The economic fortunes of mom-and-pop businesses are diverging from those of their larger counterparts — a pre-existing gap that now appears to be getting bigger, faster.
Why it matters:
The evidence is in the private-sector labor market, that in recent months, has been propped up by large companies as smaller firms — typically responsible for 40% of U.S. employment — shed workers.
The big picture:
Larger businesses have been able to adapt to a tough economic backdrop — historic tariffs, high interest rates and a more cautious consumer — in ways far more challenging for small companies with fewer resources.
“It’s evident that medium and large firms are better positioned to weather what’s going on,” said ADP chief economist Nela Richardson.
“They can set prices, they can change suppliers. They can hire contractors instead of permanent employees in a more sophisticated way. They can hire globally, not just in their local region. They have more tools in the toolbox,” Richardson said.
By the numbers:
The hiring gap between small and big businesses is getting worse, a fresh sign that small business firings are holding down jobs growth across the economy.
As we mentioned yesterday, the private sector shed 32,000 jobs in November, according to payroll processor ADP. Small firms — those with fewer than 50 employees — accounted for all of the losses.
Those businesses reported a net loss of 120,000 jobs, the most small businesses have cut since the pandemic’s onset. Larger businesses grew, but not enough to offset the cuts elsewhere.
“Small business hiring reallystarted to slow in April and I attribute some of this to tariffs and the higher cost of doing business that small companies are much less able to absorb,” Peter Boockvar, chief investment officer at One Point BFG Wealth Partners, wrote in a note.
“The natural reaction is to cut costs elsewhere and we know that labor is their biggest cost,” Boockvar added.
The intrigue:
Bloomberg recently reported that there are more small businesses filing for bankruptcy under a special federal program this year than at any point in the program’s six-year history.
Subchapter V filings, which allow firms to shed debt faster and cheaper, are up 8% from last year, according to data from Epiq Bankruptcy Analytics.
Chapter 11 filings — a process used by larger businesses — are up roughly 1% over the same time frame.
Threat level:
Main Street is bearing the brunt of an economic slowdown in ways that might make it even harder for small shops to compete with larger companies.
One bright spot: Despite that pain, applications to start new businesses — ones likely to employ other people — remain notably higher than in pre-pandemic times, according to the latest data available from the Census Bureau.
What to watch:
The Trump administration shrugged off the ADP data that indicated a hiring bust. Commerce Secretary Howard Lutnick told CNBC that the cuts were due to factors unrelated to tariffs, like immigration crackdowns.
That hints at a debate among monetary policymakers, who are trying to gauge how much weak jobs growth is a byproduct of fewer available workers.
But ADP had earlier told reporters that small businesses generally had less demand for workers — not that staff weren’t available for hire.
The software monopoly that powers American hospitals wasn’t built for the data, speed, or intelligence the future of medicine demands.
Epic Systems is an American privately held healthcare software company, founded by Judy Faulkner in 1979, and has grown into the largest electronic health record (EHR) vendor by market share, covering over half of all hospital patients in the U.S.
Epic dominates American healthcare today. But so did Kodak in photography and GE in industry. Its software runs the country’s hospitals, determines the workflows clinicians, nurses and clinical support staff use, and shapes what data gets captured (or more often, what gets lost). It also serves as the front door for healthcare data for the patients it serves. Dominance has never guaranteed a future. Epic’s position reflects the architecture of the past, not the one emerging now.
More importantly, the sheer volume of activity occurring in these hospitals means they are collectively running thousands of experiments, mini clinical trials, and critical observations daily. The stakes are enormous: billions of dollars in drug discovery, the efficiency of clinical trials (currently plagued by poor recruitment and high costs), and the potential for better, personalized care. The data generated in these environments is the single most valuable, untapped resource in all of medicine.
However, this monumental source of value is being throttled by outdated infrastructure, and it shows. It’s hard to imagine a world where AI is used to its full potential in healthcare while Epic is still running the show. The ideas are oppositional at their core.
The Massive Data Problem
Technology is accelerating faster than any legacy system can keep up with. AI is reshaping every major industry, and healthcare will be forced to catch up. However, this essential transformation is structurally incompatible with the dominant system of record.
To put it bluntly: Epic has a data problem. A massive data problem. Not just imperfect data — structurally flawed data. What Epic captures is fragmented, delayed, and riddled with inconsistencies. Diagnoses become billing codes that distort reality. Interventions like intubations, pressor starts, and ventilator changes appear hours late, if at all. Outcomes are incomplete or missing. What remains isn’t a clinical record in any meaningful sense but a billing ledger dressed up as documentation. No model can learn reliably from that.
But the deeper problem is the data Epic never sees. Some of the most valuable information in modern medicine: continuous monitoring streams, ventilator logs, infusion pump data… never enters the EHR in a structured or analyzable form. In many cases, it isn’t captured at all.
I recently brought Roon (a well-known engineer at OpenAI) and Richard Hanania(a public intellectual/cultural critic)—both advisors in my new venture, in full disclosure—to one of the largest academic medical centers in the country. Both watched torrents of millisecond-scale data spill off monitors. Streams that could reveal what’s happening in the brain, heart, and vasculature. Valuable data… all vanished instantly. None of it logged. None of it stored. None of it correlated with outcomes. Roon captured this shock in a viral post on X/Twitter, essentially describing how hospitals are filled with catastrophic events like sudden cardiac death, yet we save none of the time-series data that could teach us how to prevent the next one. His shock distilled what people in technology grasp immediately and what healthcare has normalized: industries where human life isn’t exactly top of mind record everything; hospitals, where the stakes are life and death, learn almost nothing from themselves.
In Silicon Valley, losing data like this is unthinkable. In healthcare, it barely registers.
Epic was never built to ingest or learn from this scale of data. It was built to satisfy billing requirements, regulatory checklists, and documentation workflows. That is the beginning and end of its architecture. It is not a learning system, much less an AI system. It is not even a modern data system. And that is the root of Epic’s downfall.
The Cultural and Financial Moat
Epic is famous for its internal commandments — principles Judy Faulkner wrote decades ago:
Do not acquire.
Do not be acquired.
Do not raise outside capital.
(If you haven’t heard it, the latest Acquired podcast episode on Epic is essential listening)
But the same rules that built its empire now limit what it can become. What was once a strategic strength is now its ceiling.
The next era of healthcare software demands investments that were unnecessary when the EHR was the center of gravity. Building AI-native infrastructure: real-time data pipelines, device integrations, large-scale compute, continuous model training, semantic normalization — requires not millions but tens of billions of dollars. Most companies facing that kind of leap can raise capital, acquire talent, or merge with partners. Epic has ruled all of those options out.
Epic’s formidable market share is anchored by a massive customer sunk cost. With implementation fees often exceeding a billion dollars for large systems, the financial and political inertia makes replacing the EHR functionally unthinkable. However, this commitment only forces customers to defend an obsolete data architecture. By preventing them from adopting novel solutions, this inertia doesn’t protect Epic’s long-term viability, it simply guarantees a widening technical gap between the EHR and the transformative potential of AI.
A company optimized for slow, controlled expansion cannot transform itself into an AI-scale enterprise without violating the principles that define it. The culture that kept Epic dominant is the culture that prevents it from catching the next wave. Epic will continue to excel at documentation, billing, and compliance — but those strengths are anchored in the past. The future belongs to systems that learn, and Epic was never designed to learn.
The Shift to Middleware
Meanwhile, the broader economy is being held up by AI. The world’s largest tech companies are pouring staggering sums into compute, data centers, and model training. And all that compute needs rich, complex, high-value data to train on.
Healthcare is the only remaining frontier of that scale.
No other industry generates so much information while analyzing so little of it. No other sector represents nearly 20% of U.S. GDP yet still runs on fragmented workflows and manual processes. And the incentives here are unmatched: improving patient outcomes, reducing costs, eliminating inefficiency, accelerating drug development, modeling disease trajectories, and eventually automating the more repetitive layers of care. There’s even an irony: the very infrastructure needed to enable learning health systems would also finally make billing more accurate.
I’m not writing this to showcase some utopian vision of AI curing all disease. It’s the practical use of technology we already possess. Our limitation isn’t the models; it’s the missing data.
A handful of companies have bet their trillion-dollar valuations on this: OpenAI, Google, Amazon, Nvidia, Apple, Oracle. They are spending hundreds of billions a year on AI infrastructure and need high-volume, high-quality datasets to justify that investment. Healthcare produces oceans of exactly that kind of data, and most of it evaporates. The companies that learn to capture and structure it will define the next layer of healthcare infrastructure. Whether they integrate with Epic, build around it, or replace it is almost secondary.
What matters is that none of them are waiting for Epic.
Clinicians won’t either. Once tools exist that unify the data hospitals already generate, reduce workload, eliminate administrative drag, and answer the questions clinicians actually ask — What happened? Why did it happen? What should we do now? — the center of gravity will shift. Clinicians will live inside those tools, not inside an interface built for billing.
Epic can still exist, but it doesn’t need to function as healthcare’s operating system. There’s precedent for this in every major industry: the core orchestration/data layer eventually recedes into the background while workflow and data intelligence move up the stack. At that point, the EHR becomes background infrastructure or middleware. The intelligence/workflow layer becomes the real operating system. Epic will undoubtedly resist this shift, yet its attempts to maintain total control of the clinician interface will ultimately collide with the utility and data gravity of AI-native systems.
Epic becomes the backend: essential, invisible, and no longer the place where the practice of medicine occurs.
Regulatory modernization around HIPAA, interoperability, and data liquidity will be essential, but that is a conversation for another essay.
Epic isn’t vanishing tomorrow. Large institutions rarely do. But its relevance is eroding in the only domain that will matter over the next decade: the ability to harness data at a scale and fidelity that makes AI transformative. It can keep its commandments, preserve its culture, and reject outside capital — it just can’t do all that and remain the central platform of hospital data in an AI-native future.
For an economy that’s rapidly expanding, the usual drivers of job creation sure aren’t carrying their weight.
Why it matters:
Anemic job growth in key sectors is a sign that there is more underlying weakness in worker demand than the low unemployment rate might suggest.
It makes for a weaker starting point, as companies see new opportunities around the corner to use AI to automate their work.
It’s not a new trend: These sectors showed weak job creation or outright job losses for the last couple of years of the Biden administration.
But it is striking that a GDP surge fueled by data center and AI investment hasn’t been enough to generate more robust hiring.
By the numbers:
Overall employment is up 0.8% over the 12 months ended in September, but the hiring has been driven in significant part by health care, state and local government, and other less cyclical sectors.
Manufacturing employment is down 0.7% over the last 12 months. Tariffs are weighing on the sector, but its job losses long predate the Trump trade wars, with year-over-year job losses for more than two years.
Temporary help employment, which tends to be a volatile indicator underlying growth trends, is down 3%. It has been losing jobs for three consecutive years.
Two other sectors that tend to correlate with overall economic momentum, transportation and warehousing and wholesale trade, are also adding jobs at rates below that of overall job growth (0.6% and 0.2%, respectively).
Stunning stat:
As Bloomberg flagged, two sectors — health care and social assistance, and leisure and hospitality — accounted for more than 100% of net job gains so far in 2025.
Excluding those sectors, employment dropped by 6,000 jobs in the first nine months of the year.
Zoom out:
There’s not much reason to think these numbers are driven by AI-related opportunities for companies to increase productivity and rely on fewer human workers, particularly given that the phenomenon isn’t new.
But it is more plausible that seeing such opportunities on the horizon has made companies more reluctant to hire in the absence of overwhelming need.
BlackRock chief investment officer for global fixed income Rick Rieder wrote in a note after last week’s jobs report that “what we think we are seeing now is … essentially a hiring pause in anticipation of AI.”
Of note:
A report out this morning from the McKinsey Global Institute finds that AI and robotics technologies could, in theory, automate 57% of U.S. work hours.
“AI will not make most human skills obsolete, but it will change how they are used,” the authors find. “As AI takes on common tasks, people will apply their skills in new contexts,” they write, such as less time researching and preparing documents and more time framing questions and interpreting results.
The bottom line:
Beneath the headline numbers, there is some good reason that attitudes toward the job market are glum.
Layoff trends in 2025 indicate an increase in job cuts compared to 2024, with US employers announcing nearly 950,000 cuts through September, the highest number since 2020. Key drivers include cost-cutting measures, the strategic implementation of artificial intelligence (AI), and a cooling labor market.
Key Trends
Elevated Numbers: Total US job cuts through October 2025 were over one million, a 65% increase from the same period in 2024. October 2025 had the highest number of layoffs for that month in 22 years.
AI as a Primary Driver: AI adoption is a leading cause for job cuts as companies restructure for efficiency and reallocate resources. Companies like Amazon and Intel have cited AI as a reason for significant workforce reductions.
“Forever Layoffs”: A new trend involves smaller, more regular rounds of layoffs (fewer than 50 people) that create ongoing worker anxiety and impact company culture. These rolling cuts often stay out of headlines but contribute significantly to the overall job cuts.
Method of Notification: The process is becoming more impersonal, with many employees being notified of their termination via email or phone call rather than in-person meetings.
Hiring Slowdown: Alongside the layoffs, there has been a sharp drop in hiring plans, with planned hires for the year at their lowest level since 2011.
Affected Industries
While tech has been significantly impacted since late 2022, other industries are also facing substantial cuts in 2025:
Technology: Remains a leading sector for cuts as companies continue to restructure after pandemic-era overhiring and focus on AI.
Retail and Warehousing: Companies like Target and UPS are cutting thousands of jobs due to changing consumer demands, automation, and a push for efficiency.
Energy and Manufacturing: Oil giants such as Chevron and BP are making cuts as part of cost-reduction strategies and market consolidation.
Finance and Consulting: Firms like PwC and Morgan Stanley are trimming staff, citing factors like low attrition rates and the need to realign resources.
Media and Communications: Companies like CNN and the Washington Post have made cuts to pivot toward digital services and reduce costs.
Economic Context
The overall U.S. labor market remains relatively healthy despite the uptick in layoffs, though it is showing signs of cooling. The unemployment rate has inched up, and consumer sentiment has declined. The Federal Reserve is monitoring the situation and has implemented interest rate cuts to help stabilize the job market.
Artificial general intelligence (AGI) refers to AI systems that can match or exceed human cognitive abilities across a wide range of tasks, including complex medical decision-making.
With tech leaders predicting AGI-level capabilities within just a few years, clinicians and patients alike may soon face a historic inflection point: How should these tools be used in healthcare, and what benefits or risks might they bring? Last month’s survey asked your thoughts on these pressing questions. Here are the results:
My thoughts:
I continue to be impressed by the expertise of readers. Your views on artificial general intelligence (AGI) closely align with those of leading technology experts. A clear majority believes that AGI will reach clinical parity within five years. A sizable minority expect it will take longer, and only a small number doubt it will ever happen.
Your answers also highlight where GenAI could have the greatest impact. Most respondents pointed to diagnosis (helping clinicians solve complex or uncertain medical problems) as the No. 1 opportunity. But many also recognized the potential to empower patients: from improving chronic disease management to personalizing care. And unlike the electronic health record, which adds to clinicians’ workloads (and contributes to burnout), GenAI is widely seen by readers as a tool that could relieve some of that burden.
Ultimately, the biggest concern may lie not with the technology, itself, but in who controls it. Like many of you, I worry that if clinicians don’t lead the way, private equity and for-profit companies will. And if they do, they will put revenue above the interests of patients and providers.
Thanks to those who voted. To participate in future surveys, and for access to timely news and opinion on American healthcare, sign up for my free (and ad-free) newsletter Monthly Musings on American Healthcare.
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Dr. Robert Pearl is the former CEO of The Permanente Medical Group, the nation’s largest physician group. He’s a Forbes contributor, bestselling author, Stanford University professor, and host of two healthcare podcasts. Check out Pearl’s newest book, ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine with all profits going to Doctors Without Borders.
“We urge the Administration to consider the timing of these policies in the context of the broader scope of requirements and challenges facing the industry that require significant system changes.”
AHIP, March 13, 2023 (in a letter to CMS Administrator Chiquita Brooks-LaSure responding to CMS’s proposed rule on Advancing Interoperability and Improving Prior Authorization Processes, proposed Final Rule, CMS-0057-P)
“Health insurance plans today announced a series of commitments to streamline, simplify and reduce prior authorization – a critical safeguard to ensure their members’ care is safe, effective, evidence-based and affordable.”
After lobbying aggressively to delay implementation of the PA reforms proposed by the previous administration (successfully delayed one year and counting), AHIP, the big PR and lobbying group for health insurers, now claims the mantle of reformer, announcing a set of voluntary commitments to streamline prior authorization.
So naturally, the industry’s “commitments” deserve closer scrutiny. Let’s unpack them. As a former health insurance industry executive, I speak their language, so allow me to translate. AHIP, which has no enforcement power, by the way, claims that 48 large insurers will:
Develop and implement standards for electronic prior authorization using Fast Healthcare Interoperability Resources Application Programming Interfaces (FHIR APIs).Translation:CMS is already requiring all insurers to do this by 2027. We might as well take credit preemptively.
Reduce the volume of in-network medical authorizations. Translation:We already demand hundreds of millions of unnecessary prior authorizations for thousands of procedures and services, so cutting a few (who knows how many?) should be a layup and won’t cut into profits.
Enhance continuity of care when patients change health plans by honoring a PA decision for a 90-day transition period starting in 2026.Translation:We’re already required to do this in Medicare Advantage. And since we delayed implementation of e-authorization until 2027, we’re in the clear until then anyway.
Improve communications by providing members with clear explanations for authorization determinations and support for appeals. Translation:We’re already required by state and federal law to do this. We’ll double-check our materials.
Ensure 80% of prior authorizations are processed in real time and expand new API standards to all lines of business. Translation:We had to promise to hold ourselves accountable to at least one measurable goal. We will set the denominator – we’ll decide which procedures and medications require PA – so we’ll hit this goal, no problem, and we might even use more non-human AI algorithms to do it.
6. Ensuring medical review of non-approved requests. Translation:People will be relieved we’re not using robots. And we’ll avoid having Congress insist that reviews must be done by a same-specialty physician, as proposed in the Reducing Medically Unnecessary Delays in Care Act of 2025 (H.R. 2433).
Of course, I wasn’t in the room when AHIP drafted these commitments, so take my translations with a grain of salt. But let’s be honest: These promises are thin on specifics, short on accountability, and devoid of measurable impact.
It’s also rich coming from an industry still reliant on something called the X12 transaction standard – technology that is now over 40 years old – to process prior authorization requests, while simultaneously pointing the finger at providers for outdated technology and being slow to adopt modern systems. Many insurers did not start accepting electronic submissions of prior authorization until roughly 2019, nearly 20 years after clinicians started using online portals such as MyChart in their regular practice. The claim that providers are the ones behind on technology is another ploy by insurers to dodge scrutiny for their schemes.
We shouldn’t settle for incremental fixes when the system itself is the problem. Nor should we allow the industry that created this problem – and perpetuates it in its own self-interest – to dictate the pace or terms of reforming it.
As we argued in our recent piece, Congress should act to significantly curtail the use of prior authorization, limiting it to a narrow, evidence-based set of high-risk use cases. Insurers should also be required to rapidly adopt smarter, lower-friction cost-control methods, like gold-carding trusted clinicians (if it can be implemented with integrity and fairness), without compromising patient access or clinical autonomy.
Letting the fox design the hen house’s security perimeter won’t protect the hens. It’s time for Congress to build a better fence.
HENRY KOTULA 