains in labor supply, disability employment, retention and real estate efficiency.

Packed offices read well in soundbites, but the record from London and from American researchers points in another direction. Hybrid work, handled seriously, offers governments and companies a way to widen the labor pool, strengthen inclusion, reduce churn and shrink wasted space without sacrificing performance. Washington can chase nostalgia for 2019, or it can use that evidence to build a smarter, leaner and more inclusive model of work that actually matches how high-value jobs get done.

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By Dr. Gleb Tsipursky 

The bubble thesis has a problem: the customers keep showing up with real money. Nvidia’s latest results put a hard edge on the argument, with the AI investment boom producing record quarterly revenue of $81.6 billion and data center revenue of $75.2 billion. Those numbers do not prove that every AI stock is sensibly priced or that every enterprise pilot will pay off. They do prove that demand for AI infrastructure is not a social media hallucination, a conference-room fad, or a temporary burst of demo-driven enthusiasm.

The smarter conclusion is more nuanced and more useful for executives. The AI bubble hypothesis explains pockets of excess, valuation anxiety, and copycat spending. It does not explain the scale of adoption, the capital formation, or the emerging productivity evidence now visible across the economy.

Demand Is Behaving Like Infrastructure, Not Hype

Speculative bubbles tend to float above operating reality. AI is sinking into it. The Federal Reserve’s 2026 review of enterprise AI adoption found that about 18% of U.S. firms had adopted AI by the end of 2025, while work-related generative AI use among individuals stood near 41%. A separate senior-leader survey cited in the same analysis estimated that 78% of the labor force works at firms that have adopted AI.

That breadth matters because bubbles depend on a widening gap between belief and use. AI is moving in the opposite direction. Stanford’s 2026 AI Index Report shows an ecosystem shifting from experiment to buildout, with corporate investment, frontier-model revenue, cloud capital spending, and consumer value all rising together.

The most important signal is not one company’s earnings. It is the alignment between chip demand, cloud expansion, model deployment, enterprise experimentation, and worker usage. AI infrastructure spending looks less like tulip mania than like the early stages of an industrial platform. Railroads, electricity, broadband, and cloud computing all required heavy upfront investment before their highest-value applications became obvious. AI is following that pattern, though at a faster and more volatile pace.

That distinction should make leaders more confident investing into AI, not more reckless. The point is not to chase every shiny model or vendor claim. The point is to recognize that the foundation is becoming durable enough to justify serious operating commitments.

The Real Risk Is Weak Implementation, Not AI Itself

The best argument for caution is not that AI lacks value — it is that many organizations still fail to capture it. MIT’s 2025 State of AI in Business research on generative AI ROI highlighted a sharp divide between widespread pilots and successful production deployments, especially for custom enterprise tools. That finding should sober executives, but it should not freeze them.

The lesson is managerial, not technological. Companies that paste chatbots onto broken workflows usually get novelty. Companies that redesign processes, connect AI to proprietary data, clarify accountability, and measure performance get leverage.

McKinsey’s 2025 global survey found that 88% of respondents reported regular AI use in at least one business function, yet only 39% reported enterprise-level EBIT impact. The same research found that high performers were much more likely to redesign workflows, embed AI into business processes, track KPIs, and invest in talent, data, and operating model changes. In other words, AI business value is not magic. It is built.

This is where executive confidence should become sharper. The failure pattern is visible. The success pattern is visible too. Leaders do not need blind faith in AI. They need the discipline to stop funding disconnected pilots and start backing fewer, better programs tied to revenue growth, margin expansion, customer experience, risk reduction, and speed.

That also means changing the investment conversation. A serious AI leadership strategy starts with business architecture, not software procurement. The winners will not be the companies with the longest list of AI tools. They will be the companies that rebuild how work gets done.

Confident Leaders Will Govern The Buildout, Not Avoid It

The bubble debate often tempts executives into a false choice between exuberance and paralysis. Neither is appropriate. AI markets can contain excess while AI itself becomes indispensable. The internet produced both Pets.com and Amazon. Cloud computing produced both wasteful migrations and durable operating advantage. The same split is already appearing in AI.

PwC’s 2025 Global AI Jobs Barometer gives leaders another reason to stay engaged. Its analysis of nearly a billion job ads found that industries most exposed to AI saw productivity growth nearly quadruple after the spread of generative AI, while revenue per employee grew three times faster in the most AI-exposed industries than in the least exposed ones. Those AI productivity gains are early, uneven, and still contested, but they are too material to dismiss.

The right response is governed acceleration. Boards should ask for use-case economics, data-readiness plans, cybersecurity controls, vendor concentration reviews, model-risk protocols, and workforce adoption metrics. Finance teams should demand milestones. Business leaders should own outcomes. Technology teams should build reusable platforms rather than one-off demos.

That is how AI capital spending becomes strategic investment instead of theater. The goal is not to spend because competitors are spending, but rather to build capabilities that compound: better data pipelines, faster product cycles, more responsive service operations, stronger forecasting, lower error rates, and higher employee leverage.

This is also why waiting for perfect clarity is risky. By the time every AI use case has a clean benchmark and every valuation concern has settled, the leading firms will already have redesigned workflows, trained teams, negotiated infrastructure access, and learned from mistakes. AI transformation rewards accumulated learning, and accumulated learning takes time.

Conclusion

The AI bubble argument is not foolish. Markets can overprice real revolutions, and executives should distrust any strategy built on fear of missing out. But the stronger evidence now points to something larger than hype: real adoption, real infrastructure demand, real productivity signals, and a widening gap between organizations that experiment casually and those that execute seriously.

AI should make leaders more confident because the investment case is no longer built only on imagination. It is increasingly built on operating evidence. The mandate is not to spend blindly. It is to invest with conviction, govern with rigor, and move fast enough to learn before competitors turn AI from an experiment into an advantage.

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