The crypto industry may be relatively small in terms of employers — but the economic contribution is big.
That’s according to a new report published by the National Cryptocurrency Association and the Pragmatic Policy Group, which reveals that while only 34,000 people are employed by crypto companies, the industry will contribute $55 billion in 2026 to the U.S. economy.
The report, “Crypto at Work”, which claims to be the first to comprehensively analyze the crypto industry’s footprint in the U.S. labor market, said that jobs in the space also average $133,000 a year — more than double the $64,000 national median wage, and ahead of average pay in tech of and manufacturing.
“Crypto creates many jobs outside the tech industry and directly supports more jobs than key manufacturing industries,” the report said.
Using a standard input-output economic model, PPG calculated that every direct crypto job supports roughly six additional jobs elsewhere in the economy — at suppliers, and at businesses where crypto workers spend their paychecks.
Stacking those indirect and induced jobs on top of the direct total produces a figure of 232,000 jobs in total that the industry supports.
By raw headcount, though, crypto remains a small employer. The report itself benchmarks its 34,000 direct jobs against coffee and tea manufacturing (28,400 jobs) and tobacco manufacturing (10,600 jobs) — hardly the scale of a major American industry.
The industry’s footprint is also geographically lopsided: California, New York, and Texas account for 60% of all crypto jobs, with 57,600, 53,800, and 26,500 respectively.
Heartland states—Iowa, Kansas, Nebraska, and the Dakotas among them — together support just over 17,000 jobs. The report singles out Colorado and North Dakota as rising hubs, pointing to Colorado’s crypto-friendly tax policy and firms like Riot Platforms and Crusoe Energy, and North Dakota’s flare-gas mining operations and a pilot stablecoin from the state-owned Bank of North Dakota.
PPG describes the study as the first comprehensive, economy-wide look at crypto’s labor market impact, built on 2024 Bureau of Economic Analysis and Bureau of Labor Statistics data.
The firm also flagged a limitation in its own approach: because “a dedicated crypto workforce profile does not yet exist,” it modeled crypto’s financial activities using the occupational mix of broader technology industries rather than traditional finance.
NCA, which funded the research, said it hopes the findings give policymakers “an evidence-based understanding of the sector’s economic contribution.” The nonprofit launched in 2025 to promote what it describes as safe, informed cryptocurrency adoption in the U.S.
If you're a skilled writer with outsize technology chops who gets excited by the idea of taking the Ars audience with you as you go hands-on with hardware—all kinds of hardware!—then this position has your name all over it. Plus, you get to have me as your boss, and how could that be anything other than awesome?!
The job
The formal job description and application is right here and has all the specifics and HR stuff, including salary range. The short summary is that we're looking for an experienced writer (where "experience" means "several years of professional work"), who is a technologist first and foremost. We want people who tinker around with tech because they can't not tinker around with tech; that kind of joy tends to leak out into the work, and it's impossible to fake.
Some specifics on the subject matter the job will cover, copied from the job description:
When we last sat down with Jobs at TechCrunch Disrupt nearly three years ago, his firm Yosemite was brand new and biotech was still reeling from its post-pandemic crash. Now, the venture outfit has a team of 17; a cluster of blockbuster drugs are all losing patent protection in roughly the same window, creating all kinds of new opportunities; and AI has gone from a curiosity to, in Jobs' words, a huge part of what Yosemite does. "I didn't expect Yosemite to be moving this fast," he said.
New data from Ramp and Revelio show that intensive Gen AI adoption is linked to higher headcount and more entry-level hiring, challenging fears that “AI kills jobs” and reshaping IT leaders’ strategies.
New data from Ramp and Revelio show that intensive Gen AI adoption is linked to higher headcount and more entry-level hiring, challenging fears that “AI kills jobs” and reshaping IT leaders’ strategies.
The Silent Disappearance of Entry-Level Jobs in the AI Economy: A Generation’s First Career Ladder Is Breaking
As artificial intelligence reshapes industries at scale, the traditional entry-level job is quietly fading forcing young professionals to rethink how careers begin, not just how they grow.
Years went by with one clear path. Study hard, finish school, then start at the bottom. The jobs were never flashy, yet each became the base of something bigger.
Confidence grew there, slowly. Mistakes happened often and that was okay, skills formed not on paper, but while working, hands-on, day after day that structure is now under pressure.
Now machines handle jobs like typing numbers, answering questions, or writing reports tasks people once learned on the job. Firms rely more on tools that never sleep, cutting costs while speeding things up.
These starting-point duties disappear, replaced by silent software doing ten jobs at once. Learning by doing fades when algorithms take over day one. Speed wins, but newcomers lose footing before they start.
What happens next might surprise you a quiet shift that shrinks entry-level chances over time. Not sudden, yet clear when you look closely.
The Vanishing First Step
True, positions aren’t vanishing overnight. Yet the baseline for entry keeps moving. Back then, new analysts would pass months fixing data errors, setting up sheets, one task after another stacking up. Now? Much of that work finishes itself overnight, handled silently by smart software while workers sleep.
A strange situation shows up here. Workers with skills remain necessary, yet firms look only at those who’ve done the job before. Getting that history usually means starting at the bottom. Now that path is falling apart.
Out here, fresh grads hold degrees tight in hand yet stumble into jobs asking for years they do not have. Paper credentials mean little when every opening wants proof of time served.
AI Changed How We Learn by Changing What Learning Is Worth
What really changes goes beyond machines taking tasks. Experience built through practice now holds less worth in jobs. Back then, companies saw slow progress as part of bringing in fresh workers.
It took a beginner more hours to finish work, yet those extra minutes were considered building something. Instant results come first these days. As machines handle jobs in moments, there is less room for people to catch up slowly. Training fades into the background when performance matters most.
This shift sneaks into decisions without noise. Rather than bringing on a pair of newcomers meant to evolve, firms now lean toward a single seasoned worker backed by artificial intelligence aids.
The New Entry Barrier Skills Without a Safety Net
Surprisingly, skill still matters just as much since AI showed up only now you have to know more before you even begin.
These days, fresh applicants must understand software tools and processes that used to be picked up slowly at work. Instead of waiting for training, people now learn by doing small jobs, trying things alone, or showing real examples of their efforts. Hiring based on proof of skill is spreading fast.
Still, that change widens gaps in who gets hands-on chances. Some never touch actual work tasks before landing a role. Old-school company learning setups are fading quicker than new routes appear to fill them.
What’s Actually Disappearing
True, some beginner roles still exist. Not every starting position has disappeared overnight. A few openings remain, though harder to spot.
Still, low-barrier jobs aren’t gone for good. Just shifted, not erased entirely. Fading now is the workplace where learning packed every moment, yet demands stayed light. Not gone yesterday, but slipping where growth crowded in, while pressure kept its distance.
We are seeing, Fewer positions aimed only at beginners More hybrid “mid-level from day one” expectations Greater reliance on automation for foundational tasks Increased demand for self-sufficiency from new hires
Simply put, firms aren’t pushing out new hires they’re dismantling spaces that welcome them.
The Human Cost of Efficiency
Hidden in the shift, a small price slips through unnoticed by efficiency charts. Starting out meant more than a paycheck it built habits through daily routines. Mistakes happened here without serious consequences, talking with coworkers became routine, slowly shaping how tasks got done. Showing up on time mattered, just like meeting set dates.
Working alongside others revealed different approaches to shared work. Over time, handling pressure grew easier.
Fewer safety nets mean young workers often pick things up on the job, where errors cost more and patience runs thin. A whole group of people grows up knowing tools well yet rarely facing how offices truly function. Adaptation, Not Extinction Just because things have changed does not mean chances vanish instead, they shift shape.
A different path opens when the old one bends; possible routes begin to show up where none existed before, project-based hiring instead of role-based hiring, Apprenticeship models in tech and business, Portfolio-driven recruitment and AI-assisted onboarding instead of traditional training programs
Freelance and micro-internship ecosystems replacing early corporate roles What counts as entry-level now depends less on how long someone has worked and more on what they can actually do.
The Bigger Question Ahead
What matters now isn’t if machines take beginner roles. That shift happened quietly, in pieces.
Here lies a different puzzle altogether. What steps in when work stops teaching people how to grow?
Back when they were just starting out, even seasoned experts had to begin somewhere. Should those early steps grow tougher to take, fewer people will make it through over time a slow fade few notice until it’s too late.
Out here, machines aren’t pushing people out of jobs. They’re reshaping how skills grow in the first place. That shifts how things stand now.
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