• 978,539 Tranco top-1M domains fingerprinted for their tech stack from homepage HTML + response headers, of which 80.4% (787,205) resolved a working homepage — one snapshot, one domain, one record, deduplicated by run.
• Cloudflare alone fronts 36.7% of every reachable site (359,429 domains) — more than every other CDN provider combined, and roughly 9× the next-largest named CDN, Amazon CloudFront (5.0%).
• WordPress runs 73.1% of every site with a detected CMS — 148,707 of 203,498 CMS sites, or 18.9% of the whole dataset. No other CMS breaks 7% of the CMS category; Drupal is a distant second at 6.9%.
• 8.9% of sites (70,086) embed a CAPTCHA widget — and that number independently corroborates our separately-run Anti-Bot Adoption Index, which measured 9.4% of a different top-1M reachable set carrying a CAPTCHA-kind signature from a completely different detection method (challenge-response, not static HTML fingerprint).
• Shopify (29,374 sites) and WooCommerce (24,964) are effectively tied atop the ecommerce category — WordPress’s plugin ecosystem lets WooCommerce punch close to a dedicated hosted platform’s share.
• Nginx fronts 18.1% of all reachable sites directly (142,602) — more than every other named web server combined, and second only to Cloudflare as an infrastructure signature.

Search “WordPress market share” or “Cloudflare market share” and W3Techs owns the answer — it has for a decade, and there’s no reason to compete with that for the generic query. What we can do instead: run our own independent fingerprint pass across the Tranco top-1M, publish the method openly, and — because we also run a separate anti-bot detection index — cross-validate one number that no CMS-tracking site can replicate: how many sites present a CAPTCHA, measured two completely different ways, arriving at nearly the same answer.

Method at a glance

• Universe: the Tranco top-1,000,000 domain list, one fingerprint pass per domain (run_id=top1m-2026-07-08-full), scoped so every domain is counted exactly once - the underlying index holds two other overlapping passes (a recheck retry and a proxy re-scan) that are excluded here to avoid double-counting.
• Fingerprinting: homepage HTML + response headers, matched against a technology-signature database (CDN, CMS, ecommerce platform, analytics, JS framework, web server, and more) — the same category of detection BuiltWith and Wappalyzer use.
• Reachability: 787,205 of 978,539 domains (80.4%) resolved a working homepage on this pass; the rest timed out, refused the connection, or errored. A separate recheck pass recovers 3,625 more on retry, but headline figures here use the single full pass only.
• CAPTCHA measurement: a proxy count — any of four known CAPTCHA signatures (reCAPTCHA, hCaptcha, Cloudflare Turnstile, Arkose Labs) detected in the static HTML fingerprint. This is deliberately a different method from the Anti-Bot Adoption Index’s challenge-response probing, which is what makes the cross-check meaningful.
• Published under /datasets/techstack, queryable over Crawlora’s REST API.
• Snapshot dated July 8, 2026; detector version 2026.07.5.

Who fronts the web

Route through a CDN and you’re in the majority: 59.5% of all 978,539 sites in the dataset do. But “CDN market” understates how concentrated that majority actually is — one vendor dominates completely.

Inside the CDN category, Cloudflare is not a leader — it’s nearly the entire market:

Cloudflare sits in front of 36.7% of every domain in the entire 978,539-site dataset — not just the CDN subset — making it, alongside “runs some web server,” the single most common piece of shared infrastructure on the internet. This is the same concentration the Anti-Bot Adoption Index found from the opposite direction: Cloudflare covers 45% of reachable sites there too, just measured as a bot-management layer instead of a CDN.

WordPress doesn’t have a rival — it has a long tail

Among the 203,498 sites with any detected CMS, WordPress alone accounts for 148,707–73.1% of every CMS site on the web, or 18.9% of the entire dataset regardless of CMS status. No other platform gets within striking distance:

The gap between #1 and #2 here (73.1% vs. 6.9%) is wider than almost any other technology category in the dataset — WordPress isn’t competing with Drupal or Joomla so much as it’s competing with the long tail of custom-built and headless sites that carry no detectable CMS signature at all.

The plugin economy keeps WooCommerce in the same league as Shopify

Ecommerce is the one category where WordPress’s dominance doesn’t fully carry over — because WooCommerce is a plugin bolted onto a general CMS, not a purpose-built store platform, and it still lands almost tied with Shopify at the top:

The cross-check: CAPTCHA prevalence, measured two different ways

This is the finding a pure tech-stack tracker can’t produce, because it requires a second, independently-run dataset. 70,086 sites in this fingerprint pass — 8.9% of the 787,205 reachable domains — embed a CAPTCHA widget (reCAPTCHA, hCaptcha, Cloudflare Turnstile, or Arkose Labs), detected by matching known script and DOM signatures in the static HTML.

Separately, Crawlora’s Anti-Bot Adoption Index scanned a different top-1M pass ( top1m-20260614) using an entirely different technique - probing each site's live response and challenge behavior rather than reading static HTML - and found 9.4% of its 818,614 reachable sites carrying a CAPTCHA-kind signature (76,755 sites):

A 0.5-point gap between a passive HTML-signature read and an active challenge-response probe is close enough to call these independent confirmations of the same underlying rate, rather than two unrelated numbers — the small difference is exactly what you’d expect, since some sites embed a CAPTCHA widget that a static crawl can see but never actually triggers during a passive probe, and vice versa. Neither dataset was built to validate the other; they were run at different times, by different teams internally, using different signatures. Getting to within half a point anyway is the strongest evidence either study has that its CAPTCHA number is real and not a detector artifact.

Behind the CDN, Nginx quietly runs a fifth of the web

Web-server fingerprinting is the category where BuiltWith-style tools disagree most, because so many servers hide their identity behind a CDN. Where a server signature is exposed, Nginx dominates:

Apache’s near-absence here (59 domains, effectively rounding to zero of the detected server category) doesn’t mean Apache has died — it means the sites still running it overwhelmingly sit behind a CDN that strips or replaces the Server header before it reaches a fingerprinting pass. Only 15.7% of the entire dataset exposes any web-server signature at all.

What this means if you’re building on top of the open web

For competitive and market-share research, the practical lesson is that WordPress’s 73.1% CMS share and Cloudflare’s 76.2% CDN share are now closer to defaults than choices — a “which platform should I build for” question is increasingly “how do I work well inside WordPress and behind Cloudflare,” not “which of five roughly-equal options do I pick.” For anti-bot and scraping teams, the CAPTCHA cross-check is the operational takeaway: roughly 1 in 11 sites you’ll ever crawl embeds some CAPTCHA widget, confirmed by two independent measurements — plan for CAPTCHA-solving capacity at that rate, not as a rare edge case. And for anyone fingerprinting their own competitive set, this dataset is exactly the kind of “who runs what” ground truth that’s normally locked behind a paid BuiltWith/Wappalyzer export — here it’s one REST API call.

For an ecommerce-specific cut of this same fingerprint — Shopify vs. WooCommerce: the CDN gap nobody talks about — see how CDN adoption and TLD distribution diverge sharply between the two leading platforms.

Sources

Frequently asked questions

What is the most common CDN in 2026?

Cloudflare, by a wide margin. Fingerprinting the Tranco top-1M, Cloudflare fronts 36.7% of every site in the dataset (359,429 domains) and 76.2% of the CDN category specifically — more than every other named CDN combined, roughly 9x the next-largest, Amazon CloudFront.

What percentage of websites use WordPress?

In Crawlora’s July 2026 fingerprint of 978,539 Tranco top-1M domains, WordPress runs 18.9% of the whole dataset and 73.1% of every site with a detected CMS. No other CMS breaks 7% of the CMS category — Drupal is a distant second at 6.9%.

What percentage of websites use a CAPTCHA?

8.9% (70,086 of 787,205 reachable sites) embed a CAPTCHA widget by static HTML fingerprint. That figure independently corroborates Crawlora’s separately-run Anti-Bot Adoption Index, which measured 9.4% of a different top-1M reachable set via challenge-response probing — two different detection methods landing half a point apart.

Shopify vs WooCommerce — which is more common?

They’re close to tied: Shopify runs 42.2% of detected ecommerce storefronts (29,374 sites) and WooCommerce 35.9% (24,964 sites) in the July 2026 fingerprint. WooCommerce’s showing is notable since it’s a WordPress plugin, not a dedicated hosted platform, yet still nearly matches Shopify.

How was this tech stack data collected?

Homepage HTML and response headers for the Tranco top-1,000,000 domains were fingerprinted against a technology-signature database (CDN, CMS, ecommerce, analytics, web server and more) — the same category of detection BuiltWith and Wappalyzer use. 80.4% of domains resolved a working homepage; every figure is scoped to a single scan pass to avoid double-counting.

Originally published on crawlora.net. Crawlora is a structured web-data, search, and anti-bot API — dozens of platforms as normalized JSON, plus a hosted MCP server, with a free tier (no card).

Originally published at https://dev.to on July 27, 2026.

• We built an 8-year reachability panel of 172,959,928 registered domains from 80 monthly Common Crawl archives (January 2018 to May 2026) — every domain Common Crawl ever fetched a robots.txt for, tracked crawl by crawl.
• About 40 million domains — roughly 23% of the 8-year set — are calibrated genuinely dead: no DNS, or nothing accepts a connection. That is the headline, and it is a number you have to earn.
• Disappearing from a crawl is not the same as dying. Of the 62.9 million domains that vanished, only 64.2% are actually dead when you re-probe them live; 31% still resolve and 4.7% are merely blocking bots. Counting every disappearance as death would put the dead web at 36% instead of 23%.
• Dead and blocked are different failures, and we can finally measure the split: a domain last seen blocked is only 44.7% likely to be dead, versus 70.7% for one last seen healthy. Naive crawls collapse the two.
• This is not link rot and not ‘dead internet theory.’ We measure whether the domain itself still answers — not broken links inside living pages, and not who or what is writing the content.

There is a clean, satisfying way to measure the dead web, and it is wrong. Take a big list of domains from a few years ago, check which ones still show up today, and call the missing ones dead. Common Crawl makes this almost too easy: it has published a monthly snapshot of the web for years, so you can line up the archives and watch domains drop out.

We did exactly that — 80 monthly Common Crawl archives, from January 2018 to May 2026, covering 172,959,928 registered domains — and then we did the part everyone skips. We took the domains that had disappeared and asked them, live, whether they were actually gone. A third of them answered. The honest dead-web number is about 40 million domains, ~23% of the eight-year set — but only because we refused to count a domain as dead just because Common Crawl stopped seeing it.

Eight years of the web, sorted into four fates

Every domain in the panel gets one label from its full history across the 80 crawls. alive_present means it was still there in the most recent crawl. dead_candidate means it was established for a while, then went dark from a healthy state and never came back. dark_ambiguous means it went dark too, but the last thing we saw was a block or a server error - a murkier exit. intermittent means it only ever appeared sparsely, so its absence proves nothing.

Two slices need care before anyone reaches for a headline. The 38% intermittent is the honest disclaimer: Common Crawl is a sample, not a census, and its frontier shifts every month, so a domain that shows up in three scattered crawls and then never again has not necessarily died — it may simply have fallen out of the sample. We do not count those as deaths. That leaves the 36% that genuinely disappeared after a real run of presence (the dead_candidate and dark_ambiguous slices together, 62.9 million domains) - and even those, it turns out, are not all dead.

Disappearing isn’t dying

Here is the trap, in one chart. If you stop at the Common Crawl archives and call every vanished domain dead, you get 36%. When you take a stratified 20,000-domain sample of those vanished domains and re-probe each one live — a fresh DNS lookup, a TCP connection, an HTTP request, in June 2026 — only 64.2% of them are actually unreachable. Project that back and the real dead-web figure is about 23%.

The gap is not noise; it is the whole methodology. A domain leaves a crawl for many reasons that have nothing to do with death: the crawler’s budget moved on, the site started blocking robots, a redirect chain confused the fetcher, or the domain simply wasn’t sampled that month. A disappearance is a hypothesis, not a finding. Calibration — going back and checking a representative sample against the live internet — is what turns “62.9 million domains went missing” into a defensible “about 40 million are actually dead.”

So what did the vanished domains turn out to be when we knocked on the door in 2026?

Nearly a third of the domains that fell out of Common Crawl are still alive — they resolve and serve a page right now. They left the crawl, not the web. That single fact is why headline dead-web numbers built on disappearance alone run high, and why ours is deliberately, defensibly lower.

Dead and blocked are different failures

The other thing a live re-probe buys you is the distinction naive crawls erase: dead (nobody home) versus blocked (home, but won’t open the door for a bot). Both look like “couldn’t fetch it” to a scraper, and they demand opposite responses — a dead domain will never answer no matter what you do, while a blocked one opens for the right client. The probability a vanished domain is truly dead depends heavily on the last healthy signal we saw before it went dark.

Read the bottom bar carefully, because it is the contrarian finding: a domain last seen blocking bots is barely more likely than a coin flip to actually be dead. Almost half of those “disappeared while blocking” domains are alive and well — they just walled off the crawler and then dropped out of the sample. A domain last seen healthy, by contrast, is over 70% likely to have genuinely died: a healthy site goes quiet because it was switched off, not because it started refusing robots. If you treat every failed fetch the same, you write off a pile of live, defended sites as corpses.

A disappearance is a hypothesis. A live probe is the test.

The entire 13-point gap between “36% vanished” and “23% dead” is domains that left a sample of the web without leaving the web. Common Crawl is a moving sample, not a registry — so the only honest way to turn “stopped appearing” into “died” is to re-probe a representative slice against the live internet and measure the false-positive rate. We did, and it is about a third.

This is not “dead internet theory” — and not link rot

Three different questions get blended together whenever the web’s decay comes up. Keeping them apart is the point:

• This study (dead domains): does the domain still resolve and answer at all? Across an 8-year, 173-million-domain panel, about 23% no longer do.
• Link rot (Pew, Ahrefs): are the links inside still-living pages good? Pew Research found 38% of pages from 2013 had vanished by 2023; Ahrefs found two-thirds of tracked links rot. That measures decay within the living web — a complement to this, not the same number.
• Dead internet theory: the claim that bots and AI-generated content have displaced humans on the living web. That is about what’s on the pages that still load — a separate conversation entirely.

If you remember one line: link rot is about the pages that are still up; the dead web is about the domains that aren’t; dead internet theory is about who’s writing the pages that are. This study is strictly the middle one.

How we measured it

The method is deliberately simple and fully reproducible — and the open-source tool that runs it is the same one behind this post.

The corpus. Common Crawl publishes a columnar index for each monthly crawl, including a robotstxt subset - one row per host whose robots.txt it fetched. We read that subset for all 80 crawls from CC-MAIN-2018-05 to CC-MAIN-2026-21, rolled each up to the registered domain, and recorded, per crawl, whether the domain was present and what HTTP status it returned. The union is 172,959,928 domains.

Why disappearance, not status. Common Crawl’s recorded fetch_status is HTTP-only - a domain that is genuinely dead produces no row at all, not a row with an error. So death cannot be read from a status code; it has to be inferred from a domain dropping out across consecutive crawls. That inference is exactly what makes calibration mandatory.

The labels. For each domain we compute its first and last crawl, how many of the 80 it appeared in, its longest run of recent absence, and the last state we saw. A domain that was present for several crawls and then absent from the most recent ones — from a healthy state — is a dead_candidate; from a blocked or erroring state, dark_ambiguous; sparsely-seen domains are intermittent and never counted as deaths.

The calibration. We drew a stratified 20,000-domain sample of the vanished domains and re-probed each one live in June 2026 — DNS resolution, a TCP connection, and an HTTP request — classifying it as dead, alive, or blocked. Per-stratum dead rates (with Wilson 95% confidence intervals) convert the raw “this many disappeared” counts into the calibrated “this many are truly dead.” That step is the difference between 36% and 23%.

Limits, honestly. Common Crawl is a sample of the web, not a census, and its coverage drifts over eight years — which is precisely why we calibrate rather than trust disappearance, but it also means the panel is biased toward domains Common Crawl chose to crawl, and a domain it never fetched a robots.txt for never enters the set. The live re-probe is a single datacenter vantage in mid-2026, so a domain that blocks datacenter IPs can read as blocked when a residential browser would get in, and a parked domain that serves a 200 reads as alive. The dead-rate projection assumes the 20k sample's per-stratum rates hold across each full stratum. Snapshot: the panel ends May 2026; the re-probe ran June 2026.

The full per-domain panel — all 172.9 million rows, every label and field — is open under CC BY 4.0, and the CLI that built it is open source. If you want the live, interactive view of today’s web instead of the 8-year history, the companion Dead-Web Index and the snapshot study 14% of the Web Is Actually Dead measure the same thing on the current top-ranked web.

Sources

Frequently asked questions

How much of the web has died since 2018?

About 23%. We built an 8-year reachability panel of 172,959,928 domains from 80 monthly Common Crawl archives (2018–2026); roughly 40 million are calibrated genuinely dead — no DNS, or nothing accepting a connection. That figure is much lower than the 36% you get by counting every domain that simply stopped appearing in the crawl, because disappearing from a sample of the web is not the same as leaving it.

Does disappearing from Common Crawl mean a domain is dead?

No. Common Crawl is a moving sample of the web, not a registry, so a domain can drop out because the crawler’s budget shifted, it started blocking robots, or it simply wasn’t sampled — not because it died. When we re-probed a stratified 20,000-domain sample of vanished domains live in 2026, only 64.2% were genuinely dead; 31% still resolved and answered, and 4.7% were alive but blocking bots.

What’s the difference between a dead domain and a blocked one?

A dead domain never answers — no DNS record, or nothing accepts a TCP connection. A blocked domain is alive and answering; it just refuses an automated client with a 403, 429, or anti-bot challenge. They demand opposite responses (a dead domain never answers no matter what; a blocked one opens for the right client), and a domain last seen blocking bots is only 44.7% likely to be dead, versus 70.7% for one last seen healthy.

Is this the same as the dead internet theory?

No. The dead internet theory is the claim that bots and AI-generated content have displaced humans on the living web — a question about what’s on the pages that still load. This study measures something concrete and different: whether the domain itself still resolves and answers at all. It is also distinct from link rot, which measures broken links inside still-living pages.

How was the dead-web Common Crawl dataset built?

From Common Crawl’s monthly robotstxt index subset across 80 crawls (CC-MAIN-2018–05 to CC-MAIN-2026–21), rolled up to one row per registered domain with its presence and HTTP-status history. Because Common Crawl’s status is HTTP-only, a dead host produces no row at all, so death is inferred from disappearance and then calibrated against a live re-probe. The full 172.9M-row panel is open under CC BY 4.0 and the crawlora-deadweb CLI that built it is open source.

Originally published on crawlora.net. Crawlora is a structured web-data, search, and anti-bot API — dozens of platforms as normalized JSON, plus a hosted MCP server, with a free tier (no card).

Originally published at https://dev.to on July 27, 2026.

• We re-ran our App Store AI study at 5.7× the scale — 8,505 apps that call themselves ‘AI’, up from 1,490 — and the picture got bleaker: the bigger the sample, the deader the tail.
• The median ‘AI app’ has just 10 ratings. 87% have fewer than 1,000. Eight have more than a million. It isn’t a market; it’s a lottery.
• The flood is accelerating, not slowing: 1,434 of these apps launched in 2023, 1,798 in 2024, 2,890 in 2025 — and 2026 is already past 1,400 at the half-year.
• It’s built by ~6,600 developers, but a few app farms industrialize it — one studio (ZipoApps) ships 26 different AI apps.
• Inequality is near-total (Gini 0.98). ChatGPT alone still holds ~18% of every AI-app rating; the median app has 10.
• Honest read: a median of 10 ratings means ignored, not bad. Most of these apps were simply never found by anyone.

A few weeks ago we pulled 1,490 App Store apps that call themselves “AI” and found a brutally top-heavy market: a few giants, a long tail of clones. The most common pushback was fair — your sample is too small.

So we went back and pulled 5.7× more: 8,505 unique AI apps, gathered by paginating ~100 AI search terms across every sub-category and brand until the App Store stopped surfacing new ones. It’s the near-complete searchable universe of AI apps. The findings didn’t soften with scale. They got worse — because a bigger net mostly catches more of the dead.

(Method note, as before: the stores rate-limit and fingerprint plain scrapers, so we read them through a structured web-data API. US storefront, June 2026. “AI app” is a strict tag — explicit AI/GPT/assistant signals or a named AI brand — over the search results; caveats at the end.)

Picture the App Store’s AI apps as 100 squares

Eighty-eight of them are ghosts. Each square below is about 85 apps, colored by how many ratings it has ever collected:

That grey field is the whole story. Most “AI apps” aren’t competing for users — they were never found by any.

A median of 10 ratings

Here’s what scaling the sample did to the typical app. When we had 1,490 apps, the median one had 171 ratings. At 8,505, the median is 10:

The median dropped 17× not because 2026’s apps are worse than the ones we saw first, but because the first, smaller pull skimmed the visible head. Dig deeper and you find what’s actually there: a near-bottomless tail of apps with single-digit ratings.

From 11,000 results to 8 giants

Run the whole thing as a funnel. About 11,000 apps surface for AI searches; 8,505 are genuinely AI; of those, a few hundred have a real audience and eight have a massive one:

The flood is accelerating, not slowing

You might assume the AI-app gold rush peaked with ChatGPT’s debut. It didn’t. By launch year, 2025 produced more AI apps than 2023 and 2024 — and 2026 is on pace to break the record:

The app farms

Most of these apps come from someone’s only app — ~6,600 developers for 8,505 apps, 83% of them one-and-done. But a handful of studios have industrialized the gold rush, each shipping a dozen or two near-identical AI apps:

What they all are

Cluster the 8,505 by what each app actually does and it’s a narrow menu — chatbots, photo generators, and an enormous pile of AI companions:

How concentrated is it?

Rank all 8,505 apps by rating count and the inequality is near-total — a Gini of 0.98. Five apps hold 38% of every rating; a hundred hold 82%; everything else shares the scraps:

The most-rated “AI” apps are the same incumbents-with-a-feature we saw last time — ChatGPT (8.0M), Canva (3.4M), DuckDuckGo (2.3M), Gemini, Gauth, Microsoft Copilot, Picsart. The 8,400 apps behind them are not, mostly, competing with those. They’re invisible.

How we did this (and the caveats)

We gathered the corpus by paginating ~100 AI-intent search terms through a structured App Store API (US storefront, June 2026), deduping to 10,958 unique apps, then strict-tagging 8,505 as genuinely AI (explicit AI/GPT/assistant signals or a named AI brand — not the bare word “chat”, which over-counts messaging apps). Caveats, plainly: this is the discoverable search universe, not a true census, so the real tail is even longer and the inequality even higher than the Gini 0.98 we measured — every figure here is a lower bound. Rating count is a proxy for scale, not exact installs. The strict tag still catches incumbents that bolted AI on (DuckDuckGo, Canva, Goodnotes). And we make no claim that the long tail is bad — a median of 10 ratings means undiscovered, which is a very different thing from a bad app.

The deeper read is simple: the first study undercounted the graveyard. At 1,490 apps the median had 171 ratings; at 8,505 it has 10. Keep digging and that number keeps falling — because the App Store’s “AI” layer is overwhelmingly apps that nobody ever opened.

This is the kind of thing we read for a living. Crawlora is a web-data API for AI agents and pipelines that returns normalized JSON for the App Store, Google Play, search engines, marketplaces, and social platforms — handling proxies, rendering, and anti-bot — billed pay-on-success. The crawl behind this post was ~100 API calls and 16 minutes. We’ve since indexed the whole catalog — 4.1M apps across the App Store and Google Play — browsable in our mobile app dataset, and track the daily winners and losers in the App Store + Google Play charts dataset.

Sources

Frequently asked questions

How many AI apps are on the App Store?

We found 8,505 discoverable AI apps (June 2026, US App Store), gathered by paginating about 100 AI search terms — 5.7 times more than our earlier 1,490-app study. The true number is higher still; this is the searchable universe, not an exhaustive census.

What is the typical AI app like?

Forgettable. The median AI app has just 10 ratings, 87% have fewer than 1,000, and only 8 of the 8,505 have more than a million. Most were never found by anyone — ignored, not necessarily bad.

Is the AI app boom slowing down?

No — it is accelerating. By launch year, 1,434 of these apps shipped in 2023, 1,798 in 2024, and 2,890 in 2025; 2026 is already past 1,400 at the half-year mark. 89% launched in 2023 or later.

Who makes all these AI apps?

About 6,600 developers, 83% of them shipping a single app. But a few app farms have industrialized the gold rush — one studio (ZipoApps) ships 26 distinct AI apps, several others 10 or more.

How was this measured?

By paginating about 100 AI-intent search terms through a structured App Store API (US storefront, June 2026), deduping to 10,958 unique apps, then strict-tagging 8,505 as genuinely AI. Rating count is a proxy for scale, and the figures are a lower bound on the true long tail.

Originally published on crawlora.net. Crawlora is a structured web-data, search, and anti-bot API — dozens of platforms as normalized JSON, plus a hosted MCP server, with a free tier (no card).

Originally published at https://dev.to on July 27, 2026.

• The gold rush is real: of ~1,490 discoverable ‘AI’ apps on the App Store, 84% launched in 2023 or later — more in 2023 alone (488) than in every prior year combined (236).
• But the takeover skipped the top chart. Only 6–8% of the Top 100 free apps are AI — the same handful (ChatGPT, Claude, Gemini, Meta AI) on both Apple and Google. The flood is in the catalog, not the rankings.
• It’s a free-install, subscription business: AI is 0% of the top paid chart, yet ChatGPT is the #1 top-grossing app on the iPhone.
• Five apps, half the ratings. The distribution is brutally unequal (Gini 0.95): ChatGPT alone holds 22% of all AI-app ratings and the top 5 hold 45%. The median AI app has just 171 ratings; two-thirds have under 1,000.
• Built by an army of one-app developers: ~1,300 developers for ~1,490 apps, 90% shipping a single app, a quarter carrying template ‘AI…/Chat…’ names. 30 of the top 50 results for ‘chatgpt’ aren’t even made by OpenAI.
• Honest caveat: most of these apps aren’t bad — they’re ignored. The long tail is unrated, not one-star, and we make no quality claim from store ratings.

Everyone agrees AI ate the App Store. The interesting question is where it took a bite — and the answer is not the one the headlines imply. So instead of vibes, we pulled the listings: about 1,490 distinct “AI” apps from Apple’s App Store, the full top charts across nineteen categories, Google Play’s install counts, and a sample of reviews — all in June 2026.

How many AI apps are on the App Store? About 1,490 are unambiguously AI as of June 2026 — out of roughly 2.42 million apps total — and that is just the discoverable head, so the real figure is higher. Don’t confuse it with the web AI-tool directories that claim anywhere from 1,200 to 90,000+: those count websites and browser tools, not installable App Store apps, which is a different and much fuzzier question. For scale, AI apps booked about $18.5 billion in revenue in 2025 with over 1.1 billion users — this study is about which of those ~1,490 actually broke through.

A method note, with the usual irony: we read the stores through a structured web-data API. Both Apple and Google rate-limit and fingerprint plain scrapers, and Google Play hides install counts behind rendered pages — which is exactly why a maintained, structured API beats hand-scraping. (The short version is in the box below; full caveats are at the end.)

Methodology at a glance

• Storefront & window: US App Store + Google Play, a single June 2026 snapshot.
• Corpus: ~1,490 distinct “AI” apps, paginated from about a dozen AI seed searches — the discoverable head of the market, not a census, so every concentration figure is a lower bound.
• What counts as “AI”: a strong signal only — an explicit “AI”/”GPT”/assistant marker or a named AI brand — not the bare word “chat” (which over-counts messaging and dating apps).
• Rating counts are a proxy for scale, not exact installs (Apple doesn’t publish installs).
• Google Play install counts are Google’s own bucketed figures, inflated by pre-installed apps like Gemini and Copilot.
• Review quotes are a hand-read sample for illustration, not a sentiment census.

Full caveats are at the end. Every figure came back as structured JSON — see a sample App Store or Google Play response.

What this means

The story isn’t “AI is winning the App Store” — it’s that AI flooded the catalog while the charts barely moved. Supply exploded; attention didn’t. What that implies depends on who’s reading:

• App builders: a generic chatbot wrapper now joins ~1,490 look-alikes where the median app has just 171 ratings — a crowded, low-traction bet. The opening is category-specific workflows in the verticals AI has barely touched (Finance, Shopping, Navigation and Social were all 0–2% of their top charts).
• Investors & market researchers: read catalog counts as noise and traction as signal. The boom is supply, not demand — five apps hold ~45% of all AI-app ratings and ChatGPT alone holds 22%. Judge AI apps on ratings, installs and grossing rank, not on how many shipped.
• ASO teams: the top of the chart is locked by four incumbents, but the long tail of ‘chatgpt’-intent searches still moves — 30 of the top 50 results for that query aren’t OpenAI. Track rank, ratings and review sentiment continuously, because the wrapper tier turns over fast.
• Data & API buyers: the durable signal is rankings, install counts and reviews pulled as structured JSON and re-pulled on a schedule — a one-time snapshot like this one decays as the charts shift.

The gold rush is real

Start with supply. Tag every app whose name or maker signals AI, bucket by original release date, and the category simply materializes after ChatGPT:

84% of the AI apps we found launched in 2023 or later — more in 2023 alone than in the App Store’s entire prior history of AI. That tracks the wider market: Appfigures put 2026 app releases up 60% year-over-year across both stores (and +80% on iOS alone), and Sensor Tower clocked AI-app downloads up 148% in 2025. AI didn’t just grow; it became the default thing to ship.

But it skipped the top of the chart

Here’s the twist. All that supply barely dented the rankings. On any given day, only a handful of the Top 100 free apps are AI-native — and it’s the same handful on both stores: ChatGPT, Claude, Gemini, and Meta AI. The rest of the chart is the incumbents you’d have seen in 2019: streaming, banking, shopping, social.

What’s distinctive is how AI monetizes. It’s almost never a paid download — it’s a free install wrapped around a subscription:

And the takeover is wildly uneven by category. AI saturated creative tooling and assistants, and barely touched anything transactional:

Four apps ate the AI

Now the part that reframes everything. Across both stores, just four AI-native apps crack the Top 100 free chart — ChatGPT, Claude, Google Gemini, and Meta AI — and the ratings show how empty the space behind them is. Rank all 1,490 apps by rating count and the curve is almost vertical: ChatGPT alone holds 22% of every AI-app rating we counted; the five most-rated — ChatGPT, Canva, Google Gemini, Gauth, and Microsoft 365 Copilot — hold 45%; the top hundred hold 91%. (The two lists barely overlap — Claude and Meta AI top today’s charts but trail on lifetime ratings, where big installed bases like Canva, Gauth and Copilot pile up; a rating count rewards reach and age as much as hype.)

The flip side of that curve is a vast, silent tail. The median AI app has just 171 ratings. Two-thirds have fewer than a thousand. Seven have more than a million. ChatGPT, with nearly eight million, has roughly forty-six thousand times the median app:

That’s the whole shape of the gold rush: a few prospectors struck a seam, and everyone else is panning an empty creek.

An AI app for every feeling

What are the other 1,480 apps? Mostly the same few ideas, copied. Cluster the corpus by what each app does and it’s a narrow menu — assistants, photo generators, and an enormous pile of AI companions:

An app farm, not an ecosystem

The 1,490 apps come from about 1,300 developers — and 90% of them ship exactly one app. Nearly a quarter of all titles match a template name (they start with “AI “ or are some permutation of “Chat / Bot / Assistant / GPT”). This isn’t a thousand teams solving a thousand problems; it’s a thousand wrappers around the same few APIs, racing to rank for the same keywords.

You can see the keyword race most clearly around one word: ChatGPT. Apple polices literal name-spoofing — the fake “ChatGPT 5.0 Official” apps that surface in search autocomplete don’t survive into the ranked results. So the clones adapted: instead of copying the name, they copy the intent. Of the top 50 results for the query “chatgpt,” 30 are not made by OpenAI — a combined 3.7 million ratings, all free, fronted by anonymous LLCs and overseas studios wrapping the OpenAI API behind a paywall (the busiest of them is the very same one-developer “app farm” that tops our corpus).

And the reviews are where the wrapper model shows its seams. These are recent reviews — the apps’ lifetime ratings stay high — but the same complaint recurs across the clone tier: a surprise subscription you didn’t mean to buy.

“I accidentally paid for this app instead of the ACTUAL Chat GTP that I can actually CHAT with…” — one-star review of a ChatGPT-wrapper app

“I was charged around $200 AUD for this app despite barely using it… I didn’t even recognise the name when the charges appeared and had to Google it to work out what it was.” — one-star review of the same app

“They initially said it is free of charge and the credit card is used only for age verification; once the card details are given, immediately they charge USD 39.99.” — one-star review of a ‘ChatGPT’ clone

To be clear, this is the pattern at the wrapper tier, not a verdict on AI apps generally — plenty of users love the real thing (“ChatGPT is truly the best — I cannot imagine how I lived life without it”). But it’s the predictable economics of a market where a thousand near-identical apps compete for installs and monetize on the way in.

Android tells a slightly different story

One more cut, because Google Play exposes something Apple won’t: real install counts. Run the same analysis there and the concentration is just as steep — but the king is different. On Android, Gemini, not ChatGPT, leads, and four apps clear a billion installs:

It’s a neat illustration of home-field advantage — Google ships Gemini with Android, the way Apple would if it had a comparable app — and a reminder that “most popular AI app” depends entirely on which store you measure.

How we did this (and the caveats)

We pulled App Store and Google Play listings, charts, and reviews through a structured API in June 2026, on the US storefront. The AI corpus is ~1,490 unique apps gathered by paginating searches across a dozen AI seed terms; the chart and category figures come from the live top-100 lists. Caveats worth stating plainly: “AI app” is a tagging judgment — we required a strong signal (an explicit “AI”/”GPT”/assistant marker or a named AI brand) rather than the bare word “chat,” which over-counts messaging and dating apps. The corpus is the discoverable head, not a census — store search has a relevance floor, so the true long tail is even longer and the real inequality even higher than the Gini of 0.95 we measured. Release dates are the original bundle’s first release, so apps that added AI later (Canva, Picsart, Snapchat) read older than their AI debut; the pure-play 2023+ chatbots are the clean “AI-native” cohort. Rating count is a proxy for scale, not exact installs (which Apple doesn’t expose and Google reports in buckets). And we make no claim that low-rating apps are bad — most simply have no traction, which is a different thing from a bad review.

If you’re wondering how we read charts and reviews that block plain scrapers: that’s the day job. Crawlora is a web-data API for AI agents and pipelines that returns normalized JSON for the App Store, Google Play, search engines, marketplaces, and social platforms — handling proxies, rendering, and anti-bot — and bills pay-on-success. The same approach powers our Product Hunt trends study, our streaming fragmentation study, our podcast gold rush study, and our anti-bot adoption index — and the full app catalog behind this study, 4.1M apps across the App Store and Google Play, is browsable in our mobile app dataset — with the daily App Store + Google Play charts and app-review sentiment as their own queryable datasets.

Sources

Frequently asked questions

How many AI apps are on the App Store?

We counted about 1,490 unambiguously-AI apps on the US App Store in June 2026 — out of roughly 2.42 million apps in total — and that is only the discoverable head, so the true figure is higher. (Don’t confuse this with web ‘AI tool’ directories that cite anywhere from 1,200 to 90,000+; those count websites and browser tools, not App Store apps.) It’s a long tail: 84% launched in 2023 or later, the median app has just 171 ratings, and two-thirds have under 1,000.

What is ChatGPT’s market share among AI apps?

Among App Store AI apps, ChatGPT holds about 22% of all ratings and is the #1 top-grossing iPhone app. It is not unrivalled: Gemini leads on Android installs (helped by OS bundling), and across the wider AI-assistant market ChatGPT’s usage share recently slipped below 50% as Gemini, DeepSeek, Claude, Grok and Perplexity grew. Either way the concentration is extreme — the top five apps hold about 45% of all AI-app ratings.

How many AI apps are there like ChatGPT?

Many, and most are wrappers. Of the top 50 App Store results for ‘chatgpt,’ 30 are not made by OpenAI (a combined 3.7 million ratings), and about a quarter of all AI apps carry template ‘AI…/Chat…’ names. But attention is concentrated: ChatGPT alone holds about 22% of all AI-app ratings, and the next assistants — Gemini, Claude, Perplexity, Grok, DeepSeek — trail far behind on the App Store.

What is the most popular AI app?

ChatGPT, by a wide margin. On iOS it has nearly 8 million ratings — about 22% of all AI-app ratings we measured — and it is the #1 top-grossing iPhone app. On Android, Google Gemini leads on installs (~1.9 billion), helped by being bundled with the OS.

Is the App Store full of AI apps now?

In supply, yes; on the charts, no. Only about 6–8% of the Top 100 free apps are AI, and it’s the same handful (ChatGPT, Claude, Gemini, Meta AI) on both Apple and Google. The flood is in what gets listed, not what ranks.

Which app categories did AI take over?

Mostly creative tooling and assistants. AI is about 28% of the Photo & Video top chart and 27% of Productivity, with smaller footholds in Reference (translators) and Music. It is essentially absent — 0% — from Weather, Shopping, Navigation, Games and Food & Drink.

Are most AI apps any good?

Most simply get no traction rather than bad reviews — the median has only 171 ratings. About a quarter are template ‘AI…/Chat…’ wrappers, 90% come from single-app developers, and 30 of the top 50 results for ‘chatgpt’ aren’t even made by OpenAI. We make no quality claim from store ratings, which stay high for the apps people actually use.

How was this measured?

By pulling App Store and Google Play listings, top charts, and reviews through Crawlora’s structured web-data API (US storefront, June 2026) and aggregating them. The stores rate-limit plain scrapers and Google Play hides install counts, so a structured API is how you read them at scale. Figures are a point-in-time snapshot and a lower bound on the true market.

Originally published on crawlora.net. Crawlora is a structured web-data, search, and anti-bot API — dozens of platforms as normalized JSON, plus a hosted MCP server, with a free tier (no card).

Originally published at https://dev.to on July 27, 2026.

• 7,850,222 distinct Airbnb listings across 60+ countries — deduplicated to one record per listing, this is roughly 87–98% of Airbnb’s own stated 8–9M global active inventory, built without touching Airbnb’s investor deck.
• The United States (1.21M listings) and France (922K) alone account for over a quarter of the entire measured census — France, a country a sixteenth the US’s population, runs nearly as many listings as the US does.
• Airbnb ratings barely function as a filter: 4.81★ average across 4.97M rated listings, and 89.5% of every rated listing sits at 4.5★ or above. A 4.2★ listing isn’t mediocre on this platform — it’s in the bottom decile.
• Nightly prices vary 5–6× across markets that carry a comparable USD price tag: $250 median in the Netherlands and $231 in the US, versus $43 in India and $35 in Kenya — for the ~18% of listings with a captured, USD-denominated rate.
• Superhost counts are a lower bound by design (status is only recorded when a listing is re-scanned), so we don’t headline a rate — see the method note for why we show raw counts instead.
• Everything below is queryable over Crawlora’s Airbnb Markets API — aggregate market rollups only, never individual listings, hosts, photos or review text.

Every “Airbnb statistics 2026” post on the internet cites the same handful of numbers — Airbnb’s own investor letter, host counts, gross booking value. None of it says where the actual supply sits, market by market, or what a listing in Croatia costs against one in Norway. So we built that map ourselves: a logged-out, map-grid census of 7,850,222 distinct Airbnb listings across 60+ countries, deduplicated to one record per listing.

Method at a glance

• Discovery: logged-out map-grid search of public Airbnb listings, reverse-geocoded to a country, deduplicated to one record per distinct listing ID.
• Coverage: 7,850,222 distinct listings observed, of which 7,745,272 resolve to a country. Airbnb states roughly 8–9M active listings worldwide, so this census sits at 87–98% of Airbnb’s own claimed global inventory — a near-complete public-supply count, not a sample.
• Ratings: computed only over the 4,968,367 listings (63% of the geo-resolved total) that carry a rating; an unrated listing is excluded, never counted as a zero.
• Prices: the median nightly rate for the ~18.7% of listings with a captured, USD-denominated price. Listings without a captured currency are excluded rather than mixed in.
• Superhost: captured only when a listing is scanned, so the raw count is a floor, not a rate — we don’t headline a percentage (see the caveats).
• Aggregate-only, published under /datasets/airbnb: every number below is a market rollup by country, metro or geo cell. No individual listings, hosts, photos or review text ever leave the aggregate.
• Snapshot dated July 3, 2026.

Where the world’s Airbnb supply actually sits

Two countries — the United States and France — account for more than a quarter of every listing in the census. France in particular is a striking outlier: a country of 68 million people runs 922,055 listings, not far behind the US’s 1.21 million from a population of 340 million. Airbnb density, it turns out, tracks tourism intensity and regulatory permissiveness a lot more than population.

Croatia is the census’s biggest surprise: a country of under 4 million people runs 156,789 listings — more than Canada, Australia or Japan. Coastal Adriatic tourism and a light regulatory hand turned a small country into the sixth-largest single market for short-term rental supply on earth.

Show the full 60-country table (listings, rating, median price)

Airbnb listing census by country, July 2026: distinct listings, average rating, and median nightly price (USD-priced listings only) All 60 countries in the census, ranked by listing count. Rating and price columns are computed over the subset of listings in that country carrying a rating / USD price respectively. Source: Crawlora Airbnb Markets dataset, July 3, 2026 snapshot.

• United States: 1,212,294 listings, 4.85★, $231 median nightly
• France: 922,055 listings, 4.81★, $123
• Brazil: 548,223 listings, 4.86★, $83
• Italy: 459,280 listings, 4.79★, $166
• United Kingdom: 351,401 listings, 4.81★, $176
• Spain: 311,308 listings, 4.73★, $162
• Germany: 244,305 listings, 4.80★, $141
• India: 206,714 listings, 4.71★, $43
• Mexico: 198,321 listings, 4.79★, $95
• Croatia: 156,789 listings, 4.83★, $189
• Greece: 155,794 listings, 4.84★, $173
• Canada: 146,890 listings, 4.84★, $153
• Australia: 144,967 listings, 4.80★, $213
• Indonesia: 124,883 listings, 4.77★, $56
• Colombia: 116,025 listings, 4.78★, $91
• Price extremes — Netherlands: 49,336 listings, 4.76★, $250 (highest median); Israel: 13,865 listings, 4.81★, $283 (highest overall); Kenya: 35,045 listings, 4.76★, $35 (lowest)
• Full 60-country table (all countries, ranked by listings): crawlora.net/blog/airbnb-listings-by-country-2026

Ratings barely discriminate anymore

If you filter Airbnb by “4.5 stars and up,” you’re filtering out almost nothing. Across the 4,968,367 rated listings in the census, the distribution is packed into the top band:

Two-thirds of rated listings sit at 4.8★ or higher, and 89.5% clear 4.5★. Fewer than one in a hundred rated listings falls under 4.0★. This is the same grade-inflation pattern that shows up in Uber driver ratings and eBay seller feedback: a two-sided reputation system where hosts can decline problem guests and guests self-select toward listings that already look safe, compressing the whole distribution toward the ceiling. On a platform like this, a 4.3★ listing isn’t “pretty good” — it’s in roughly the bottom 8% of the rated population.

Nightly prices span a 5–6× range across otherwise-comparable markets

Only about 18.7% of listings in the census carry a captured, USD-denominated nightly price — almost all listings price in local currency, and we exclude those rather than force a currency conversion that would introduce its own error. Within that USD-priced subset, though, the country-to-country spread is stark:

The US ($231 median) and France ($123 median) — the two biggest markets by supply — sit at opposite ends of the price spectrum, not because Airbnb’s product differs but because average nightly rates track each country’s cost of living and average trip type (short city breaks in France’s dense supply vs. longer-stay vacation rentals common in the US). Across the full country table above, the range runs from Israel’s $283 median down to Kenya’s $35 — an 8× spread that a single “average Airbnb price” headline number completely erases.

Why we don’t headline a Superhost rate

Superhost status is captured at the moment a listing is scanned, which means the aggregate count in this census — 195,000+ observed Superhost listings across all 60 countries — is a floor, not a rate. A listing that earned Superhost status after our last visit to its page is counted as non-Superhost until it’s re-scanned. Reporting superhost_observed ÷ total_listings as "the Superhost rate" would systematically understate reality, so the dataset API exposes the raw observed count and lets you judge coverage for your own use case rather than us publishing a number we know is biased low.

What this means if you build on Airbnb data

For property managers and revenue-management tools, the takeaway is that “average nightly rate” is close to meaningless without a country (or better, metro) anchor — the 8× country spread dwarfs any seasonal or day-of-week effect you’d otherwise be modeling. For market-entry and investment research, supply density (Croatia’s outlier position, France’s tourism-driven scale) is a far better leading indicator than population or GDP. And for competitive intelligence on the short-term-rental sector generally, a rating-based quality filter is close to useless above 4.0★ — you need occupancy, review-volume, or price-tier signals instead, since almost every active listing already clears the rating bar.

For a US-specific cut of this same dataset, see where Airbnb listings actually outnumber homes for sale — we joined this Airbnb census against Crawlora’s Housing Markets dataset by metro.

Sources

Frequently asked questions

How many Airbnb listings are there in 2026?

Crawlora’s July 2026 census counted 7,850,222 distinct Airbnb listings across 60+ countries, deduplicated to one record per listing. That’s roughly 87–98% of Airbnb’s own stated 8–9M global active inventory — a near-complete public-supply count, built independently of Airbnb’s investor disclosures.

Which countries have the most Airbnb listings?

The United States leads with 1,212,294 listings, followed by France (922,055), Brazil (548,223), Italy (459,280), and the United Kingdom (351,401). France stands out for its density: a country a fraction of the US’s population runs nearly as many listings.

What is the average Airbnb rating?

4.81 stars across the 4,968,367 rated listings in the census (63% of the geo-resolved total). Ratings barely discriminate: 89.5% of every rated listing sits at 4.5 stars or above, and two-thirds clear 4.8 stars — a listing under 4.0 stars is in roughly the bottom 1% of the platform.

How much does an Airbnb cost by country?

Among the ~18.7% of listings with a captured, USD-denominated nightly price, medians range from $283 in Israel and $250 in the Netherlands down to $43 in India and $35 in Kenya — roughly an 8x spread. The two largest markets by supply, the US ($231) and France ($123), sit far apart, reflecting cost of living and trip-type differences rather than platform differences.

Is this individual listing data or aggregates?

Aggregate-only. Every figure is a market rollup by country, metro, or geographic cell — listing counts, ratings, price bands and Superhost counts. No individual listings, host names, photos or review text are ever published; thin cells are suppressed.

Originally published on crawlora.net. Crawlora is a structured web-data, search, and anti-bot API — dozens of platforms as normalized JSON, plus a hosted MCP server, with a free tier (no card).

Originally published at https://dev.to on July 27, 2026.

• X’s displayed App Store rating has been 4.6 stars the entire time — 4.6 at 6.6M ratings the week Musk’s deal closed (October 2022), 4.6 at 8.6M by mid-2024, 4.6 at 11M today. The headline number is the least informative thing about it.
• Inside the written reviews, the picture is very different. Across 10,861 English App Store reviews, the mean star among people who actually wrote something fell from 3.81 before the acquisition to 2.95 in the X era, and the share of one-star written reviews jumped from 17.8% to 43.6%.
• The topics changed even more than the scores. Mentions of Elon Musk went from 0.8% of reviews before the deal to 16.4% after — the single biggest shift. Subscription/pricing complaints tripled (3.2% to 9.2%) after Twitter Blue launched, and a Grok/AI theme that didn’t exist before now appears in 4.6%.
• But one narrative the data does NOT support: censorship and bias complaints did not rise. They were 7.7% of reviews before Musk and 6.6% after — essentially flat. Political-moderation grievances predate the acquisition; the oldest review in our set complaining about a politically motivated lockout is from 2017.
• This measures what vocal reviewers write, not what all users think. Written reviews are self-selected toward strong opinions, and they got more polarized in both directions — free-speech praise rose (3.2% to 7.4%) at the same time one-star reviews doubled. We report the shift; we take no position on whether X got better or worse.

When Elon Musk closed his $44 billion purchase of Twitter on October 27, 2022, it was one of the most scrutinized corporate takeovers in tech history. Nearly four years later, the app’s App Store rating sits at 4.6 stars — exactly where it was the week the deal closed. That stability is itself the surprise, and it’s misleading. We pulled 92,088 reviews of the X app from the iOS App Store and Google Play, reaching back to 2009, and theme-coded them around the acquisition date. The rating didn’t move. Almost everything underneath it did.

To be clear up front about what this is and isn’t: this is a measurement of what people write in app reviews, before and after a date. It is not a referendum on whether X is a better or worse product now, and we don’t offer one. Reviews are written by a self-selected, opinionated minority. What they’re useful for is showing how the composition of that opinion changed — which topics rose, which fell, and which, contrary to the popular story, didn’t move at all.

The number that didn’t move

Three point-in-time snapshots of the App Store listing — two from the Internet Archive, one live today — tell the same story:

• Oct 28, 2022 (deal closed): 4.6★, 6.6M ratings — Twitter, Inc.
• May 30, 2024 (post-rebrand): 4.6★, 8.6M ratings — X Corp.
• Jul 16, 2026 (today): 4.6★, 11M ratings — X Corp.

The aggregate is an all-time average weighted by millions of silent tap-a-star ratings, so it moves at a glacial pace and heavily favors years of accumulated goodwill. It is designed not to move. That’s exactly why it hides what changed.

What changed: written-review sentiment fell off a cliff in 2023

When you look only at reviews where someone wrote text — a much smaller, much louder population — the mean star rating tracks a clear break at the acquisition. Across 10,861 English-language App Store reviews:

The share of one-star written reviews more than doubled across the acquisition:

Again, the caveat that keeps this honest: these are people motivated enough to write, and the App Store’s “most helpful” sort surfaces heavily-upvoted reviews, which skew critical. A doubling of one-star written reviews is a real shift in the vocal population — it is not a claim that 44% of X’s users are unhappy. The silent majority is what keeps the aggregate at 4.6.

What changed most: the reviews became about Elon Musk

We theme-coded every English review with a keyword classifier across fourteen categories. The single largest shift, by a wide margin, is that reviews started being about the owner:

Before the deal, 0.8% of reviews mentioned Elon Musk. After, 16.4% did — one in six. A social app’s reviews are usually about the app; X’s became partly about its owner. The other risers map cleanly onto real product changes: subscription/pricing complaints tripled (3.2% → 9.2%) after Twitter Blue, later X Premium, launched the same week the deal closed; a Grok/AI theme that was essentially absent (0.1%) now appears in 4.6% after xAI’s assistant was folded into the app; and free-speech praise more than doubled (3.2% → 7.4%), the clearest signal of the supportive camp.

The full before/after table, including the themes that fell:

• Mentions Elon Musk: 0.8% → 16.4% (+15.6)
• Subscription / pricing: 3.2% → 9.2% (+6.0)
• Grok / AI: 0.1% → 4.6% (+4.4)
• Free-speech praise: 3.2% → 7.4% (+4.3)
• Bots / spam: 2.3% → 6.4% (+4.1)
• Trolls / hate speech: 6.7% → 8.0% (+1.4)
• Censorship / bias complaint: 7.7% → 6.6% (−1.1)
• Account lock / ban: 9.2% → 8.2% (−0.9)
• Algorithm / feed: 9.9% → 8.3% (−1.6)
• Bugs / reliability: 10.5% → 8.7% (−1.9)
• Quality praise: 12.0% → 7.1% (−4.9)

The narrative the data doesn’t support

The most politically loaded claim about post-Musk Twitter — that it became dramatically more or less censored — is not what the review topics show. Censorship and bias complaints were 7.7% of reviews before the acquisition and 6.6% after: essentially flat, and slightly down. Complaints about moderation, shadow-banning, and political bias were already one of the most common review themes under the old ownership. The oldest example in our set is titled “Locked out of my account for political reasons” — from November 2017, five years before Musk.

This cuts against a tidy story in both directions. Reviewers who expected the data to show censorship complaints vanishing after a “free speech” takeover, and reviewers who expected them to spike, will both be disappointed: the theme barely moved. What moved was everything around it — the owner’s name, the subscription, the AI, and the overall sentiment.

Meanwhile, quality praise fell the most of any theme (12.0% → 7.1%). Fewer people writing reviews are there to say they love the app.

What reviewers actually wrote, both sides

The reviews got more polarized, not uniformly negative. Verbatim, kept short and shown from both camps and both eras:

Before the acquisition:

“I’m grateful for Twitter” — 5★, 2021
“Locked out of my account for political reasons” — 1★, 2017

After, supportive:

“I’m so grateful to Elon Musk.” — 5★, 2023
“In X we trust” — 5★, 2023

After, critical:

“Bring back Twitter” — 1★, 2024
“Elon’s Pathetic Echo Chamber” — 1★, 2025

We’re quoting these to show the polarization, not to endorse either side. Both grew louder at once.

Cross-platform: where the comparison holds and where it doesn’t

• iOS App Store (93 storefronts): 49,723 reviews, 2009→2026 — the before/after spine
• Google Play (deep pagination): 42,365 reviews, mostly 2026 with a thin tail to 2018 — current-state cross-check
• Chrome Web Store: 0 official — no first-party X extension exists

The before/after comparison rides entirely on the App Store, because its per-country feeds reach back years. Google Play’s public feed is overwhelmingly recent — X collects roughly 39,700 Play reviews in 2026 alone, so paginating back to 2022 at scale isn’t feasible; we use it only to confirm the current-state picture. And unlike some apps we’ve studied, X ships no official browser extension — we confirmed this by searching the Chrome Web Store for X Corp. and Twitter, Inc. as developers (zero results). The third-party ecosystem around X (Control Panel for Twitter, ~300K users, and others) is real but not built by the company, so it isn’t comparable.

What holds up, and what doesn’t

What holds up: the displayed rating is stable while written-review sentiment dropped sharply and topic composition shifted, both breaking cleanly at the 2022–2023 boundary. The Elon-mentions, subscription, and Grok shifts map onto documented product changes. The censorship-complaint theme, contrary to the loudest narratives, did not rise.

What limits it:

• Written reviews are not users. They are a self-selected, opinion-heavy minority, and “most helpful” sorting over-samples highly-upvoted (often critical) reviews. Read the shifts in what this vocal group writes, not the absolute levels as a poll.
• Theme coding is keyword-based, not semantic. It undercounts anyone expressing a theme without its keywords, and it counts a mention, not a verified fact. “Reviewers mention X more” is not “X is more true.”
• The corpus spans ~78 storefronts and many languages. Star ratings are language-independent (used for the whole trajectory), but theme coding runs only on the 10,861 English-majority-storefront reviews, a smaller sample.
• Google Play can’t corroborate the pre-2022 period at scale, so the before/after rests on the App Store alone.
• Snapshot rating figures come from three archived/live points, not a continuous series; the aggregate’s stability is directional.

Sources

Sources

Methodology

Every number here was pulled through Crawlora’s own APIs on July 16, 2026: App Store reviews fanned across 93 country storefronts at full feed depth (Apple caps its public feed at 10 pages × 2 sort orders per storefront, but each storefront is an independent feed reaching back years), and Google Play reviews via continuation-token deep pagination. Point-in-time rating snapshots were read from the Internet Archive and the live listing. The Chrome Web Store check used the chrome-extensions dataset by developer name.

Corpus: 92,088 reviews — 49,723 App Store (78 storefronts with data, 2009–2026) and 42,365 Google Play. Theme coding used a documented keyword classifier across fourteen categories on the 10,861 English-majority-storefront App Store reviews (2,735 before the acquisition, 8,126 after); a review may carry more than one theme. We publish aggregates only — no raw review text tied to individual reviewers. The full aggregate dataset — theme before/after, star-by-year, and rating snapshots — is open on GitHub under CC BY 4.0.

Reproduce it in the Playground; the how to scrape App Store reviews and how to scrape Google Play guides walk through the calls, and our app review sentiment dataset has millions of pre-scraped reviews. For the same review-mining method applied to the Claude app, see what 1,610 reviews of the Claude app reveal.

Frequently asked questions

Did X’s app rating drop after Elon Musk bought Twitter?

The displayed App Store rating did not — it has been 4.6 stars throughout, at 6.6M ratings when the deal closed in October 2022, 8.6M by mid-2024, and 11M today. That aggregate is an all-time average weighted by millions of silent ratings, so it barely moves. But among the smaller population who write text reviews, sentiment fell sharply: across 10,861 English App Store reviews, the mean written-review star dropped from 3.81 before the acquisition to 2.95 in the X era, and the one-star share of written reviews rose from 17.8% to 43.6%.

What do people complain about in X app reviews after the acquisition?

The topics shifted more than the scores. Mentions of Elon Musk went from 0.8% of English reviews before the deal to 16.4% after — the single largest change. Subscription and pricing complaints tripled (3.2% to 9.2%) after Twitter Blue / X Premium launched, a Grok/AI theme rose from near-zero to 4.6%, and free-speech praise more than doubled (3.2% to 7.4%). Quality praise fell the most, from 12.0% to 7.1%.

Did censorship complaints about X increase after Musk took over?

No — this is the narrative the data does not support. Censorship and bias complaints were 7.7% of English App Store reviews before the acquisition and 6.6% after, essentially flat and slightly down. Complaints about moderation and political bias were already common under the old ownership; the oldest example in our corpus, a review about a politically motivated account lockout, is from 2017, five years before Musk.

How many X reviews were analyzed, and how?

92,088 reviews — 49,723 from the iOS App Store (fanned across 93 country storefronts, reaching back to 2009) and 42,365 from Google Play — pulled via Crawlora’s APIs on July 16, 2026. Star-rating trends use the full multi-language corpus (stars are language-independent); theme coding uses a documented keyword classifier on the 10,861 English-majority-storefront App Store reviews (2,735 before the acquisition, 8,126 after). We publish aggregates only, no raw review text tied to reviewers.

Does this study say whether X got better or worse under Musk?

No. It measures what a self-selected, opinionated minority writes in app reviews, before and after a date — not overall product quality or user satisfaction. Written reviews skew toward strong opinions and got more polarized in both directions after 2022, with free-speech praise and one-star reviews rising at the same time. We report the composition shift and take no position on whether the app improved.

Originally published on crawlora.net. Crawlora is a structured web-data, search, and anti-bot API — dozens of platforms as normalized JSON, plus a hosted MCP server, with a free tier (no card).

Originally published at https://dev.to on July 27, 2026.

• Across two completely independent corpora — 427 Reddit comments and 806 first-party browser-extension reviews — code quality was the least-discussed theme both times: 5 mentions out of 427, and 3 out of 806. Developers are arguing about cost, quotas, logins and reliability, not about which agent writes better code.
• Anthropic’s Claude extension has the largest installed base of any first-party AI browser extension we found (11 million users) and the worst rating (2.71, with 53% one-star reviews). OpenAI’s two extensions have 6 million users between them and rate 3.33 and 3.71. Bigger install base, angrier reviews — in both directions.
• On July 9, 2026, OpenAI turned its ChatGPT Chrome extension into Codex for Chrome, replacing the Atlas sidebar. Its reviewers noticed: 52 of 166 recent reviews mention Codex, many furious about a product they did not choose. That is what a bundled 8M active-user number looks like from underneath.
• On GitHub — the one metric both tools expose in directly comparable form — Claude Code leads, not Codex: 137,890 stars to 98,105, and 22,235 forks to 14,628. That is the opposite of the headline, though the two repositories hold different things, and we explain why that matters below.
• None of this crowns a winner, and that is the finding. Vendor user counts are unfalsifiable; the independent corpora describe a question that practitioners have largely stopped asking.

On July 14, 2026, OpenAI’s Thibault Sottiaux posted that Codex had reached 8 million active users. It was the third such number in three days — 6M on July 12, 7M on July 13, 8M on July 14 — and, like the two before it, it was explicitly a combined figure for two products:

“Hello. We have reached 8M active users across Codex and ChatGPT Work.”
-@thsottiaux, July 14, 2026

We fact-checked that number’s framing yesterday: it bundles Codex with ChatGPT Work, and the “Claude Code has 2M” baseline it gets compared against is a stale, undated snapshot. That post answered whether the number means what the headlines said. It didn’t answer the question underneath: is Codex actually beating Claude Code?

So we tried to answer that one — not from press releases, but from corpora neither company controls. Vendor user counts are unfalsifiable by construction: you cannot audit them, reproduce them, or check their denominator. Posts, comments and store reviews are independent, timestamped, and minable. Here is what they say.

Which surfaces can actually answer this — measured, not assumed

Codex and Claude Code are command-line tools, so the instinct is to assume no store data exists. That instinct is half wrong, and the half that’s wrong is the interesting half.

There genuinely is no Codex app and no Claude Code app on either mobile store. But both vendors ship a lot of other software, and rather than assume their store listings are irrelevant, we measured the coding-agent signal in each corpus directly:

Surface by surface — first-party presence, coding-agent signal, and whether it’s usable:

• iOS App Store — ChatGPT (8,592,629 ratings), Claude (199,486): 1 of 100 recent reviews mentions Codex or Claude Code. Usable? No — signal ~1%.
• Google Play — ChatGPT, Claude: same two chat products. Usable? No.
• Chrome Web Store — Claude (11M users), ChatGPT search (4M), ChatGPT (2M), all first-party: 52 of 166 ChatGPT-extension reviews mention Codex (31%). Usable? Yes.
• GitHub — anthropics/claude-code, openai/codex: both canonical repos. Usable? Yes.
• Reddit — six active subreddits: direct discussion. Usable? Yes.

iOS App Store

ChatGPT (8,592,629 ratings), Claude (199,486)

1 of 100 recent reviews mentions Codex or Claude Code

No — signal ~1%

Chrome Web Store

Claude (11M users), ChatGPT search (4M), ChatGPT (2M) — all first-party

52 of 166 ChatGPT-extension reviews mention Codex (31%)

Yes

The mobile apps are the category error everyone makes. The ChatGPT iOS app has 8,592,629 ratings and the Claude app has 199,486; that 43:1 ratio gets cited as evidence in this debate. We pulled 100 recent reviews across both and exactly one mentioned a coding agent. Those ratings measure consumer chat — and now we can say so with a number rather than an assumption.

The Chrome Web Store is the surprise, and it turned out to be where the real story was.

OpenAI turned its ChatGPT extension into Codex — and its users noticed

Both vendors run official Chrome extensions, and they are not small:

• Claude (Anthropic): 11,000,000 users, 1,306 ratings, score 2.71, 53% one-star, 24% five-star
• Chat GPT search (OpenAI): 4,000,000 users, 1,558 ratings, score 3.33, 40% one-star, 26% five-star
• ChatGPT (OpenAI): 2,000,000 users, 333 ratings, score 3.71, 28% one-star, 56% five-star

That last row is the one that matters. OpenAI’s ChatGPT extension was updated on July 9, 2026 — the same day GPT-5.6 Sol shipped — and it is no longer really a ChatGPT extension. It is Codex for Chrome, and it replaced the Atlas sidebar its users had installed it for. They noticed immediately:

“this is not ChatGPT for Chrome, this is Codex for Chrome”

“You are retiring Atlas and you are replacing it with this”

Of its 166 most recent reviews, 52 mention Codex — 31% — which makes this the only first-party store corpus with meaningful Codex-specific feedback in it. Its rating profile is sharply bimodal: 56% five-star, 28% one-star, very little in between. People who wanted a coding agent are delighted. People who wanted their browser assistant back are not.

This matters beyond a Chrome rating, because it is the clearest available look at what a bundled active-user number is made of. OpenAI reports “8M active users across Codex and ChatGPT Work.” Some of those users are here, in the one-star reviews, having been moved into a Codex surface they did not choose. Growth by replacement counts identically to growth by adoption in a metric like that — and one of the two is not a competitive win over Claude Code.

Anthropic’s extension is bigger and angrier

The mirror-image finding keeps this honest: Anthropic’s Claude extension has the largest install base of the three (11 million) and the worst score (2.71), with 53% one-star reviews. If store ratings were a referendum on which company is winning, Anthropic would be losing its own extension badly.

But its one-stars aren’t about code quality either. They’re about logins that loop, permissions creep, and tokens:

“This extension was installed by claude without asking me”

“They want us to use more tokens so they can make more money”

“Claude CLI ignores the existence of this plugin completely”

Both companies are being punished by their own users for the same things: auth friction, cost, and shipping changes nobody asked for. Neither is being punished for writing bad code.

The same finding, twice, on unrelated surfaces

We ran a theme classification over both corpora independently — 806 first-party extension reviews, and 427 Reddit comments from six subreddits. The result replicates:

• Reliability / bugs: 120 of 806 extension reviews, 21 of 427 Reddit comments
• Auth / login: 111 extension reviews, not measured on Reddit
• Cost / pricing / tokens: 59 extension reviews, 20 Reddit comments
• Rate limits / quotas: not measured in extension reviews, 17 Reddit comments
• Workflow / running both tools: not measured in extension reviews, 22 Reddit comments
• Forced product change: 31 extension reviews, not measured on Reddit
• Privacy / permissions: 25 extension reviews, not measured on Reddit
• Context handling: not measured in extension reviews, 17 Reddit comments
• Speed: not measured in extension reviews, 10 Reddit comments
• Code quality: 3 extension reviews, 5 Reddit comments

Code quality is the least-discussed theme in both corpora, and it isn’t close. Three mentions in 806 store reviews. Five in 427 Reddit comments. Two populations with nothing in common — one grumbling about a browser extension, one deep in CLI workflows — independently agree that the thing this debate is nominally about is the thing nobody brings up.

What they bring up instead is the bill and the plumbing. That is a coherent picture for products priced as metered subscriptions with quotas that reset: the same 8M-users post announces a usage-limit reset in its second sentence. When both tools are good enough that quality stops being the bottleneck, the argument moves to what they cost and whether they log you in.

GitHub says the opposite of the headline

Both tools have a canonical public repository, both are actively pushed to, and both have a star count anyone can verify in seconds.

• Stars (July 15, 2026): Claude Code 137,890, Codex 98,105 — Claude Code leads 1.41x
• Forks: Claude Code 22,235, Codex 14,628 — Claude Code leads 1.52x
• Open issues: Claude Code 11,461, Codex 9,481
• Repository created: Claude Code Feb 22, 2025, Codex Apr 13, 2025
• Repository contains: Claude Code issue tracker + docs, Codex full Rust source under Apache-2.0

On the one surface where both products expose a comparable public number, Codex is not ahead — on the exact day the “Codex is winning” headlines peaked.

We are not going to pretend that settles it, because the two repositories are not the same kind of object. openai/codex is the actual Rust source under Apache-2.0: you can read it, fork it, ship patches. A star there can mean "I want this code." anthropics/claude-code is an issue tracker and documentation repo for a closed-source tool distributed over npm - a star there is closer to a bookmark. Those are different acts, and a raw comparison flattens them.

The honest reading is narrow: Codex is not ahead here, which is incompatible with “and it’s not even close.” It is not proof that Claude Code is winning.

We also tried to measure release cadence for both repositories, which would have been genuinely useful. The releases endpoint returned upstream errors for both, so we left that cut out rather than guess.

On Reddit, the threads that name both tools aren’t comparisons

We pulled the 100 most recent posts from each of six subreddits — r/ClaudeCode, r/ClaudeAI, r/OpenAI, r/ChatGPTCoding, r/CodingLLM, r/Claudex — 495 posts, plus 427 comments.

One methodological note first, because it is a finding in itself: Reddit’s search endpoint cannot isolate this topic. A query for “codex vs claude code” matches loosely on the word “code” and returns Destiny 2 promo-code lists, VS Code memes, and an Arduino project. Anyone quoting Reddit search-result counts in this debate is counting noise. We went subreddit by subreddit and matched on tool names.

• r/ClaudeCode: 100 posts sampled (Jul 14–15) — Codex named 12, Claude Code named 40, both named 10
• r/ClaudeAI: 100 posts sampled (Jul 14–15) — Codex named 6, Claude Code named 43, both named 4
• r/OpenAI: 100 posts sampled (Jul 13–15) — Codex named 18, Claude Code named 1, both named 0
• r/ChatGPTCoding: 100 posts sampled (Feb 14 — Jul 7) — Codex named 21, Claude Code named 13, both named 5
• r/CodingLLM: 92 posts sampled (Nov 2025 — Jul 1) — Codex named 5, Claude Code named 8, both named 1
• r/Claudex: 3 posts sampled (Feb 9) — Codex named 2, Claude Code named 1, both named 1
• Total: 495 posts — Codex named 64, Claude Code named 106, both named 21

Twenty-one of 495 posts named both tools. We read them. Almost none are verdicts — they are integration questions: What’s the best way to use both Codex and Claude Code? and Is Codex useful in a Claude Code based workflow?

The answers are about plumbing, not allegiance. In the best way to use both thread, developers describe symlinking ~/.claude/skills into ~/.codex/skills, registering OpenAI's Codex MCP server inside Claude Code, and pointing both agents at the same working directory. One reply is simply:

“Best way to use: rub them together.”

The dominant pattern is a division of labour — one agent plans, the other implements or reviews, specifically because a second model catches what the first misses. From the Codex in a Claude Code workflow thread:

“It’s about how they work together. I use both.”

And, more bluntly, the comment that could serve as this article’s thesis:

“Stop scoring Codex against Fable head to head.”

What this does and does not show

What we think holds up: code quality is the least-argued theme in two unrelated corpora, by a wide margin. Both vendors’ users complain about cost, auth and reliability instead. OpenAI converted its ChatGPT extension into a Codex surface on July 9 and a visible slice of its users are angry about it, which is direct evidence that bundled active-user growth and competitive adoption are not the same thing. On the one comparable public metric, Claude Code leads. Co-mention threads skew heavily toward interoperability.

What limits it:

• The samples are small and unevenly windowed. 100 posts covers about a day in r/ClaudeCode and about five months in r/ChatGPTCoding, and we did not normalise for it. r/ChatGPTCoding’s newest post is eight days old and asks for new moderators, so its slowness reflects that sub’s health, not the tools.
• Store reviews are the angriest fraction of a userbase. 333 ratings against 2,000,000 installs is not a sample of users; it is a sample of people motivated enough to write. Bimodality is what that always looks like. Read the themes, not the scores.
• Reddit’s public feeds cap at 100 items and do not expose scores, so we could not weight by engagement. Every post counts once.
• Theme classification is keyword-based, not semantic. It undercounts anyone describing a rate limit without the words “rate limit,” and the two corpora were coded with different theme sets, so the columns above are not strictly like-for-like. Treat the counts as a shape, not a census.
• GitHub stars are a vanity metric, and the two repos hold different things, as covered above.
• We could not measure release cadence — the endpoint failed upstream for both repositories.

None of these rescue the “Codex overtook Claude Code” claim. They mean our counter-evidence is a shape, not a verdict — which is the standard the original claim never met.

The actual answer

Is Codex beating Claude Code? On the evidence available to anyone outside either company: nobody can tell you, and the developers using both tools have largely stopped caring.

The user counts can’t answer it — they measure two products, on different dates, against a stale baseline, with no published denominator, and at least some of the growth is users being migrated into a Codex surface they are one-starring. The mobile stores can’t answer it: we checked, and the signal is one review in a hundred. The one clean public metric points mildly at Claude Code, with real caveats. And the people actually doing the work aren’t holding a referendum — they are 22 comments deep in how to make the two agents hand work to each other, and far deeper in complaints about what that costs.

The race being narrated is not the race being run. That is the finding.

Sources

Sources

Methodology

Every figure here was pulled through Crawlora’s own APIs on July 15, 2026, rather than taken secondhand: the Chrome Web Store item and review endpoints plus the Chrome extensions dataset to enumerate each vendor’s first-party listings, App Store reviews, GitHub repository metrics, subreddit posts and thread comments, and the X post endpoint for Tibo’s announcement verbatim.

Corpus: 1,828 items — 806 first-party Chrome extension reviews (Claude, ChatGPT, ChatGPT search), 495 Reddit posts and 427 comments across r/ClaudeCode, r/ClaudeAI, r/OpenAI, r/ChatGPTCoding, r/CodingLLM and r/Claudex, and 100 recent iOS reviews across the ChatGPT and Claude apps. We publish aggregates only — no raw review or comment records.

Reproduce it yourself in the Playground; the how to scrape GitHub, how to scrape Reddit and how to scrape App Store reviews guides walk through the exact calls, and pricing covers the free tier. For the number that started this, see our fact-check of the 6M/7M claim. For the wider landscape, see web scraping with AI and best AI web scraping tools.

Frequently asked questions

Is Codex beating Claude Code?

On evidence available outside either company, nobody can tell you. OpenAI’s 8M figure covers Codex and ChatGPT Work combined, not Codex alone, and the Claude Code number it gets compared against is a stale, undated 2M snapshot. On GitHub — the one surface where both tools expose a directly comparable public metric — Claude Code leads with 137,890 stars to Codex’s 98,105 as of July 15, 2026. Part of Codex’s reported growth is also users being migrated into Codex surfaces they did not choose: OpenAI converted its ChatGPT Chrome extension into Codex for Chrome on July 9, 2026, and 28% of that extension’s recent reviews are one-star.

Why can’t App Store or Google Play reviews settle the Codex vs Claude Code debate?

Because neither coding agent is on them, and we measured the gap rather than assuming it. The ChatGPT iOS app (8,592,629 ratings) and the Claude app (199,486) are consumer chat products; across 100 recent reviews of both, exactly one mentioned a coding agent. That roughly 1% signal is why the widely-cited 43:1 ratings ratio tells you about chat adoption, not about Codex or Claude Code.

Do Codex and Claude Code have browser extensions with reviews you can mine?

Yes, and this is the surface most people miss. Anthropic ships a Claude extension (11,000,000 users, 2.71 rating, 53% one-star) and OpenAI ships ChatGPT search (4,000,000 users, 3.33) and ChatGPT (2,000,000 users, 3.71). On July 9, 2026 OpenAI turned that last one into Codex for Chrome, replacing the Atlas sidebar — and 52 of its 166 most recent reviews mention Codex, making it the only first-party store corpus with real Codex feedback in it.

What do developers actually argue about when comparing Codex and Claude Code?

Cost and quotas, not code quality — and the finding replicates across two unrelated corpora. Among 427 Reddit comments, code quality was the least-discussed theme (5 mentions) while cost (20) and rate limits (17) drew 37. Among 806 first-party browser-extension reviews, code quality was again last with just 3 mentions, behind reliability (120), auth and login (111), and cost (59).

Do developers use Codex and Claude Code together?

Frequently. Of 495 recent posts across six subreddits, 21 named both tools, and nearly all were integration questions rather than comparisons. Common patterns include symlinking Claude Code skills into Codex, registering OpenAI’s Codex MCP server inside Claude Code, and having one agent plan while the other implements or reviews.

Originally published on crawlora.net. Crawlora is a structured web-data, search, and anti-bot API — dozens of platforms as normalized JSON, plus a hosted MCP server, with a free tier (no card).

Originally published at https://dev.to on July 27, 2026.

Key takeaways

• Visa’s SEC filings have named the FIFA World Cup as a driver of marketing-expense growth in every tournament cycle since at least 2014 — four World Cups running, always inside the “Marketing expenses” discussion of a 10-Q or 10-K.
• The size of that swing is not a clean escalation. Marketing expense fell 9% year-over-year in the quarter tied to Brazil 2014 (a tough comp against the prior year’s Sochi Olympics spend), then rose 8% for Russia 2018, 18% for Qatar 2022, and 44% for the 2026 tournament — its biggest jump of the four, and the only quarter that isn’t final yet.
• Every single cycle credits the World Cup jointly with that year’s Winter Olympics, never alone. Visa sponsors both, and since 1994 the Winter Games and World Cup have always shared a calendar year — no filing isolates a World-Cup-only dollar figure.
• Qatar 2022 is the only cycle where the reporting quarter fully contains the tournament dates (Nov 20 — Dec 18, 2022 sits entirely inside Visa’s fiscal Q1 2023). The other three cycles’ figures come from quarters that only partly overlap, or precede, kickoff.
• The 44% figure for 2026 is a leading indicator, not a final result — it’s from the quarter before the tournament (January-March 2026). The quarter that actually contains the June 11-July 19 tournament isn’t due until early August 2026.

Visa’s SEC filings have named the FIFA World Cup as a reason its marketing spend moved — every tournament cycle since at least 2014. We pulled the exact sentence from Visa’s 10-Q or 10-K for Brazil 2014, Russia 2018, Qatar 2022, and the ongoing 2026 tournament, along with the dollar figures behind each one. The naming pattern holds for over a decade. The dollar swing behind it does not: marketing expense fell 9% year-over-year the quarter tied to 2014, then rose 8%, 18%, and 44% in the three cycles since.

What this post draws on

All filing text below is quoted directly from Visa Inc.’s (SEC CIK 1403161) own EDGAR filings — one 10-Q or 10-K per World Cup cycle since 2014 — pulled live via Crawlora’s SEC filing-sections endpoint. Two caveats up front: the comparison windows aren’t perfectly uniform, since each row uses whichever filing actually contains Visa’s World Cup sentence, and that sentence lands in a different position relative to kickoff every cycle (exact dates in the table below); and every quote credits the World Cup jointly with that year’s Winter Olympics, never in isolation — see “The World Cup never travels alone” below for why.

The pattern, cycle by cycle

*Pre-tournament quarter — the quarter that actually contains the 2026 tournament hasn’t been filed yet.

Same-quarter year-over-year change in Visa’s reported marketing expense, the quarter each cycle’s World Cup sentence appears in. 2026 is a pre-tournament figure, not a final one — see below.

What each filing actually says

2014 (Brazil) — 10-Q for the quarter ended June 30, 2014, filed July 24, 2014:

“Marketing decreased during the three month comparable period and increased over the nine month comparable period primarily due to elevated spend supporting the 2014 Sochi Winter Olympics campaign beginning in the third quarter of fiscal 2013 through the first half of fiscal 2014. The nine month comparable period also increased due to spend supporting the 2014 FIFA World Cup campaign.”

2018 (Russia) — 10-Q for the quarter ended June 30, 2018, filed July 27, 2018:

“Marketing expenses increased primarily due to the higher level of spending to support a number of campaigns, including the PyeongChang 2018 Olympic Winter Games and 2018 FIFA World Cup.”

2022 (Qatar) — 10-Q for the quarter ended December 31, 2022, filed January 27, 2023:

“Marketing expenses increased primarily due to increased spending in various campaigns, including the FIFA World Cup 2022™ and client marketing.”

2026 (USA/Mexico/Canada) — 10-Q for the quarter ended March 31, 2026, filed April 29, 2026:

“Marketing expenses increased over the three and six-month prior-year comparable periods primarily due to higher spending for client marketing and various campaigns, both driven in part by the Olympic and Paralympic Winter Games Milano Cortina 2026 and the FIFA World Cup 2026™.”

Four filings, four different fiscal quarters, one recurring sentence.

The World Cup never travels alone

Notice what’s in every quote above: the World Cup is never the only event named. It’s always paired with that year’s Winter Olympics.

World Cup cycle Same-fiscal-year Winter Olympics

2014 (Brazil)

Sochi 2014 (February)

2018 (Russia)

PyeongChang 2018 (February)

2022 (Qatar)

Beijing 2022 (February)

2026 (USA/Mexico/Canada)

Milano Cortina 2026 (February)

That’s not a coincidence specific to Visa — since 1994, the Winter Olympics and the World Cup have shared the same calendar year every time, both running on their own four-year cycles offset from the Summer Olympics. Visa sponsors both events, and its October-September fiscal year always contains both in the same reporting window. The practical result: no Visa filing anywhere in this dataset isolates a World-Cup-only marketing figure. Every dollar change above is a joint attribution, and there’s no clean way to split it from the filing text alone.

2026 is still a leading indicator

The 44% figure is the biggest of the four cycles, but it’s also the least finished. It comes from Visa’s fiscal Q2 2026 (January-March) — the quarter before the tournament’s June 11 opening match. By contrast, 2014’s and 2018’s quarters ran through most of their tournaments, and 2022’s quarter (October-December) fully contained Qatar’s entire November 20 — December 18 window.

Visa’s fiscal Q3 2026 10-Q — the filing that will actually cover the June 11-July 19 tournament window — is due in late July or early August 2026, roughly following the same ~30-day filing lag as its Q1 FY2023 report after Qatar. Until then, 44% is what Visa’s pre-tournament ramp-up looked like, not what the World Cup itself did to its books.

How we measured this

Pulled directly from Visa Inc.’s (CIK 1403161) 10-Q and 10-K filings via Crawlora’s SEC filing-sections endpoint, one filing per World Cup cycle — specifically, the fiscal quarter (or year, checked for 2022) in which Visa’s own Management’s Discussion and Analysis names the FIFA World Cup as a driver of marketing-expense growth. Accession numbers for the two oldest filings (2014, 2018) were located via SEC’s company-submissions filing index rather than EDGAR’s full-text search, which is tuned to surface recent filings first. Every figure is the as-reported GAAP marketing-expense line item — not adjusted or non-GAAP — read directly from each filing’s Operating Expenses table.

Related reading: 105 SEC Filings Mention the World Cup in 2026 — Visa’s Explains a 44% Marketing Jump · The Official FIFA World Cup App Scores 2.65 Stars. Its Ticket App Scores 1.79.

Originally published at https://crawlora.net on July 23, 2026.

Key takeaways

• We sampled the top comments on the World Cup final’s two highest-view YouTube highlight videos (FIFA’s own, 12.6M views, and Fox Sports’, 2.9M) and three of the highest-view TikTok clips about the match. The two platforms argue about completely different things.
• On YouTube, 16.7% of the 60 comments sampled independently cite the same fact — that Argentina had zero shots on target — and 26.7% use flat dominance/deserved-it framing. The single most-liked comment (14,000 likes), on FIFA’s own highlights video: “The most deserved win at the whole tournament. Absolute domination.”
• On TikTok, it depends entirely on which clip you land on. On Fox Sports’ trophy-ceremony clip (996K plays, 1,701 comments), the two most-liked comments by a wide margin — 7,430 and 4,247 likes, versus 625 for the next-highest — are both about Donald Trump’s presence at the ceremony, not the football. A third of the top comments we sampled there mention him by name.
• The stat that dominates YouTube shows up on TikTok too — contested. One long-form comment argues Argentina “let the match go,” citing the same zero-shots-on-target fact as evidence of a conspiracy rather than dominance.
• A different TikTok video — an account posting under the name “Christiano Ronaldo” (the misspelling suggests it isn’t the real player) congratulating Spain — pulled 1.9M plays, and its comments are dominated by a Messi-vs-Ronaldo-vs-Yamal generational debate, plus at least one commenter asking outright whether the account is “AI or it’s real.”
• Pulled live via Crawlora’s YouTube and TikTok comment endpoints on July 21, 2026, two days after the final. Samples are each platform’s top-ranked comments (by relevance/likes), not a full census — see the methodology section for exact counts and a pagination-overlap dedup we had to apply.

Ask what people actually said about the World Cup final, and the answer depends entirely on which app you open. We pulled the real comment threads under the final’s biggest videos — not hashtag totals, not post counts, the actual top comments people are reading — on YouTube and TikTok. YouTube argues the result with a stat. TikTok, on one of its biggest clips, barely mentions the result at all.

Theme share across 60 top comments sampled on FIFA’s official highlights video (12.6M views) and Fox Sports’ (2.9M), Crawlora YouTube comment pull, July 21, 2026. Categories overlap — a comment can cite a stat and call Argentina dirty in the same sentence.

What this post draws on

All comments and view/like counts came from a live pull through Crawlora’s YouTube ( youtube_search, youtube_comments) and TikTok ( tiktok_search, tiktok_comments) endpoints on July 21, 2026, two days after the July 19 final. Every theme percentage below is counted over the specific sample described in that section - see "How we measured this" for exact video IDs, sample sizes, and a real gotcha (paginated comment calls returning duplicate rows) we had to filter out before counting anything.

YouTube: the same stat, independently, over and over

Search YouTube for the final and the highest-view results are exactly what you’d expect: FIFA’s own highlights reel (12.6M views) and Fox Sports’ (2.9M). What’s less expected is how uniform the top comments are. Commenters on both videos, independently, keep reaching for the same piece of evidence:

The single fact commenters keep independently reaching for: Argentina didn’t register a shot on target. It shows up phrased at least four different ways — “Argentina made history by failing to register a single shot on target in a World Cup final” (4,200 likes), “Argentina having 0 shots on target until the last 5 minutes is insane” (9,200 likes), “Reminder that Argentina had 0 shots on target in 120 min” (2,600 likes), and as a joke — “Spain’s goalie could have worked remotely” (3,900 likes). Nobody is quoting each other; these are independent top-level comments on two different videos, converging on the same number.

Layered on top of the stat is a moral framing that goes well past “Spain played better”: “Argentina did not deserve to play in the World Cup final because they relied on excessive physicality, dirty play, and gamesmanship rather than fair competition” pulled 7,100 likes and 249 replies — one of the most-discussed comments in the sample. And the single most-liked comment on FIFA’s own video isn’t about a player at all: “I’ve never been so satisfied in a long time, thank you Spain for saving football” (12,000 likes). A near-identical line — “thank you Spain for saving football” — shows up independently on the Fox Sports video too, at lower volume. It isn’t one viral phrase copy-pasted; it’s the same sentiment arrived at separately, twice.

TikTok: it depends which clip you land on

TikTok doesn’t converge the way YouTube does — the dominant topic changes entirely depending on which specific video you open, even for the same match:

Video (plays) What the top comments are actually about

Fox Sports trophy-ceremony clip (996K)

Trump’s presence at the ceremony

“Christiano Ronaldo”-named account, congratulating Spain (1.9M)

Messi vs. Ronaldo vs. Yamal — and whether the account itself is real

Yamal-focused edit (2.3M)

Generational awe that Yamal is 19

Fox Sports’ own trophy-lift clip is the sharpest case. It has 1,701 comments total; we sampled the top 21. The two most-liked by a wide margin — “bro i’m crying, yall saw Trump tryna be involved in the trophy lift” (7,430 likes) and “why is trump there” (4,247 likes) — are both about the former US president standing near the trophy, not the match. The next-highest comment on any topic has 625 likes. A third of the sample (7 of 21) mentions Trump by name at all: “Who invited Donald trump,” “The way they zoom in so you can’t see Trump standing there,” “trump WANTED TO BE Invited into their Celebration…. But NO.” This is the same moment Crawlora’s social-listening study already traced spreading across Reddit and dedicated TikTok reaction videos within four minutes of the live broadcast — what this comment sample adds is that the reaction wasn’t confined to purpose-made reaction content. It’s the top comment under a neutral, official trophy-lift highlight clip that isn’t about Trump at all.

The other two videos show TikTok can converge too, just on something else entirely. The account posting as “Christiano Ronaldo” — a misspelling that suggests it isn’t the real Cristiano Ronaldo — racked up 1.9M plays on a simple congratulations post, and its comments are almost entirely a Messi-vs-Ronaldo-vs-Yamal generational argument: “messi❌ c.Ronaldo✅ yamal✅,” “yamal is better than ronaldo,” “I don’t like Argentina and Messi.” One commenter cuts through it directly: “AI or it’s real” — a real, live question about whether a viral World Cup account is even who it claims to be.

The contested stat

The “zero shots on target” fact that YouTube treats as settled evidence of dominance gets argued with on TikTok. One reply on the Fox Sports clip, unprompted and at length:

“I genuinely believe Argentina let that match go… What I find hard to believe is that a team with Argentina’s quality finished with 0 shots on target and 0 real attacking threat. That just doesn’t add up to me. […] maybe Messi wanted Lamine Yamal to lift the trophy, maybe something else happened I don’t know.”

Another, shorter one on the same video: “it was rigged because the referee did not foul the Spain players.” Neither comment disputes the underlying number — both treat it as suspicious rather than as proof, which is the opposite read from every high-vote YouTube comment citing the same fact. Same statistic, opposite conclusion, different platform.

How we measured this

Pulled live via Crawlora’s youtube_search (sorted by view count, query "world cup 2026 final Spain Argentina highlights") and youtube_comments, plus tiktok_search (keyword "world cup 2026 final") and tiktok_comments, on July 21, 2026, US/English locale.

YouTube sample (n=60): the full top-comment set returned for two videos — FIFA’s “Highlights | Spain 1–0 Argentina | FIFA World Cup 2026™ FINAL” (12,598,306 views, video id 6HaHNYjnghE) and Fox Sports' "Spain vs Argentina Highlights" (2,899,950 views, video id x-cpRHf4xd4). Both videos' comment streams were exhausted (the API returned an empty continuation token), so this is each video's complete top-ranked comment set at pull time, not an arbitrary cutoff. Theme percentages are keyword/pattern-matched over the full text of all 60; categories overlap by design (a single comment can cite the shots-on-target stat and call Argentina dirty in the same sentence), so the five shares don't sum to 100%.

TikTok sample: three videos identified from a view-sorted search — Fox Sports’ trophy-ceremony clip (996,100 plays, aweme id 7664376797336751391, 1,701 total comments, top 21 unique comments sampled after dedup), the "Christiano Ronaldo"-named account's congratulations post (1.9M plays, aweme id 7664437295822540046), and a Yamal-focused edit from account "Conditinho" (2.3M plays, aweme id 7664394784974376224).

A real gotcha, worth flagging: paginating TikTok’s comments endpoint two pages deep on the same video returned two comments that were exact duplicates of ones already seen on page one — same author, same text, same like count. This is the same overlapping-pagination behavior Crawlora’s social-listening study already documented for TikTok’s search and hashtag endpoints; it applies to the comments endpoint too. We deduped by comment text before counting anything, and the 21-comment TikTok sample and the Trump percentage above are both post-dedup.

Related reading: The World Cup Final Boo: How It Spread Across Reddit and TikTok, Hour by Hour · The Official FIFA World Cup App Scores 2.65 Stars. Its Ticket App Scores 1.79. · What America Searched For After the 2026 World Cup Final

Originally published at https://crawlora.net on July 22, 2026.

Key takeaways

• FIFA runs three separate apps for this World Cup, and its own users rate them nothing alike: the tournament companion app averages 2.65★ on the App Store (712 ratings) and 2.97★ on Google Play; the dedicated ticket-delivery app averages 1.79★ (390 ratings) and 2.21★ on Google Play; FIFA’s separate, year-round football app holds 4.62★ (2,163 ratings) and 4.52★ on Google Play.
• It got worse as the stakes went up, not better. In the 100 most recent Google Play reviews on each app, the tournament app’s 1-star share rose to 58% (from 43.7% lifetime) and the ticket app’s rose to 84% (from 76.9% lifetime) — covering exactly the knockout-round and final window.
• The ticket-app complaints aren’t cosmetic: 41% of the negative reviews we sampled describe being unable to access or transfer a ticket already paid for, and 27% describe being locked out by login or verification failures — on the one app that determines whether you get into the stadium.
• Real, dated reviews: one reviewer describes losing $2,100 and being unable to enter a Brazil match; another paid $2,600 and spent 90 minutes and four password resets the night before a flight to the game, still with no tickets. A third’s own account of a SeatGeek ticket getting pulled back by the seller five days before delivery matches, almost beat for beat, the ‘ghost ticket’ pattern NPR reported on this same tournament.
• Pulled live via Crawlora’s App Store and Google Play endpoints on July 21, 2026, two days after the final. The star histograms are each app’s lifetime cumulative distribution; the ‘getting worse’ read and the complaint themes come from the 100 most recent reviews on each app — a sample, not a full census.

FIFA runs three separate apps for this World Cup, and its own users rate them nothing alike. The tournament companion app averages 2.65 stars. Its dedicated ticket-delivery app — the one that determines whether you actually get into the stadium — averages 1.79, with more than three out of every four ratings at the lowest possible score. FIFA’s separate, year-round football app, unrelated to this specific tournament, holds 4.62. Same organization, the same six weeks, a 2.8-star spread. We pulled the App Store and Google Play histograms directly, plus the most recent reviews on

each app, to see exactly what’s driving it.

App Store rating average per app (Crawlora, pulled July 21, 2026, two days after the July 19 final). Google Play shows the same order: 2.21★, 2.97★, 4.52★.

What this post draws on

All three star histograms and every review quoted below came from a live pull through Crawlora’s App Store ( appstore-ratings, appstore-reviews) and Google Play ( googleplay-reviews) endpoints on July 21, 2026 - two days after the July 19 final. Histograms are each app's full lifetime rating distribution; the "100 most recent reviews" samples used for the trend and complaint-theme reads are dated snapshots, not a full census. See the methodology section below for exactly how the theme percentages were counted.

Three apps, one World Cup, one 2.8-star spread

FIFA didn’t build one World Cup app — it built (or commissioned) three, and split entirely different jobs across them:

FWC2026 Mobile Tickets

Ticket delivery & stadium entry — the only way in with a purchased ticket

1.79★

390

2.21★

FIFA World Cup 2026

Tournament companion — scores, fixtures, fantasy football

2.65★

712

2.97★

FIFA Official App

Year-round football app — clubs, transfers, unrelated to this tournament

4.62★

2,163

4.52★

The tickets app carries a bundle id of io.tixngo.app.fifatickets - it's a white-labeled product built by a third-party ticketing vendor, TixNgo, not FIFA's in-house app team. That's context, not an excuse: it's still the one app every ticket-holder is forced to use to get through the gate.

The full 1–5 star breakdown makes the gap even clearer than the averages:

It got worse as the stakes went up, not better

If the low ratings were mostly early-tournament teething problems, the most recent reviews should look better than the lifetime average. They look worse:

The tournament app’s most recent 100 Google Play reviews land almost entirely inside the knockout rounds and the final itself — exactly the window when usage, and the cost of the app failing, is highest. The tickets app’s recent window runs from late May through five days before the final, covering the exact stretch NPR and other outlets were reporting ticket-delivery problems across the tournament.

What people are actually angry about

For the ticket app, the complaints aren’t cosmetic — they’re about whether the ticket you paid for actually gets you into the building:

What the negative review describes Share of sampled 1–2★ reviews

Can’t access or transfer a ticket already paid for

41.2%

Locked out by login or verification failures

27.1%

Confusing navigation or design

18.8%

Slow to load or freezes

12.9%

The reviews are specific and dated. One App Store review from June 19 reads simply: “Lost 2100 Dollars worth of tickets, CANNOT GET INTO BRAZIL GAME.” Another, from July 1, describes paying $2,600 for tickets, then spending 90 minutes and four password resets trying to retrieve them the night before a flight to the match — “still no tickets so I will try again tomorrow. #Fail.” A June 28 review: “total failure doesn’t work on Samsung Galaxy S24 Ultra. Can not even verify login. Timeout error.” A July 6 Google Play review: “day of match, app won’t work — can’t access the tickets I purchased.”

One July 2 App Store review is essentially a first-person case study of the “ghost ticket” pattern NPR reported on this same tournament: the reviewer bought Senegal-vs-Belgium tickets through SeatGeek on June 12 as a birthday present for their son, was told the tickets would be available June 30, and found nothing on either the FIFA app or website that day or the next. At the stadium box office on match day, they learned the reseller had taken the seats back on June 25 — five days before FIFA had told them delivery would happen.

The tournament app’s complaints run a different pattern — less about access, more about the app simply working:

“The app functions like it’s been rushed through development,” reads one July 3 review. “I’ve had to update the app twice in the past two days. For an app that only needs to be used for three weeks. It’s inexcusable.” A July 19 review, the day after the final: “here all is messed up nothing can be done by this app. no live match video no score post nothing.” Others describe the app draining 60% of their phone’s battery running in the background during a match, and one reviewer flags that ticket access is buried inside a separate app entirely — “it’s also mysteriously buried so far down into the app without proper navigation.”

It isn’t unanimous complaint — both apps have five-star reviews too, including a couple of workaround tips from users who found the login loop by turning off their VPN, or by transferring tickets to a second account when the app claimed they were “already logged in elsewhere.” But those are the exceptions in the sample, not the rule.

This isn’t the first sign of ticketing trouble this tournament

Crawlora already covered the pricing side of this story: final tickets that reached $2.3 million on FIFA’s own resale marketplace, a first-time dynamic-pricing system, and the “slow ticketing” pattern of metering out inventory in batches. That post used anti-bot scanning and contemporaneous reporting — Al Jazeera, Newsweek, NPR — as its evidence. This one uses a completely different data source, structured app-review mining, and lands on the same underlying story from the other side: real people, with real money on the line, unable to get a purchased ticket to actually work.

How we measured this

Pulled live via Crawlora’s App Store ( appstore-ratings for the full histogram, appstore-reviews for individual review text) and Google Play ( googleplay-reviews) endpoints on July 21, 2026, US storefront. The three apps: FIFA World Cup 2026 (App Store id 6476561442 / Google Play com.fifa.tournament), FWC2026 Mobile Tickets (App Store id 6532603739 / Google Play io.tixngo.app.fifatickets), and FIFA Official App (App Store id 756904853 / Google Play com.fifa.fifaapp.android).

Star histograms are each app’s full lifetime rating distribution as Apple reports it — a complete population, not a sample. The “100 most recent reviews” figures are exactly that: the newest 100 Google Play reviews returned per app at pull time, sorted newest-first, which is why the two apps’ windows cover different date ranges (the tickets app’s most recent 100 reviews stretch back to late May; the tournament app’s only back to July 12 — a sign the tournament app is simply getting reviewed far more often right now). Complaint-theme percentages are keyword-matched counts over the 1–2 star reviews with review text in that same 100-review sample, not the full lifetime population — treat them as directional, not exhaustive, and expect some reviews to touch more than one theme.

Related reading: World Cup Final Tickets Hit $2.3M — The Anti-Bot Data Behind Ticketing · More Apps, Fewer Hits: 18,000 App-Store Winners, by Birth Year · What America Searched For After the 2026 World Cup Final

Originally published at https://crawlora.net on July 21, 2026.