The Technology Jobs Everybody Thinks Disappeared Were Never Here

The technology jobs everybody thinks disappeared were never here

Years of headlines about tech layoffs have created a widely held belief that computer and technology jobs are vanishing from the U.S. economy. The occupation-level data says otherwise — and the reason for the gap between perception and reality turns out to be about where those jobs are actually classified, not whether they exist. Data: BLS Occupational Employment and Wage Statistics (OEWS), SOC 15-0000 “Computer and Mathematical Occupations,” 2004–2024; DOL/OFLC H-1B LCA disclosure data by SOC code, 2010–2024.

SOC 15-0000 employment, 2004 → 2024
2.92M → 5.19M
+2.28 million jobs, +78%, over 20 years
2023 → 2024 net change
+15,490
Smaller than most years on record, but still a gain — not a loss
2024 H-1B LCAs, SOC 15
566,993
63.7% of all H-1B/E-3/H-1B1 LCAs filed that year
Where those jobs actually sit
NAICS 54: 35.4%
vs. just 14.0% in NAICS 51 “Information” — the sector most layoff headlines come from
National employment, Computer & Mathematical Occupations (SOC 15-0000), 2004–2024

This is every worker BLS counts in computer and mathematical occupations nationwide — software developers, systems analysts, IT support, data scientists, network engineers, and the rest of SOC 15 — regardless of what industry employs them. The only down years on record are 2009–2010, during the financial crisis. Every other year, including 2022–2024, the years most associated in the press with mass tech layoffs, employment grew.

2.5M3.0M3.5M4.0M4.5M5.0M5.5M20042006200820102012201420162018202020222024
The recent slowdown is real, but it’s a slowdown, not a decline. 2022 alone added 349,160 jobs to this occupation — the single best year on record. 2023 added another 173,490. 2024 added just 15,490 — a sharp deceleration that lines up with the layoff headlines — but the total kept climbing. Nationally, the occupation has not shrunk.
H-1B applications for computer & math occupations (SOC 15), by year

The same years show sustained, large H-1B activity specifically in this occupation. This is LCA filing volume — it includes renewals, extensions, amendments, and employer transfers, not just new hires, so it should not be read as “this many new workers.” What it does show is the program’s scale and persistence in exactly the occupation this report is about, running alongside the employment trend above rather than in place of it.

0K100K200K300K400K500K600K158K’10183K’11234K’12274K’13337K’14426K’15450K’16427K’17446K’18459K’19399K’20549K’21417K’22416K’23567K’24
H-1B/E-3/H-1B1 LCAs filed for SOC 15 occupations (thousands)
2009 is excluded here — that year’s H-1B table shows an unexplained, sharply lower total record count than every surrounding year (consistent with an incomplete import rather than a real one-year collapse), so it isn’t reliable enough to chart. Data resumes cleanly at 2010.
Jobs added vs. H-1B applications, side by side, 2010–2024

This is the two charts above overlaid year by year: the actual net change in national SOC 15-0000 employment that year (blue, can go negative — it does in 2010), against H-1B/E-3/H-1B1 LCAs filed for SOC 15 that same year (orange). H-1B volume is larger than net job growth in every single year shown, sometimes by a wide margin — 2024 is the starkest, 567,000 applications against just 15,000 net jobs added, roughly 37 to 1. That gap is expected and shouldn’t be read as “H-1B replaced 37 native jobs for every 1 created” — most LCA filings are for workers already in the country (renewals, extensions, employer transfers), not brand-new positions, so the two bars aren’t counting the same thing. What the comparison does show plainly: the program’s activity in this occupation has been consistently large relative to the occupation’s actual net growth for a decade and a half, including in the one year — 2024 — where that growth nearly stalled.

-100K0K100K200K300K400K500K600K-20K158K’10123K183K’11172K234K’12118K274K’13138K337K’14171K426K’15160K450K’1696K427K’17123K446K’18169K459K’1935K399K’2067K549K’21349K417K’22173K416K’2315K567K’24
Net change in SOC 15-0000 employment that year (can be negative)
H-1B/E-3/H-1B1 LCAs filed for SOC 15 that year
Where computer & math jobs actually sit, by industry — 2024

This is the part that explains the gap between perception and reality. When people picture “tech jobs,” they picture NAICS 51 — Information: software publishers, internet companies, telecom, media — the sector most mass-layoff headlines come from. But that sector accounts for only 14.0% of the nation’s computer & math occupation workforce. More than twice as many — 35.4% — sit in NAICS 54, Professional, Scientific & Technical Services: IT consulting, computer systems design services, engineering services, staffing firms. Those companies rarely make headlines because they’re vendors and contractors serving every other industry, not household names. The rest are scattered across finance, manufacturing, government, education, and virtually every other sector on the list — because by 2024, writing code and running systems is not a “tech industry” job, it’s a function every industry does in-house.

Prof., Scientific & Technical Services (NAICS 54)1,838,900 (35.4%)Information (NAICS 51)724,520 (14%)Finance and Insurance (NAICS 52)512,670 (9.9%)Management of Companies (NAICS 55)370,290 (7.1%)Manufacturing (NAICS 31-33)312,540 (6%)Government (fed/state/local, excl. schools) (NAICS 99)300,740 (5.8%)Admin & Support Services (NAICS 56)269,000 (5.2%)Educational Services (NAICS 61)256,410 (4.9%)Wholesale Trade (NAICS 42)203,560 (3.9%)Health Care & Social Assistance (NAICS 62)158,440 (3.1%)Transportation & Warehousing (NAICS 48-49)56,380 (1.1%)Retail Trade (NAICS 44-45)47,540 (0.9%)
NAICS 54 — Professional, Scientific & Technical Services
NAICS 51 — Information
All other sectors
What this actually means

Two things are both true at once, and they don’t contradict each other. Real, visible layoffs happened at real, recognizable companies — mostly ones classified under NAICS 51, Information, which is exactly the slice of the economy that generates headlines when it cuts staff. And at the same time, national employment in computer & mathematical occupations grew in 19 of the last 21 years, including the exact years the layoffs made news, because that sector is only 14% of where these jobs live. The other 86% — consulting firms, finance, manufacturers, hospitals, universities, government agencies, retailers — mostly kept hiring, they just don’t put out press releases when they do. The jobs people think disappeared didn’t disappear. Most of them were never concentrated in the industry people were watching in the first place.

Data: BLS Occupational Employment and Wage Statistics (OEWS), National estimates and National Industry-Specific estimates, SOC 15-0000, May 2004–May 2024 · DOL/OFLC H-1B, H-1B1 & E-3 LCA disclosure data by SOC code, 2010–2024.

Discussing H-1B on Twitter and YouTube: A Year in the Analytics

Discussing H-1B on Twitter and YouTube: a year in the analytics

A year of posting H-1B/guest-worker-visa reform content on X and YouTube felt like it stopped landing sometime in early 2026. Rather than guess why, this pulls the platforms’ own analytics — account and video-level exports from X, and full lifetime YouTube Studio data — to see what actually happened. Kept here as a reference to check back against as the next round of posting (including bringing back “jobs direct”) gets underway.

X reach per post, Aug 2025 vs. Jul 2026
8.0K → 1.7K
Impressions per post, at nearly identical posting volume (452 vs. 454 posts/month).
YouTube lifetime (since May 2022)
25,845 views · 301 subs
227 videos published. Growth concentrated almost entirely in Nov 2025–Feb 2026.
Best subscriber-converting video
51 of 301 subs
One video, “jobs direct,” drove 17% of the channel’s entire lifetime subscriber count.
X (Twitter) reach per post, by month

Impressions per post, not just total impressions — so this isn’t a story about posting less. August 2025 and July 2026 both ran almost exactly 450 posts for the month; reach per post fell roughly 5x anyway. The biggest single-day spikes (300–460K impressions) landed on days tracking real national H-1B news moments — the fee/lottery reform fights in September 2025 and January 2026 in particular — which is a “riding the news cycle” signature more than a steady baseline getting throttled. The renewed dip from February through May 2026 coincides with running for U.S. Senate and a stretch spent upgrading recording equipment rather than posting — both of which pulled time away from the account directly, separate from anything the platform’s algorithm was doing.

0K2K4K6K8K8.0KAug7.1KSep5.5KOct3.5KNov1.9KDec5.0KJan2.7KFeb1.8KMar1.2KApr1.4KMay2.4KJun1.7KJul
Impressions per post (thousands), Aug 2025 – Jul 2026
X-native video views, by month

X tracks video separately from overall post impressions. The pattern is the same shape, but the mechanism is clearer here: 19 of the year’s 30 X-native videos were posted in December 2025 alone, then posting nearly stopped (1 in May, 2 in June, 4 in July). The two best individual clips — 10,539 and 5,098 views — are an 11-second and a 17-second clip posted the same day, Jan 25, 2026, which is real proof that short vertical clips are the one format that has reliably outperformed everything else tried on the account. This is a supply problem more than a reach problem: the format works when used, it just wasn’t kept up.

0K10K20K30K40K50K8.7KAug52.6KSep32.3KOct28.4KNov41.2KDec15.9KJan1.7KFeb4.0KMar49Apr412May1.0KJun2.2KJul
X-native video views (thousands), Aug 2025 – Jul 2026
Plan: resurrect “jobs direct” in the next few days — it’s the single best subscriber-converting piece of content across either platform (see below), and reviving it deliberately, rather than letting it sit, is the highest-leverage next move available.
YouTube: where views actually come from

Lifetime totals since the channel started May 12, 2022: 25,845 views, 448 watch hours, 301 subscribers, across 227 videos. Nearly all of that growth landed in the same Nov 2025–Feb 2026 window as the X surge — the same campaign-driven pattern showing up independently on a second platform. The traffic-source breakdown below is the more useful finding: YouTube’s own recommendation system (“Browse features,” the home feed) is the second-largest source at 34.6% of all views, run at a healthy 5.1% click-through rate — the algorithm is actively distributing this content, not burying it. “External” (39.1%, the largest single source) is traffic from links shared elsewhere — meaning reach still depends heavily on personally driving people there. The one genuine weak point: “Suggested videos” gets shown 25,089 times but converts at only 0.9% click-through, the lowest of any source — the algorithm is offering the exposure, the thumbnail/title isn’t winning the click in that context.

External (links you share)10,109 (39.1%)Browse features (home feed)8,954 (34.6%)Channel pages1,914 (7.4%)Direct or unknown1,809 (7.0%)Shorts feed1,128 (4.4%)YouTube search971 (3.8%)Other YouTube features348 (1.4%)Suggested videos334 (1.3%)Notifications160 (0.6%)Playlists115 (0.4%)
YouTube: top 10 videos by lifetime views

Four things stand out. “jobs direct” (#2) drove 51 of the channel’s 301 total subscribers — 17% of every subscriber the channel has ever had, from one video, despite a mediocre 2.2% click-through rate; whatever is converting viewers there is worth understanding and repeating on purpose. “346,000 Texas Kids” (#6) has the best retention by far at 70% average view completion on a 20-second clip — a hook that holds people to the end. “How to use the state map…” (#1) has the best click-through rate at 9.0%, double the channel average. And “Between Foreign Workers and American Workers” (#10) shows the opposite problem: 457 views but only 5.4% retention — people click and leave almost immediately, the clearest example on the channel of a title/thumbnail overpromising what the video delivers in its first few seconds.

# Title Published Views Watch hrs Subs gained Avg view % Impressions CTR
1 How to use the state map to find employers who hire H-1B’s in any state Jan 8, 2026 2,372 66.4 5 24.9% 16,749 9.0%
2 jobs direct Aug 17, 2025 2,321 28.2 51 21.5% 835 2.2%
3 Ken Paxton says he’s tough on immigration and putting Americans first Feb 26, 2026 1,634 7.3 2 32.0% 628 4.0%
4 If you want jobs in Texas & America, please share with everybody including those not on social media Feb 3, 2026 1,553 14.0 2 51.7% 2,590 3.5%
5 H-1B History Files, Jobs and Sharia Law in Houston TX Jun 26, 2026 1,382 12.0 8 47.4% 23,735 4.4%
6 346,000 Texas Kids. Where are their jobs? Nov 15, 2025 957 5.4 3 70.0% 14,290 5.3%
7 Amazing how our population growth has been decreasing since we started sending our jobs overseas Dec 7, 2025 646 8.6 0 21.6% 8,202 5.8%
8 Lets talk about the United States Navy Mar 1, 2026 591 10.9%
9 How can I survive on social security of $1,380 per month? Dec 1, 2025 475 25.6%
10 Between Foreign Workers and American Workers, who gets the jobs? May 29, 2026 457 5.4%
What this actually means

Reach on both platforms tracks two things more than anything else: how much time was personally spent posting, and whether H-1B happened to be a national story that week. Neither platform’s data supports “the algorithm is hiding this” as the main explanation — YouTube’s own recommendation system is actively distributing this content at a solid click-through rate, and X’s biggest spikes line up with real news moments, not a stable audience getting cut off. What the data does show clearly: short video is the highest-leverage format on both platforms, a small number of specific videos already prove a winning formula (hook, thumbnail, subject) exists, and the 1,000-subscriber threshold that’s felt so far away is much more a function of posting consistency than of any ceiling being imposed from outside. That’s a more encouraging problem to have — it’s one a steady posting cadence can actually solve.

Data: X (Twitter) account and video analytics exports, Jul 31, 2025 – Jul 30, 2026 · YouTube Studio channel analytics, lifetime (since May 12, 2022), pulled Jul 30, 2026.

Is the labor-shortage number a big-business number?

Is the “labor shortage” number a big-business number?

JOLTS job-openings data gets cited constantly to argue employers can’t find domestic workers — the same argument used to justify sending jobs overseas and importing labor on guest-worker visas. We tested whether the survey behind that number is actually built from large-firm responses. Sources: BLS JOLTS establishment-size research series (Dec 2000–May 2026) and DOL OFLC H-1B/H-1B1/E-3 LCA disclosure data (FY2026 Q1–Q2).

Share of job openings from 5,000+ employee establishments
3.2%
May 2026, Total Private. Barely moved from 2.0% in 2019 and 1.9% in 2015 — a stable feature of the data, not a recent shift.
JOLTS survey response rate, 2020 → 2024
58% → 33%
And per BLS/Federal Reserve research, response is lower in the largest size classes — those estimates are the least reliable, not the most.
“Certainty stratum” in the sample design
100% sampling
Every establishment above a size threshold is included with certainty by design — it’s the small ones that get randomly subsampled.
Where the nation’s job openings actually sit

JOLTS classifies every sampled unit into one of six employment-size classes and publishes job-openings levels for each. This is establishment size — a single physical location — not firm size. Read at face value, the headline “can’t find workers” number is overwhelmingly a small-and-mid-business number: locations under 250 employees account for ~77% of measured openings.

0% 8% 16% 24% 32% 20.0% 1–9 30.4% 10–49 26.6% 50–249 12.3% 250–999 7.5% 1K–5K 3.2% 5K+
Response rates cut the other way too: BLS and Federal Reserve researchers have both flagged that the largest size classes have the lowest survey response, meaning the already-small 5,000+ slice is also the noisiest one in the published data.
Establishment ≠ firm: why “large employer” doesn’t mean “large establishment”

A firm is one or many establishments under a common EIN. A retail or restaurant chain with 50,000 employees shows up in JOLTS as thousands of separate small locations — most falling into the “under 250” buckets above. A company that concentrates its U.S. workforce into a handful of huge campuses shows up as a few units in the certainty stratum instead. Using this project’s own H-1B/LCA disclosure data, distinct worksite addresses per employer vary enormously — illustrating just how differently “large employer” maps onto establishment size class from one company to the next.

0 450 900 1,350 1,800 sites Ernst & Young 1,710 Infosys Limited 496 Apple 283 Oracle America 274 Amazon Web Services 206 Cisco Systems 170 LinkedIn 39 Qualcomm Technologies 29

Distinct worksite street addresses named across each employer’s FY2026 LCA filings. Not a headcount measure — DOL’s position-count fields turned out to be batch-filing ceilings (e.g. Qualcomm’s LCAs are routinely filed in round lots of 100), not confirmed hires, so we didn’t use them for anything requiring real magnitude.

The gap nobody outside BLS can check: JOLTS never publishes an industry × establishment-size cross-tabulation — not for tech, not for any sector. So the sharper version of the “big offshoring-heavy employers drive the shortage number” claim can’t actually be tested against public JOLTS data, by us or anyone else. That’s a real transparency limit in the dataset, not evidence either way.
Data: BLS JOLTS Estimates by Establishment Size Class, Total Private, Dec 2000–May 2026 (seasonally adjusted where applicable) · DOL OFLC H-1B/H-1B1/E-3 LCA disclosure data, FY2026 Q1–Q2 · BLS Handbook of Methods; San Francisco Fed Economic Letter, Mar 2025, on JOLTS response rates.

Are Job Openings Turning Into Real Hiring? A Tech-Sector Check

Are job openings turning into real hiring? A tech-sector check

Testing whether elevated JOLTS job openings actually convert to hires in tech-heavy sectors, and how H-1B tech hiring volume compares to the broader trend. Seasonally adjusted BLS JOLTS data (Information & Professional/Business Services) and DOL H-1B LCA disclosure data (Computer & Mathematical occupations, SOC 15-xxxx), 2020-2026.

Info sector: openings vs hires gap (last 12mo avg)
+0.8 pts
Openings rate minus hires rate. Positive = postings outpacing hires.
Prof/Biz Services: same gap (last 12mo avg)
+0.9 pts
Pre-2020, this sector’s hires rate typically ran ABOVE openings.
H-1B tech volume vs JOLTS tech hires, latest
101 vs 92
Mar 2026 index values (Jan 2020 = 100 each)
Openings rate vs. hires rate, by sector

If openings run persistently above hires, postings aren’t converting into hires at the historical rate. Pre-2020, hires typically ran above openings in both sectors below — that relationship has flipped since.

Information
0.0%2.3%4.7%7.0%’20’21’22’23’24’25’26
Professional & Business Services
0.0%2.3%4.7%7.0%’20’21’22’23’24’25’26
Openings rate
Hires rate
H-1B tech hiring volume vs. JOLTS tech-sector hires (indexed, Jan 2020 = 100)
JOLTS tech-sector hires (indexed)
H-1B tech LCA volume (indexed)

H-1B volume = certified LCAs for Computer & Mathematical occupations, by decision month (DOL disclosure data). JOLTS = combined Hires level, Information + Professional/Business Services (BLS). Both indexed to their own Jan 2020 value so two different-unit series can share one axis.

0501001502002502020202120222023202420252026Shutdown / backlog
Oct-Nov 2025 spike/dip: the Oct 2025 federal lapse in appropriations paused DOL case processing; decisions backlogged and were pushed through in November once government reopened. Read that pair of months as one disrupted period, not real volume swings.
Data: BLS JOLTS (national industry series, seasonally adjusted) · DOL OFLC H-1B/H-1B1/E-3 LCA disclosure data, FY2020-FY2026.