From Consumer Rights Wiki

Microsoft Office 2019 and 2021 for Mac view-only con­ver­sion (2026) is a sched­uled re­mote degra­da­tion of per­pet­u­ally-li­censed Microsoft Office soft­ware for ma­cOS and iOS, set for July 13, 2026 when a li­cense-val­i­da­tion cer­tifi­cate used by the Office apps ex­pires.[1] After Office 2019 for Mac reached end of sup­port in October 2023, Microsoft as­sured cus­tomers their in­stalled apps would “continue to func­tion.“[2] The July 13, 2026 con­ver­sion in­stead drops the apps into a Microsoft-defined “reduced func­tion­al­ity mode,” in which files can be opened and viewed but not edited or saved.[1][3] By May 30, 2026, the orig­i­nal 2023 end-of-sup­port page had been re-dated and rewrit­ten on Microsoft’s site; the “continue to func­tion” clause was re­moved.[4][2]

Microsoft an­nounced gen­eral avail­abil­ity of Office 2019 for Windows and Mac on September 24, 2018. In the launch blog post, Microsoft’s Jared Spataro wrote that “Office 2019 is a one-time re­lease and won’t re­ceive fu­ture fea­ture up­dates,” po­si­tion­ing the prod­uct as the on-premises al­ter­na­tive to the Office 365 sub­scrip­tion.[5] Contemporary Microsoft Store pages mar­keted Office Home & Student 2019 as a “One-time pur­chase for 1 PC or Mac” at $149.99, with copy that ex­plic­itly con­trasted the per­pet­ual prod­uct against the Office 365 sub­scrip­tion model: “One-time pur­chases don’t have an up­grade op­tion, which means if you plan to up­grade to the next ma­jor re­lease, you’ll have to buy it at full price.“[6]

Office 2021 for Mac be­came gen­er­ally avail­able on October 5, 2021 un­der the same one-time-pur­chase model & is sched­uled to reach end of sup­port on October 13, 2026 per the Microsoft Lifecycle Policy.[7]

Office 2019 for Mac reached end of sup­port on October 10, 2023.[3]

Microsoft’s end-of-sup­port page for Office 2019 for Mac, be­fore and af­ter the 2026 edit

Internet Archive snap­shot of the page from June 3, 2023; orig­i­nally pub­lished April 12, 2023.[2]

Internet Archive snap­shot of the page from June 3, 2023; orig­i­nally pub­lished April 12, 2023.[2]

The same Microsoft URL cap­tured on May 30, 2026, re-dated Published: May 15th, 2026.[4]

The same Microsoft URL cap­tured on May 30, 2026, re-dated Published: May 15th, 2026.[4]

The June 3, 2023 snap­shot of Microsoft’s end-of-sup­port page con­tained this pas­sage:

Support for Office 2019 for Mac will end on October 10, 2023. Rest as­sured that all your Office 2019 apps will con­tinue to func­tion—they won’t dis­ap­pear from your Mac, nor will you lose any data. However, you could ex­pose your­self to se­ri­ous and po­ten­tially harm­ful se­cu­rity risks.[2]

Support for Office 2019 for Mac will end on October 10, 2023. Rest as­sured that all your Office 2019 apps will con­tinue to func­tion—they won’t dis­ap­pear from your Mac, nor will you lose any data. However, you could ex­pose your­self to se­ri­ous and po­ten­tially harm­ful se­cu­rity risks.[2]

By May 30, 2026, the same URL car­ried a new pub­li­ca­tion date of May 15th, 2026 and a shorter pas­sage:

Support for Office 2019 for Mac ended on October 10, 2023. Rest as­sured that all your Office 2019 apps won’t lose any data. Your data can be ac­cessed on any sup­ported Microsoft 365 or Office prod­uct. However, you could ex­pose your­self to se­ri­ous and po­ten­tially harm­ful se­cu­rity risks.[4]

Support for Office 2019 for Mac ended on October 10, 2023. Rest as­sured that all your Office 2019 apps won’t lose any data. Your data can be ac­cessed on any sup­ported Microsoft 365 or Office prod­uct. However, you could ex­pose your­self to se­ri­ous and po­ten­tially harm­ful se­cu­rity risks.[4]

The 2023 as­sur­ance that the apps would “continue to func­tion” was re­moved; the data-safety clause was kept; a new sen­tence point­ing own­ers to “any sup­ported Microsoft 365 or Office prod­uct” was added.[2][4] The 2023 word­ing was resur­faced in May 2026 by JimmyTech, a San Francisco IT con­sul­tancy, which char­ac­ter­ized the July 2026 con­ver­sion as Microsoft “breaking that promise.“[8]

Microsoft’s ad­min­is­tra­tor doc­u­men­ta­tion states that “Microsoft 365 apps use a dig­i­tal cer­tifi­cate to val­i­date li­cens­ing. The cer­tifi­cate cur­rently in use ex­pires on July 13, 2026. Apps that are up­dated to the min­i­mum re­quired ver­sions al­ready in­clude the re­newed cer­tifi­cate and con­tinue to func­tion nor­mally. Apps on older ver­sions en­ter re­duced func­tion­al­ity mode af­ter the cer­tifi­cate ex­pires.“[1] The min­i­mum re­quired builds are ver­sion 16.83 on ma­cOS and ver­sion 2.93 on iOS, & those builds in turn re­quire ma­cOS 12 (Monterey) or later, or iOS 17.0 or later.[1]

Office 2019 has no fix. The prod­uct line is bounded by a hard build cap be­low the 16.83 thresh­old, and Microsoft’s own sup­port doc­u­men­ta­tion states the is­sue “cannot be re­solved by up­dat­ing or re­in­stalling Office 2019 for Mac.“[3][8] Office 2021 for Mac, by con­trast, is still re­ceiv­ing up­dates through its October 13, 2026 re­tire­ment date & can reach 16.83 on sup­ported ma­cOS ver­sions.[7][3] Windows and Android ver­sions of Office are not af­fected by the cer­tifi­cate ex­piry.[1]

After July 13, 2026, af­fected in­stalls of Word, Excel, PowerPoint, Outlook, and OneNote on Mac, iPhone, and iPad will en­ter re­duced func­tion­al­ity mode, in which Microsoft says users “can open and view files but can’t edit, save, or ac­cess full fea­tures.“[1] Office 2021 for Mac and Microsoft 365 for Mac users on ma­cOS 12 (Monterey) or later can avoid the con­ver­sion by up­dat­ing to build 16.83.[1] Office 2019 for Mac users have no up­date path.[3]

Microsoft be­gan email­ing af­fected cus­tomers in mid-May 2026 about the up­com­ing change.[8] PiunikaWeb, which pub­lished the ear­li­est press cov­er­age on May 16, 2026, char­ac­ter­ized the user re­sponse as “largely neg­a­tive.“[9] The email in­cluded an of­fer of a free Microsoft 365 Personal trial that re­quires a pay­ment method and con­verts to a paid sub­scrip­tion if not can­celled.[3][9]

Microsoft di­rects af­fected users to three op­tions: con­tin­u­ing to use the apps in view-only mode, switch­ing to the free Microsoft 365 web apps, or pay­ing for a Microsoft 365 sub­scrip­tion or a new per­pet­ual Office Home 2024 li­cense.[3][1] Microsoft has is­sued no pub­lic state­ment rec­on­cil­ing the July 2026 con­ver­sion with the 2023 “continue to func­tion” as­sur­ance.[2]

AppleInsider’s Amber Neely, in a May 28, 2026 ar­ti­cle, wrote that Microsoft “will be ef­fec­tively brick­ing the stand­alone Office 2019 for Mac, iPad, and iPhone users on July 13, 2026.“[10] JimmyTech framed the choice as dis­cre­tionary:

But cer­tifi­cates can get re­newed. The fact that Microsoft is us­ing this ex­pi­ra­tion as a dead­line that re­tires older ver­sions of Office, rather than qui­etly re­new­ing the cer­tifi­cate, is a choice.

But cer­tifi­cates can get re­newed. The fact that Microsoft is us­ing this ex­pi­ra­tion as a dead­line that re­tires older ver­sions of Office, rather than qui­etly re­new­ing the cer­tifi­cate, is a choice.

[8]

TidBITS Talk and PiunikaWeb com­menters dis­cussed mi­grat­ing to LibreOffice, OnlyOffice, and Apple’s Pages.[11][9]

Microsoft

Microsoft 365

Adobe Creative Suite ac­ti­va­tion

↑ 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 “Certificate up­date for Microsoft 365 apps on man­aged ma­cOS and iOS de­vices”. Microsoft Learn. Microsoft. 2026 – 05-14. Retrieved 2026 – 05-29.

↑ 2.0 2.1 2.2 2.3 2.4 2.5 “End of sup­port for Office 2019 for Mac”. Microsoft Support. Microsoft. Archived from the orig­i­nal on 2023 – 06-03. Retrieved 2026 – 05-30.

↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 “Update Microsoft 365 or Office on your ma­cOS or iOS de­vice”. Microsoft Support. Microsoft. May 2026. Retrieved 2026 – 05-29.

↑ 4.0 4.1 4.2 4.3 “End of sup­port for Office 2019 for Mac”. Microsoft Support. Microsoft. Retrieved 2026 – 05-30.

↑ Spataro, Jared (2018 – 09-24). “Office 2019 is now avail­able for Windows and Mac”. Microsoft 365 Blog. Microsoft. Retrieved 2026 – 05-29.

↑ “Buy Office Home & Student 2019”. Microsoft Store. Microsoft. Archived from the orig­i­nal on 2020 – 01-05. Retrieved 2026 – 05-29.

↑ 7.0 7.1 “Office 2021 - Microsoft Lifecycle”. Microsoft Learn. Microsoft. Retrieved 2026 – 05-29.

↑ 8.0 8.1 8.2 8.3 Obomsawin, Jimmy (2026 – 05-21). “Microsoft is dis­abling Office 2019 for Mac on July 13, 2026”. JimmyTech. Retrieved 2026 – 05-29.

↑ 9.0 9.1 9.2 K, Sudhanshu (2026 – 05-16). “Using an older Apple de­vice? Microsoft Office is tak­ing away edit­ing fea­tures soon”. PiunikaWeb. Retrieved 2026 – 05-29.

↑ Neely, Amber (2026 – 05-28). “Microsoft is killing Office 2019 for Mac and iPhone, and you can’t do much about it”. AppleInsider. Retrieved 2026 – 05-29.

↑ “Office 2019 switch­ing to view-only mode, what to do?”. TidBITS Talk. 2026 – 05-16. Retrieved 2026 – 05-29.

The hard part of writ­ing soft­ware has never been the writ­ing. It was build­ing a work­ing model of the do­main in your head first. Before you could ship a pay­roll sys­tem you had to un­der­stand gar­nish­ments and pre-tax de­duc­tions and what hap­pens when some­one’s pay pe­riod strad­dles a rate change. Before you could ship a tran­sit app you had to learn what a GTFS feed is, why a trip and a route aren’t the same thing, and how a bus that’s “on time” can still be wrong. The code was a tran­scrip­tion of that un­der­stand­ing. Acquiring the un­der­stand­ing was the job.

Agentic AI sev­ered the link be­tween the two. You can now pro­duce the soft­ware with­out ever build­ing the model, and that breaks an as­sump­tion the whole pro­fes­sion was or­ga­nized around.

The stan­dard take, in­clud­ing my own from last year, is that these tools am­plify se­nior de­vel­op­ers be­cause se­nior de­vel­op­ers have judg­ment. True, but in­com­plete. What I’ve watched hap­pen since is more spe­cific and more in­ter­est­ing: the bind­ing con­straint has moved from can you build it to can you tell whether it’s right.

Think about who can ac­tu­ally use one of these tools well. Picture two peo­ple.

The first is a do­main ex­pert with no real soft­ware back­ground. A lo­gis­tics dis­patcher, a clin­i­cal coder, an ac­tu­ary. They can’t read a stack trace and they could­n’t tell you the dif­fer­ence be­tween a hash map and a list. But they can look at a sched­ule the agent gen­er­ated and know in­stantly that no dri­ver can legally work that shift, or that a claim with those codes would never pay. They know the cor­rect out­puts for a given set of in­puts be­cause they’ve spent ten years liv­ing in those in­puts and out­puts. Hand them an agent and they are star­tlingly ef­fec­tive, be­cause the thing they’re miss­ing, the abil­ity to pro­duce code, is ex­actly the thing the agent sup­plies. What they bring is the thing the agent can’t: the ground truth.

The sec­ond is a strong gen­er­al­ist en­gi­neer who has never worked in the do­main. They can ar­chi­tect any­thing, they know re­li­a­bil­ity and test­ing and how to keep a sys­tem from falling over at 2am. But drop them into clin­i­cal cod­ing and they can­not tell a plau­si­ble-look­ing wrong an­swer from a right one. The agent will hap­pily gen­er­ate a billing rule that com­piles, passes the tests the en­gi­neer thought to write, and is sub­tly, ex­pen­sively in­cor­rect. The en­gi­neer has no or­a­cle. They can ver­ify that the soft­ware is well-built. They can­not ver­ify that it’s cor­rect, be­cause cor­rect­ness here is de­fined en­tirely by a do­main they don’t hold in their head.

Notice which way this cuts. Pre-agent, the en­gi­neer had a path the dis­patcher did­n’t: they could go learn the do­main. Slowly, painfully, by shad­ow­ing ex­perts and read­ing specs and get­ting things wrong in pro­duc­tion, they would build the men­tal model and then they could build the sys­tem. That path was the whole ca­reer lad­der in a lot of fields. The do­main ex­pert had no equiv­a­lent path, be­cause learn­ing to build re­li­able soft­ware is years of work they were never go­ing to do.

Agentic tools col­lapsed one of those paths and not the other. The en­gi­neer’s ad­van­tage, the abil­ity to trans­late a do­main model into work­ing code, is now cheap. The do­main ex­pert’s ad­van­tage, know­ing what right looks like, is not. You can’t prompt your way to it. There’s no skill file that con­tains the tacit knowl­edge of a per­son who has rec­on­ciled a thou­sand pay­rolls.

So the most valu­able per­son in this new world is the one who has both skills be­cause they can ver­ify at both lay­ers. They know the gen­er­ated code is sound and they know the an­swers it pro­duces are true. They can write the test that en­codes “a dri­ver can’t ex­ceed eleven hours” be­cause they know the rule, and they can tell that the test it­self is mean­ing­ful be­cause they know what they’re test­ing. The agent does the tran­scrip­tion. They do the judg­ing, twice.

If you’re an ex­pe­ri­enced en­gi­neer bet­ting on where to spend the next few years, this is the bet. The me­chan­i­cal skill you sweated for, turn­ing a clear idea into clean code, has got­ten dra­mat­i­cally less valu­able. The thing that’s still scarce is a deep, ver­i­fied model of some real do­main. Go get one. Pick an in­dus­try, an in­stru­ment, a reg­u­la­tory regime, a phys­i­cal process, and learn it the way you once learned a pro­gram­ming lan­guage or frame­work. That’s the part the agent can’t do for you, and it’s the part that’s now worth the most.

Skip to con­tent

/

13:21, 30 May 2026

Anthropic has be­come the most valu­able ar­ti­fi­cial in­tel­li­gence startup in the world, sur­pass­ing OpenAI in mar­ket val­u­a­tion. Following a new fund­ing round, the val­u­a­tion of the de­vel­oper be­hind the Claude AI as­sis­tant has ap­proached the $1 tril­lion mark, re­ports a Qaz­in­form News Agency cor­re­spon­dent.

Anthropic an­nounced that it had raised $65 bil­lion in a Series H fund­ing round. The largest in­vestors in­cluded Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital.

Following the deal, Anthropic of­fi­cially over­took OpenAI in mar­ket val­u­a­tion and be­came the largest AI com­pany among Silicon Valley’s pri­vate star­tups.

The new val­u­a­tion is nearly three times higher than the com­pa­ny’s February val­u­a­tion, when Anthropic was es­ti­mated to be worth around $380 bil­lion. The fund­ing pack­age also in­cluded pre­vi­ously agreed in­vest­ments, in­clud­ing $5 bil­lion from Amazon.

The main dri­ver be­hind Anthropic’s growth is said to be the pop­u­lar­ity of its Claude AI as­sis­tant and the Claude Code ser­vice, which is widely used by soft­ware de­vel­op­ers. The com­pany re­ported that its an­nual rev­enue had grown to $47 bil­lion. Last year, the fig­ure stood at about $10 bil­lion.

At the same time, Anthropic in­tro­duced its new ar­ti­fi­cial in­tel­li­gence model, Claude Opus 4.8, as well as the closed sys­tem Claude Mythos Preview, which of­fers en­hanced cy­ber­se­cu­rity ca­pa­bil­i­ties for cor­po­rate clients.

Anthropic Chief Financial Officer Krishna Rao stated that de­mand for Claude prod­ucts con­tin­ues to grow rapidly around the world.

It is noted that Anthropic’s rise has in­ten­si­fied com­pe­ti­tion in the ar­ti­fi­cial in­tel­li­gence mar­ket. In March, OpenAI was val­ued at $852 bil­lion fol­low­ing a record $122 bil­lion fund­ing round. At the same time, the largest AI com­pa­nies are prepar­ing for pub­lic list­ings. According to CNBC, OpenAI may file for an ini­tial pub­lic of­fer­ing (IPO) within the com­ing weeks. Anthropic is also con­sid­er­ing a pub­lic stock of­fer­ing, al­though the ex­act tim­ing has not yet been dis­closed.

Earlier, Qazinform News Agency re­ported that Kazakhstan ranked among the coun­tries least con­cerned about job losses caused by ar­ti­fi­cial in­tel­li­gence, ac­cord­ing to the lat­est global sur­vey by the Gallup International Association.

To my amaze­ment, I found a fully solid rock that eerily re­sem­bles a seashell at the base of a cliff in the Alghat desert, Saudi Arabia. I did­n’t know what to make of it at first, it had the swirls and shape of a seashell but was fully a rock, more im­por­tantly, it should­n’t be here; the near­est coast­line is Dammam’s, 500 km away.

This looks im­pos­si­ble

Carbonate rocks (e.g. lime­stone), ma­rine fos­sils, coral fos­sils, and sed­i­men­tary struc­tures (like rip­ples or bio­tur­ba­tion) all ex­ist in and around Alghat, which points to the fact that parts of the Arabian Peninsula were once sub­merged un­der the sea. Specifically in the late Jurassic age (~150 mil­lion years ago)[1].

Stratigraphic dis­tri­b­u­tion fig­ure of ar­eas near Najd[1]

Nevertheless, I was still su­per cu­ri­ous about the fos­sil I found; what an­i­mal in­hab­ited it? what did it look like back in the Jurassic age? any mod­ern rel­a­tives or looka­likes?

The proper way of an­swer­ing these ques­tions is to con­duct a de­tailed analy­sis of the fos­sil (e.g. via in­spect­ing the sed­i­ment it was found in, its shape, etc.), this should be done by an ex­pert pa­le­on­tol­o­gist. However, I know no pa­le­on­tol­ogy, or any pa­le­on­tol­o­gist, so I fig­ured I could DIY it my­self (how hard could it be..?), though I’ll do it strictly via its shape — or what’s called its mor­phol­ogy. Morphology alone is prob­a­bly not ac­cu­rate enough to dis­cern lin­eage as dif­fer­ent species might looka­like but are from dif­fer­ent lin­eages, so this is prob­a­bly not the best way to do it, but it sounded fun and in­tu­itive, so I gave it a try.

Concretely, I plan on:

Mathematically rep­re­sent­ing the shape of a shell

Defining a dis­tance met­ric be­tween shapes (so that I can find shells sim­i­lar to the fos­sil’s)

Mapping out the space of shapes

7894 dif­fer­ent species and 59244 im­ages of shells were in the Zhang, et al. shell dataset[2]; good enough for me!

Capturing ‘shape’ is ac­tu­ally a very hard prob­lem; any ob­ject can be ro­tated by pitch, yaw, roll, scaled, and trans­lated. Before start­ing any sta­tis­ti­cal analy­sis, I fol­lowed a guide­line to iso­late the shape from other fac­tors

The shell must be cen­tered to the mid­point of the pic­ture

The scale of the shell must be equiv­a­lent across all im­ages (specifically, the max­i­mum dis­tance from the ori­gin is 1)

Orientation is the hard­est part

Pitch and yaw can be fixed by only choos­ing sam­ples where the shel­l’s open­ing is fac­ing the cam­era. This is not per­fect, but I found the dataset to be pretty con­sis­tent with its an­gles Roll is dif­fi­cult. A shell can be ro­tated in any way around the axis (even whilst the open­ing is fac­ing the cam­era). My fix was to use the longest ra­dius as the ref­er­ence point, and ro­tate the shell so that the longest ra­dius is al­ways on the right. This is not per­fect ei­ther, but it was good enough for me.

Pitch and yaw can be fixed by only choos­ing sam­ples where the shel­l’s open­ing is fac­ing the cam­era. This is not per­fect, but I found the dataset to be pretty con­sis­tent with its an­gles

Roll is dif­fi­cult. A shell can be ro­tated in any way around the axis (even whilst the open­ing is fac­ing the cam­era). My fix was to use the longest ra­dius as the ref­er­ence point, and ro­tate the shell so that the longest ra­dius is al­ways on the right. This is not per­fect ei­ther, but it was good enough for me.

Then, I ex­tracted the con­tour of the shell to 256 points rel­a­tive to the cen­ter. This way, each shell is rep­re­sented by a 256x2 ma­trix, where each row is the (x, y) co­or­di­nates of a point on the con­tour. Example:

> con­tours[0].shape

(256, 2)

> con­tours[0].tolist()[:5]

[-0.38561132550239563, 0.9804982542991638], [-0.4204626679420471, 0.9785506725311279], [-0.4553140103816986, 0.976603090763092], [-0.4901654124259949, 0.9746555089950562], [-0.5230183005332947, 0.9685550928115845]]

Normalization pipeline

Naturally, the dis­tance be­tween two shells s1 and s2 is squared eu­clid­ean dis­tance be­tween their con­tour points:

$$ d(s1, s2) = {\sum_{256} (s1.x_i - s2.x_i)^2 + (s1.y_i - s2.y_i)^2} $$

Representing the space will re­quire 256 di­men­sions, which is a lit­tle more than just the 2 I need to plot it over x and y. Given the nor­mal­ized shell con­tour above, it’s clear that many of these di­men­sions are re­dun­dant (for in­stance, the space of all pos­si­ble 256 con­tour points al­lows in­ter­sec­tion, while the space of pos­si­ble shells does­n’t, AFAIK), so the space of pos­si­ble shells can be con­densed into a smaller la­tent space. To drive my point home, I’ll show three ex­am­ples of fully ran­dom con­tours (i.e. pseudo-ran­dom points around the ori­gin).

Probably not a real shell

Dimensionality re­duc­tion tech­niques map the orig­i­nal 256 di­men­sions onto a smaller num­ber of di­men­sions (e.g. 2 or 3) while try­ing to pre­serve the dis­tance be­tween shells as much as pos­si­ble. One such tech­nique I’ll be us­ing is Principal Component Analysis (PCA). Here’s an ex­cel­lent frag­ment that ex­plains how PCA works: https://​stats.stack­ex­change.com/​ques­tions/​2691/​mak­ing-sense-of-prin­ci­pal-com­po­nent-analy­sis-eigen­vec­tors-eigen­val­ues/​140579#140579.

After ap­ply­ing PCA, I re­tained 56.50% of the vari­ance us­ing only the first prin­ci­pal com­po­nent (PC1), and 67.25% us­ing the first two. This means we can de­scribe a shel­l’s shape by only two num­bers, and be pretty close to the orig­i­nal shape!

The in­ter­est­ing part is try­ing to un­der­stand what these two num­bers mean; di­men­sion 1 in the orig­i­nal 256-dimensional space an­no­tates the lo­ca­tion of the first con­tour point of the shell, whereas di­men­sion 1 of the la­tent space an­no­tates a high-level fea­ture, learned by the PCA al­go­rithm. We can vi­su­ally try to un­der­stand what PCA di­men­sion PC1 rep­re­sents by find­ing two shells, di­a­met­ri­cally op­po­site in the PC1 di­men­sion, yet sim­i­lar in all other di­men­sions.

Essentially, we want to find two shells i and j such that the fol­low­ing score is max­i­mized:

$$ \text{score}(i,j) = \frac{|z_{i,1} - z_{j,1}|} {|\mathbf{z}_{i,2:k} - \mathbf{z}_{j,2:k}|_2} $$

PC1 seems to cap­ture the ‘pointiness’ of the shell, i.e. more than 50% of vari­ance in shell shapes can be ex­plained by how pointy they are. PC2 seems to cap­ture the sym­me­try of the shell, or per­haps the mass dis­tri­b­u­tion over the ver­ti­cal axis. I’ll leave the in­ter­pre­ta­tion of the other di­men­sions as an ex­er­cise for the reader (I have no idea).

And now for the grand fi­nale, we can plot the shells in the la­tent space, and see where our Alghat fos­sil fits in it. But first, for dra­matic ten­sion, I will dis­cuss the plot.

The plot rep­re­sents PC1 on the x-axis and PC2 on the y-axis, while color rep­re­sents the rough­ness of a shell (computed as the dif­fer­ence in slope be­tween con­sec­u­tive points). The fol­low­ing ob­ser­va­tions are worth not­ing:

Negative PC1 val­ues (representing round­ness) are way more com­mon than pos­i­tive PC1 val­ues (representing poin­ti­ness). Yet round­ness is less di­verse and oc­cu­pies less space than pointy shells

Pointy shells seem to be way more rough than round shells

Negative PC1 val­ues al­ways have PC2 val­ues close to zero; no shell in the dataset has a round but asym­met­ric shape. Below, I will pro­ject those shells back from la­tent space to the shape space, imag­in­ing im­pos­si­ble shells

Map of shell la­tent space with ex­am­ple shells

Modifying Principal Components against the mean shell

Projecting ‘impossible’ shells

So, what shell most closely re­sem­bles our Alghat fos­sil? It’s Sphincterochila can­didis­sima (try to pro­nounce it). However, it is re­ally young, nowhere near the Jurassic age; in­stead, the ear­li­est fos­sil of it dates back 38 mil­lion years ago[4]. Ultimately, shape is not the best way of de­ter­min­ing shell lin­eage, but its eerie sim­i­lar­ity to the Alghat fos­sil is still fas­ci­nat­ing, and per­haps points to some sort of con­ver­gent evo­lu­tion, where two dif­fer­ent species evolve to have sim­i­lar shapes due to sim­i­lar en­vi­ron­men­tal pres­sures.

Left: Alghat fos­sil com­pared, Right: Sphincterochila can­didis­sima[3]

Explore the tool

Feel free to ex­plore the tool and try to fig­ure out where a shell of your choice fits in the shell la­tent space!

https://​shell.hawzen.me

References

Aba Alkhayl, S. S. (2022). Marine macro-in­ver­te­brate fos­sils from the Lower Hanifa Formation (Hawtah Member), cen­tral Saudi Arabia. Arabian Journal of Geosciences, 15, 1410. https://​doi.org/​10.1007/​s12517 – 022-10581-w

Zhang, Q., Zhou, J., He, J. et al. A shell dataset, for shell fea­tures ex­trac­tion and recog­ni­tion. Sci Data 6, 226 (2019). https://​doi.org/​10.1038/​s41597 – 019-0230 – 3

https://​en.wikipedia.org/​wiki/​Sphinc­te­rochi­la_­can­didis­sima

Tracey, S., Todd, J. A., & Erwin, D. H. (1993). Mollusca: Gastropoda. In M. J. Benton (Ed.), The Fossil Record 2 (pp. 131 – 167). London: Chapman &

To my amaze­ment, I found a fully solid rock that eerily re­sem­bles a seashell at the base of a cliff in the Alghat desert, Saudi Arabia. I did­n’t know what to make of it at first, it had the swirls and shape of a seashell but was fully a rock, more im­por­tantly, it should­n’t be here; the near­est coast­line is Dammam’s, 500 km away.

This looks im­pos­si­ble

Carbonate rocks (e.g. lime­stone), ma­rine fos­sils, coral fos­sils, and sed­i­men­tary struc­tures (like rip­ples or bio­tur­ba­tion) all ex­ist in and around Alghat, which points to the fact that parts of the Arabian Peninsula were once sub­merged un­der the sea. Specifically in the late Jurassic age (~150 mil­lion years ago)[1].

Stratigraphic dis­tri­b­u­tion fig­ure of ar­eas near Najd[1]

Nevertheless, I was still su­per cu­ri­ous about the fos­sil I found; what an­i­mal in­hab­ited it? what did it look like back in the Jurassic age? any mod­ern rel­a­tives or looka­likes?

The proper way of an­swer­ing these ques­tions is to con­duct a de­tailed analy­sis of the fos­sil (e.g. via in­spect­ing the sed­i­ment it was found in, its shape, etc.), this should be done by an ex­pert pa­le­on­tol­o­gist. However, I know no pa­le­on­tol­ogy, or any pa­le­on­tol­o­gist, so I fig­ured I could DIY it my­self (how hard could it be..?), though I’ll do it strictly via its shape — or what’s called its mor­phol­ogy. Morphology alone is prob­a­bly not ac­cu­rate enough to dis­cern lin­eage as dif­fer­ent species might looka­like but are from dif­fer­ent lin­eages, so this is prob­a­bly not the best way to do it, but it sounded fun and in­tu­itive, so I gave it a try.

Concretely, I plan on:

Mathematically rep­re­sent­ing the shape of a shell

Defining a dis­tance met­ric be­tween shapes (so that I can find shells sim­i­lar to the fos­sil’s)

Mapping out the space of shapes

7894 dif­fer­ent species and 59244 im­ages of shells were in the Zhang, et al. shell dataset[2]; good enough for me!

Capturing ‘shape’ is ac­tu­ally a very hard prob­lem; any ob­ject can be ro­tated by pitch, yaw, roll, scaled, and trans­lated. Before start­ing any sta­tis­ti­cal analy­sis, I fol­lowed a guide­line to iso­late the shape from other fac­tors

The shell must be cen­tered to the mid­point of the pic­ture

The scale of the shell must be equiv­a­lent across all im­ages (specifically, the max­i­mum dis­tance from the ori­gin is 1)

Orientation is the hard­est part

Pitch and yaw can be fixed by only choos­ing sam­ples where the shel­l’s open­ing is fac­ing the cam­era. This is not per­fect, but I found the dataset to be pretty con­sis­tent with its an­gles Roll is dif­fi­cult. A shell can be ro­tated in any way around the axis (even whilst the open­ing is fac­ing the cam­era). My fix was to use the longest ra­dius as the ref­er­ence point, and ro­tate the shell so that the longest ra­dius is al­ways on the right. This is not per­fect ei­ther, but it was good enough for me.

Pitch and yaw can be fixed by only choos­ing sam­ples where the shel­l’s open­ing is fac­ing the cam­era. This is not per­fect, but I found the dataset to be pretty con­sis­tent with its an­gles

Roll is dif­fi­cult. A shell can be ro­tated in any way around the axis (even whilst the open­ing is fac­ing the cam­era). My fix was to use the longest ra­dius as the ref­er­ence point, and ro­tate the shell so that the longest ra­dius is al­ways on the right. This is not per­fect ei­ther, but it was good enough for me.

Then, I ex­tracted the con­tour of the shell to 256 points rel­a­tive to the cen­ter. This way, each shell is rep­re­sented by a 256x2 ma­trix, where each row is the (x, y) co­or­di­nates of a point on the con­tour. Example:

> con­tours[0].shape

(256, 2)

> con­tours[0].tolist()[:5]

[-0.38561132550239563, 0.9804982542991638], [-0.4204626679420471, 0.9785506725311279], [-0.4553140103816986, 0.976603090763092], [-0.4901654124259949, 0.9746555089950562], [-0.5230183005332947, 0.9685550928115845]]

Normalization pipeline

Naturally, the dis­tance be­tween two shells s1 and s2 is squared eu­clid­ean dis­tance be­tween their con­tour points:

$$ d(s1, s2) = {\sum_{256} (s1.x_i - s2.x_i)^2 + (s1.y_i - s2.y_i)^2} $$

Representing the space will re­quire 256 di­men­sions, which is a lit­tle more than just the 2 I need to plot it over x and y. Given the nor­mal­ized shell con­tour above, it’s clear that many of these di­men­sions are re­dun­dant (for in­stance, the space of all pos­si­ble 256 con­tour points al­lows in­ter­sec­tion, while the space of pos­si­ble shells does­n’t, AFAIK), so the space of pos­si­ble shells can be con­densed into a smaller la­tent space. To drive my point home, I’ll show three ex­am­ples of fully ran­dom con­tours (i.e. pseudo-ran­dom points around the ori­gin).

Probably not a real shell

Dimensionality re­duc­tion tech­niques map the orig­i­nal 256 di­men­sions onto a smaller num­ber of di­men­sions (e.g. 2 or 3) while try­ing to pre­serve the dis­tance be­tween shells as much as pos­si­ble. One such tech­nique I’ll be us­ing is Principal Component Analysis (PCA). Here’s an ex­cel­lent frag­ment that ex­plains how PCA works: https://​stats.stack­ex­change.com/​ques­tions/​2691/​mak­ing-sense-of-prin­ci­pal-com­po­nent-analy­sis-eigen­vec­tors-eigen­val­ues/​140579#140579.

After ap­ply­ing PCA, I re­tained 56.50% of the vari­ance us­ing only the first prin­ci­pal com­po­nent (PC1), and 67.25% us­ing the first two. This means we can de­scribe a shel­l’s shape by only two num­bers, and be pretty close to the orig­i­nal shape!

The in­ter­est­ing part is try­ing to un­der­stand what these two num­bers mean; di­men­sion 1 in the orig­i­nal 256-dimensional space an­no­tates the lo­ca­tion of the first con­tour point of the shell, whereas di­men­sion 1 of the la­tent space an­no­tates a high-level fea­ture, learned by the PCA al­go­rithm. We can vi­su­ally try to un­der­stand what PCA di­men­sion PC1 rep­re­sents by find­ing two shells, di­a­met­ri­cally op­po­site in the PC1 di­men­sion, yet sim­i­lar in all other di­men­sions.

Essentially, we want to find two shells i and j such that the fol­low­ing score is max­i­mized:

$$ \text{score}(i,j) = \frac{|z_{i,1} - z_{j,1}|} {|\mathbf{z}_{i,2:k} - \mathbf{z}_{j,2:k}|_2} $$

PC1 seems to cap­ture the ‘pointiness’ of the shell, i.e. more than 50% of vari­ance in shell shapes can be ex­plained by how pointy they are. PC2 seems to cap­ture the sym­me­try of the shell, or per­haps the mass dis­tri­b­u­tion over the ver­ti­cal axis. I’ll leave the in­ter­pre­ta­tion of the other di­men­sions as an ex­er­cise for the reader (I have no idea).

And now for the grand fi­nale, we can plot the shells in the la­tent space, and see where our Alghat fos­sil fits in it. But first, for dra­matic ten­sion, I will dis­cuss the plot.

The plot rep­re­sents PC1 on the x-axis and PC2 on the y-axis, while color rep­re­sents the rough­ness of a shell (computed as the dif­fer­ence in slope be­tween con­sec­u­tive points). The fol­low­ing ob­ser­va­tions are worth not­ing:

Negative PC1 val­ues (representing round­ness) are way more com­mon than pos­i­tive PC1 val­ues (representing poin­ti­ness). Yet round­ness is less di­verse and oc­cu­pies less space than pointy shells

Pointy shells seem to be way more rough than round shells

Negative PC1 val­ues al­ways have PC2 val­ues close to zero; no shell in the dataset has a round but asym­met­ric shape. Below, I will pro­ject those shells back from la­tent space to the shape space, imag­in­ing im­pos­si­ble shells

Map of shell la­tent space with ex­am­ple shells

Modifying Principal Components against the mean shell

Projecting ‘impossible’ shells

So, what shell most closely re­sem­bles our Alghat fos­sil? It’s Sphincterochila can­didis­sima (try to pro­nounce it). However, it is re­ally young, nowhere near the Jurassic age; in­stead, the ear­li­est fos­sil of it dates back 38 mil­lion years ago[4]. Ultimately, shape is not the best way of de­ter­min­ing shell lin­eage, but its eerie sim­i­lar­ity to the Alghat fos­sil is still fas­ci­nat­ing, and per­haps points to some sort of con­ver­gent evo­lu­tion, where two dif­fer­ent species evolve to have sim­i­lar shapes due to sim­i­lar en­vi­ron­men­tal pres­sures.

Left: Alghat fos­sil com­pared, Right: Sphincterochila can­didis­sima[3]

Explore the tool

Feel free to ex­plore the tool and try to fig­ure out where a shell of your choice fits in the shell la­tent space!

https://​shell.hawzen.me

References

Aba Alkhayl, S. S. (2022). Marine macro-in­ver­te­brate fos­sils from the Lower Hanifa Formation (Hawtah Member), cen­tral Saudi Arabia. Arabian Journal of Geosciences, 15, 1410. https://​doi.org/​10.1007/​s12517 – 022-10581-w

Zhang, Q., Zhou, J., He, J. et al. A shell dataset, for shell fea­tures ex­trac­tion and recog­ni­tion. Sci Data 6, 226 (2019). https://​doi.org/​10.1038/​s41597 – 019-0230 – 3

https://​en.wikipedia.org/​wiki/​Sphinc­te­rochi­la_­can­didis­sima

Tracey, S., Todd, J. A., & Erwin, D. H. (1993). Mollusca: Gastropoda. In M. J. Benton (Ed.), The Fossil Record 2 (pp. 131 – 167). London: Chapman &

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GPTZeroInvestigations·

Exclusive

Chasing the Hallucinations

Ernst & Young (EY) Canada pub­lished a cy­ber­se­cu­rity re­port on loy­alty pro­gram safe­guards. We chased down every ci­ta­tion. Most were hal­lu­ci­nated.

View Investigation

MAY 14, 2026

Earlier this year, an en­gi­neer at GPTZero coined the term “vibe cit­ing” to de­scribe the ac­ci­den­tal cre­ation of fake ref­er­ences via LLM hal­lu­ci­na­tions. It turns out that the fric­tion of cre­at­ing and check­ing ci­ta­tions is lead­ing many re­searchers, con­sul­tants, lawyers, and pub­lic of­fi­cials to em­brace the vibe (if you know what we mean).

Among the con­verts are the au­thors of a 2025 Ernst & Young re­port ti­tled Points of Attack: Uncovering Cyber Threats and Fraud in Loyalty Systems. This re­port, stuffed with fake ci­ta­tions and in­ac­cu­rate claims, is sur­fac­ing in news­pa­pers, blog posts, and AI search overviews, poi­son­ing the data that both hu­man re­searchers and AI agents rely on.

GPTZero be­gan tar­get­ing vibe ci­ta­tions with our Hallucination Check tool in 2025, which we used to fur­ther in­ves­ti­ga­tions into a gov­ern­ment pub­li­ca­tion, two dif­fer­ent Deloitte re­ports, and pres­ti­gious ma­chine learn­ing / ar­ti­fi­cial in­tel­li­gence con­fer­ences like NeurIPS and ICLR. Over the past few months we’ve set up an au­to­mated pipeline to search for vibe ci­ta­tions by find­ing and scan­ning pub­lic re­ports from ma­jor con­sult­ing firms. What we’ve found sug­gests that the vibe cit­ing epi­demic is al­ready en­demic, even among the ma­jor play­ers.

Instead of re­leas­ing our re­sults all at once, we’re go­ing to fo­cus on one re­port at a time. This ap­proach both pre­vents in­di­vid­ual ex­am­ples be­ing over­looked and al­lows us to il­lus­trate the neg­a­tive im­pacts of vibe cit­ing on re­search qual­ity and pub­lic trust.

On the menu: Ernst & Young (EY)

Ernst & Young is one of the “big four” global con­sult­ing firms, pro­vid­ing ac­count­ing and con­sult­ing ser­vices to gov­ern­ments and pri­vate en­ti­ties from 150 of­fices around the world. The Canadian mem­ber firm (EY Canada) pro­vides mil­lions of dol­lars of ser­vices to the Canadian gov­ern­ment an­nu­ally.

In late 2025, EY Canada pub­lished a 44-page re­port on cy­ber se­cu­rity ti­tled Points of Attack: Uncovering Cyber Threats and Fraud in Loyalty Systems. While cred­ited to three em­ploy­ees (two part­ners and one se­nior man­ager), the doc­u­ment is a col­lage of vibe ci­ta­tions, mis­at­tri­bu­tions, fake sta­tis­tics, and AI-written text.

Why the Vibes Are Bad

EY Canada’s re­port does­n’t use foot­notes or nor­mal aca­d­e­mic ci­ta­tions. Instead, it ref­er­ences sources di­rectly in the text and/​or in­cludes them in a re­sources table (p. 41 – 43). This table pro­vides a source ti­tle, de­scrip­tion, and URL for all sources, as well as the pub­lisher and date in cer­tain cases. Almost all of the URLs are bro­ken or fake, and more than half of the ti­tles don’t cor­re­spond to real sources.

GPTZero uses a very spe­cific de­f­i­n­i­tion of be­cause of the po­ten­tial rep­u­ta­tional cost (to both us and the re­port’s au­thors) of false pos­i­tives. One of our team mem­bers man­u­ally ver­i­fied Hallucination Check’s re­sults to en­sure their ac­cu­racy.

During our pre­vi­ous analy­sis of aca­d­e­mic con­fer­ence sub­mis­sions, we found that many au­thors pri­mar­ily used AI to gen­er­ate and for­mat their ref­er­ences, re­sult­ing in pa­pers with vibed ci­ta­tions but low AI text scores over­all.

However, it’s hard to find hu­man fin­ger­prints in Points of Attack — harder, even, than find­ing a hu­man-writ­ten LinkedIn post. Not only does the text scan as AI-generated, it’s rid­dled with com­mon LLM er­rors like fake sta­tis­tics, mis­at­tri­bu­tions, and in­ter­nal con­tra­dic­tions.

1/4

EY Report, Page 4

A bold claim in the ex­ec­u­tive sum­mary

In the re­port’s Executive Summary, its au­thors claim the global loy­alty points mar­ket is $200 bil­lion, and that 30 – 50% of those points go un­used.

EY Report, Page 42

A fake Forbes ci­ta­tion

The ci­ta­tion we just looked at sup­ports the au­thor’s orig­i­nal claim of a $200 bil­lion global mar­ket.

EY Report, Page 10

A con­tra­dic­tory claim

Yet on page 10, the $200 bil­lion fig­ure is now the es­ti­mate of unre­deemed loy­alty points, not the col­lec­tive value of all points glob­ally. Since the au­thors have al­ready claimed that up to 50% of points are unre­deemed, this new sta­tis­tic re­quires a global mar­ket value of at least $400 bil­lion.

EY Report, Page 43

A sec­ond fab­ri­cated ci­ta­tion: McKinsey

A few rows down, a fab­ri­cated McKinsey & Company re­port pro­vides ev­i­dence for the lat­ter claim — $200 bil­lion as the value of unre­deemed points glob­ally. Two in­vented ci­ta­tions, two in­com­pat­i­ble num­bers.

We chased the source of this McKinsey ci­ta­tion back to an ob­scure fin­tech

blog­post

by Financial IT, which was pub­lished six months ear­lier.

1/2

Financial IT, Page 1

A sim­i­lar claim

Six months be­fore EY’s re­port, a blog post on the ob­scure U.K. fin­tech mag­a­zine Financial IT claims that “more than $200 bil­lion in points sit idle each year.” The lan­guage is nearly iden­ti­cal to the EY re­port.

Financial IT, Page 3

The vibes are iden­ti­cal

The blog’s sources sec­tion cites “McKinsey & Company: Loyalty Economics Report (2022)” — a re­port that does not ex­ist. This fab­ri­cated ci­ta­tion ap­pears ver­ba­tim in the EY re­port’s ref­er­ence table, laun­der­ing an in­vented source from a low-qual­ity blog into a Big Four pub­li­ca­tion.

Some of the re­port’s most du­bi­ous claims weren’t even cited at all.

1/2

EY Report, Page 6

The source is at­trib­uted to Paystone

On page 6, the au­thors claim that 72% of cus­tomer loy­alty pro­grams have re­ported theft or fraud. This fact is at­trib­uted to a 2019 post by the Canadian pay­ment proces­sor Paystone.

EY Report, Page 11

Actually, the source is Forter

However, on page 11, the same sta­tis­tic is at­trib­uted to a dif­fer­ent source — the un­usu­ally-named “NRF 2020 sum­mary” pub­lished by the dig­i­tal fraud pre­ven­tion com­pany Forter. Neither of these sources are in­cluded in the re­port’s ref­er­ence table. In fact, while the sta­tis­tic is ref­er­enced on both the Paystone and Forter pages, the orig­i­nal source seems to be a 2017 sur­vey by Ipsos.

Contradicting ref­er­ences, low-qual­ity sources, and out-of-date sta­tis­tics are all in­di­ca­tions of AI slop.

1/2

EY Report, Page 6

The 89% claim

On page 6, the au­thors claim that loy­alty pro­gram fraud at­tacks have in­creased 89% since 2019.

EY Report, Page 11

A spe­cific source for this claim

Yet on page 11, this 89% in­crease is lim­ited to a sin­gle year, 2018 to 2019, and the sta­tis­tic is at­trib­uted to a spe­cific source: the Forter Fraud Attack Index. Surprisingly, this source both ex­ists and par­tially con­firms the sec­ond ver­sion of the claim. However, like many of the sources used in the EY re­port, it is sub­stan­tially out of date. Poorly para­phrased sta­tis­tics are also a sign of AI slop.

Why Vibes Matter

It’s dif­fi­cult to mea­sure the pub­lic im­pact of EY’s re­port. Points of Attack seems to have made few waves in Canada; how­ever, it was re­cently ref­er­enced in a Canberra Times ar­ti­cle that was syn­di­cated to more than 60 news­pa­pers across Australia. It may also have cir­cu­lated through client brief­ings, in­ter­nal decks, and other pro­pri­etary me­dia that aren’t in the pub­lic do­main. Yet vibe ci­ta­tions don’t just de­ceive read­ers or cor­po­rate au­di­ences — they also have an­other, more in­sid­i­ous, im­pact.

Publishing a re­port on­line is es­sen­tially a form of data in­jec­tion into the pool of knowl­edge that is the in­ter­net. When the re­port in­cludes fake in­for­ma­tion (either vibed ci­ta­tions or false claims) it can “poison the well” by mis­lead­ing fu­ture re­searchers, es­pe­cially if the re­port is pub­lished by a well-known con­sult­ing firm and hosted on a high-traf­fic web­site.

This risk has been ag­gra­vated by the emer­gence of AI “deep re­search” tools which rely on dif­fer­ent sig­nals than hu­mans when choos­ing sources and are there­fore more vul­ner­a­ble to data poi­son­ing.

Conclusion

GPTZero is Chasing the Vibe (Citations)

Our re­search over the past few months proves that vibe cit­ing is a clear and pre­sent dan­ger to re­searchers, aca­d­e­mics, con­sul­tants, and (frankly) any­one who drinks from the dig­i­tal pool by search­ing the web. Our Hallucination Check tool is our an­swer to this threat: a way to iden­tify vibe ci­ta­tions and hal­lu­ci­na­tions with­out man­u­ally check­ing every ci­ta­tion. It is al­ready be­ing used to screen sub­mis­sions by elite aca­d­e­mic con­fer­ences like IJCAI, ICLR, and ICSE.

Now, more than ever, it’s crazy to ac­cept ci­ta­tions on faith — even those from a rep­utable source like Ernst & Young.

Try GPTZero’s Hallucination Check for your­self, or reach out to GPTZero’s team.

Written by Om Ogale

Emma Roth

is a news writer who cov­ers the stream­ing wars, con­sumer tech, crypto, so­cial me­dia, and much more. Previously, she was a writer and ed­i­tor at MUO.

Downdetector and Speedtest — the free plat­forms that al­low peo­ple on the web to quickly check in­ter­net speeds or see if an on­line plat­form may be down — will soon have a new owner. On Tuesday, the con­sult­ing and IT ser­vices provider Accenture an­nounced that it has agreed to ac­quire the Ookla-owned plat­forms from Ziff Davis for $1.2 bil­lion, as re­ported ear­lier by Reuters.

In the press re­lease, Accenture CEO Julie Sweet says the com­pany will use Ookla’s prod­ucts to cap­ture data that will help “clients across busi­ness and gov­ern­ment scale AI safely.” Ziff Davis, which owns CNET, IGN, and Eurogamer, ac­quired Ookla in 2014. Ookla’s other prod­ucts in­clude Ekahau, which makes soft­ware for net­work de­sign and trou­bleshoot­ing, along with RootMetrics, a plat­form that mea­sures mo­bile net­work speeds.

Following the ac­qui­si­tion, which is still sub­ject to reg­u­la­tory ap­proval, Ookla’s data will be used to as­sist cloud ser­vice providers and AI hy­per­scalers, though Accenture tells Ars Technica it will con­tinue to run the Ookla “business as it op­er­ates to­day.”

“Joining Accenture will al­low us to scale our pre­miere net­work data busi­ness across the world’s largest en­ter­prises and ac­cel­er­ate our goal of cre­at­ing bet­ter con­nected ex­pe­ri­ences,” Ookla CEO Stephen Bye says in a state­ment.

Follow top­ics and au­thors from this story to see more like this in your per­son­al­ized home­page feed and to re­ceive email up­dates.

Emma Roth

Network data is no longer just a life­line for the tele­coms in­dus­try; it now cre­ates sig­nif­i­cant value across all sec­tors. As AI scales, the in­sights cap­tured at the net­work, de­vice, and ap­pli­ca­tion lay­ers are es­sen­tial to en­hance fraud pre­ven­tion in bank­ing, smart home an­a­lyt­ics in util­i­ties, and traf­fic op­ti­miza­tion in re­tail. Ookla’s plat­form, which cap­tures more than 1,000 at­trib­utes per test, pro­vides the foun­da­tion for these in­sights.

“Modern net­works have evolved from sim­ple in­fra­struc­ture into busi­ness-crit­i­cal plat­forms,” said Julie Sweet, chair and CEO, Accenture. “Without the abil­ity to mea­sure per­for­mance, or­ga­ni­za­tions can­not op­ti­mize ex­pe­ri­ence, rev­enue, or se­cu­rity. By ac­quir­ing Ookla, we will help our clients across busi­ness and gov­ern­ment scale AI safely and build the trusted data foun­da­tions they need to de­liver the re­li­able, seam­less con­nec­tiv­ity that cre­ates value.”

Headquartered in Seattle, Ookla op­er­ates a port­fo­lio of glob­ally rec­og­nized brands in con­nec­tiv­ity. This deep tech­ni­cal vis­i­bil­ity is es­sen­tial for:

CSPs: Autonomous net­works en­hance bench­mark­ing and cap­i­tal plan­ning by lever­ag­ing real-time data, pre­dic­tive sim­u­la­tions, and AI-driven in­sights to op­ti­mize in­fra­struc­ture in­vest­ments and sig­nif­i­cantly re­duce op­er­a­tional costs.

Hyperscalers and Cloud Providers: To en­sure the re­silience of AI in­fra­struc­ture and edge data cen­ters which de­liver most of the in­fer­ence work­loads.

Enterprises: To de­sign and trou­bleshoot mis­sion-crit­i­cal pri­vate 5G and Wi-Fi net­works us­ing Ekahau’s spe­cial­ized hard­ware and soft­ware.

“With the Ookla port­fo­lio, we will of­fer end-to-end net­work in­tel­li­gence ser­vices es­sen­tial for AI-based trans­for­ma­tion,” said Manish Sharma, chief strat­egy and ser­vices of­fi­cer, Accenture. “Speedtest and RootMetrics de­fine the ex­pe­ri­ence; Downdetector iden­ti­fies in­ci­dents faster; and Ekahau dri­ves dig­i­tal work­place trans­for­ma­tion through su­pe­rior Wi-Fi. In an era of omni-chan­nel and agen­tic ac­cess, low-la­tency, zero-fric­tion con­nec­tiv­ity is a com­pet­i­tive ne­ces­sity, and these tools give en­ter­prises the power to build the high-per­for­mance en­vi­ron­ments they need.”

Founded in 2006 and a di­vi­sion of Ziff Davis, Inc., Ookla’s team of ap­prox­i­mately 430 ex­perts spe­cial­izes in soft­ware en­gi­neer­ing, ra­dio fre­quency en­gi­neer­ing and data sci­ence. Ookla’s data plat­form is an­chored by more than 250 mil­lion con­sumer-ini­ti­ated tests per month, com­ple­mented by con­trolled drive, walk, and em­bed­ded test­ing op­tions. Together, these el­e­ments de­liver a rich and re­silient com­bi­na­tion of qual­ity of ser­vice (QoS), ra­dio fre­quency (RF) sig­nal data, and qual­ity of ex­pe­ri­ence (QoE) in­sights that an­swer more con­nec­tiv­ity ques­tions and drive bet­ter busi­ness out­comes.

“Joining Accenture will al­low us to scale our pre­miere net­work data busi­ness across the world’s largest en­ter­prises and ac­cel­er­ate our goal of cre­at­ing bet­ter con­nected ex­pe­ri­ences,” said Stephen Bye, CEO, Ookla. “Our com­bined ca­pa­bil­i­ties will en­able us to more ef­fec­tively serve CSPs, AI in­fra­struc­ture providers, edge data cen­ters and en­ter­prise net­works. Together, we will re­de­fine how the world mea­sures, un­der­stands and ex­pe­ri­ences con­nec­tiv­ity.”

The ac­qui­si­tion is sub­ject to cus­tom­ary clos­ing con­di­tions, in­clud­ing the re­ceipt of re­quired reg­u­la­tory ap­provals. Terms of the trans­ac­tion will not be dis­closed by Accenture.

About AccentureAccenture is a lead­ing so­lu­tions and ser­vices com­pany that helps the world’s lead­ing en­ter­prises rein­vent by build­ing their dig­i­tal core and un­leash­ing the power of AI to cre­ate value at speed across the en­ter­prise, bring­ing to­gether the tal­ent of our ap­prox­i­mately 784,000 peo­ple, our pro­pri­etary as­sets and plat­forms, and deep ecosys­tem re­la­tion­ships. Our strat­egy is to be the rein­ven­tion part­ner of choice for our clients and to be the most client-fo­cused, AI-enabled, great place to work in the world. Through our Reinvention Services we bring to­gether our ca­pa­bil­i­ties across strat­egy, con­sult­ing, tech­nol­ogy, op­er­a­tions, Song and Industry X with our deep in­dus­try ex­per­tise to cre­ate and de­liver so­lu­tions and ser­vices for our clients. Our pur­pose is to de­liver on the promise of tech­nol­ogy and hu­man in­ge­nu­ity, and we mea­sure our suc­cess by the 360° value we cre­ate for all our stake­hold­ers. Visit us at ac­cen­ture.com.

Forward-Looking StatementsExcept for the his­tor­i­cal in­for­ma­tion and dis­cus­sions con­tained herein, state­ments in this news re­lease may con­sti­tute for­ward-look­ing state­ments within the mean­ing of the Private Securities Litigation Reform Act of 1995. Words such as “may,” “will,” “should,” “likely,” “anticipates,” “aspires,” “expects,” “intends,” “plans,” “projects,” “believes,” “estimates,” “positioned,” “outlook,” “goal,” “target” and sim­i­lar ex­pres­sions are used to iden­tify these for­ward-look­ing state­ments. These state­ments are not guar­an­tees of fu­ture per­for­mance nor promises that goals or tar­gets will be met, and in­volve a num­ber of risks, un­cer­tain­ties and other fac­tors that are dif­fi­cult to pre­dict and could cause ac­tual re­sults to dif­fer ma­te­ri­ally from those ex­pressed or im­plied. These risks in­clude, with­out lim­i­ta­tion, risks that: Accenture and Ookla will not be able to close the trans­ac­tion in the time pe­riod an­tic­i­pated, or at all, which is de­pen­dent on the par­ties’ abil­ity to sat­isfy cer­tain clos­ing con­di­tions; the trans­ac­tion might not achieve the an­tic­i­pated ben­e­fits for Accenture; Accenture’s re­sults of op­er­a­tions have been, and may in the fu­ture be, ad­versely af­fected by volatile, neg­a­tive or un­cer­tain eco­nomic and geopo­lit­i­cal con­di­tions and the ef­fects of these con­di­tions on the com­pa­ny’s clients’ busi­nesses and lev­els of busi­ness ac­tiv­ity; Accenture’s busi­ness de­pends on gen­er­at­ing and main­tain­ing client de­mand for the com­pa­ny’s ser­vices and so­lu­tions in­clud­ing through the adap­ta­tion and ex­pan­sion of its ser­vices and so­lu­tions in re­sponse to on­go­ing changes in tech­nol­ogy and of­fer­ings, and a sig­nif­i­cant re­duc­tion in such de­mand or an in­abil­ity to re­spond to the evolv­ing tech­no­log­i­cal en­vi­ron­ment could ma­te­ri­ally af­fect the com­pa­ny’s re­sults of op­er­a­tions; risks and un­cer­tain­ties re­lated to the de­vel­op­ment and use of AI could harm the com­pa­ny’s busi­ness, dam­age its rep­u­ta­tion or give rise to le­gal or reg­u­la­tory ac­tion; if Accenture is un­able to match peo­ple and their skills with client de­mand around the world and at­tract and re­tain pro­fes­sion­als with strong lead­er­ship skills, the com­pa­ny’s busi­ness, the uti­liza­tion rate of the com­pa­ny’s pro­fes­sion­als and the com­pa­ny’s re­sults of op­er­a­tions may be ma­te­ri­ally ad­versely af­fected; Accenture faces le­gal, rep­u­ta­tional and fi­nan­cial risks from any fail­ure to pro­tect client and/​or com­pany data from se­cu­rity in­ci­dents or cy­ber­at­tacks; the mar­kets in which Accenture op­er­ates are highly com­pet­i­tive, and Accenture might not be able to com­pete ef­fec­tively; Accenture’s abil­ity to at­tract and re­tain busi­ness and em­ploy­ees may de­pend on its rep­u­ta­tion in the mar­ket­place; if Accenture does not suc­cess­fully man­age and de­velop its re­la­tion­ships with key ecosys­tem part­ners or fails to an­tic­i­pate and es­tab­lish new al­liances in new tech­nolo­gies, the com­pa­ny’s re­sults of op­er­a­tions could be ad­versely af­fected; Accenture’s prof­itabil­ity could ma­te­ri­ally suf­fer due to pric­ing pres­sure, if the com­pany is un­able to re­main com­pet­i­tive, if its cost-man­age­ment strate­gies are un­suc­cess­ful or if it ex­pe­ri­ences de­liv­ery in­ef­fi­cien­cies or fail to sat­isfy cer­tain agreed-upon tar­gets or spe­cific ser­vice lev­els; changes in Accenture’s level of taxes, as well as au­dits, in­ves­ti­ga­tions and tax pro­ceed­ings, or changes in tax laws or in their in­ter­pre­ta­tion or en­force­ment, could have a ma­te­r­ial ad­verse ef­fect on the com­pa­ny’s ef­fec­tive tax rate, re­sults of op­er­a­tions, cash flows and fi­nan­cial con­di­tion; Accenture’s re­sults of op­er­a­tions could be ma­te­ri­ally ad­versely af­fected by fluc­tu­a­tions in for­eign cur­rency ex­change rates; Accenture’s debt oblig­a­tions could ad­versely af­fect its busi­ness and fi­nan­cial con­di­tion; changes to ac­count­ing stan­dards or in the es­ti­mates and as­sump­tions Accenture makes in con­nec­tion with the prepa­ra­tion of its con­sol­i­dated fi­nan­cial state­ments could ad­versely af­fect its fi­nan­cial re­sults; as a re­sult of Accenture’s ge­o­graph­i­cally di­verse op­er­a­tions and strat­egy to con­tinue to grow in key mar­kets around the world, the com­pany is more sus­cep­ti­ble to cer­tain risks; if Accenture is un­able to man­age the or­ga­ni­za­tional chal­lenges as­so­ci­ated with its size, the com­pany might be un­able to achieve its busi­ness ob­jec­tives; Accenture might not be suc­cess­ful at ac­quir­ing, in­vest­ing in or in­te­grat­ing busi­nesses, en­ter­ing into joint ven­tures or di­vest­ing busi­nesses; Accenture’s busi­ness could be ma­te­ri­ally ad­versely af­fected if the com­pany in­curs le­gal li­a­bil­ity; Accenture’s work with gov­ern­ment clients ex­poses the com­pany to ad­di­tional risks in­her­ent in the gov­ern­ment con­tract­ing en­vi­ron­ment; Accenture’s global op­er­a­tions ex­pose the com­pany to nu­mer­ous and some­times con­flict­ing le­gal and reg­u­la­tory re­quire­ments; if Accenture is un­able to pro­tect or en­force its in­tel­lec­tual prop­erty rights or if Accenture’s ser­vices or so­lu­tions in­fringe upon the in­tel­lec­tual prop­erty rights of oth­ers or the com­pany loses its abil­ity to uti­lize the in­tel­lec­tual prop­erty of oth­ers, its busi­ness could be ad­versely af­fected; Accenture may be sub­ject to crit­i­cism and neg­a­tive pub­lic­ity re­lated to its in­cor­po­ra­tion in Ireland; as well as the risks, un­cer­tain­ties and other fac­tors dis­cussed un­der the “Risk Factors” head­ing in Accenture plc’s most re­cent Annual Report on Form 10-K, as up­dated in Item 1A, “Risk Factors” in its Quarterly Report on Form 10-Q for the sec­ond quar­ter of fis­cal 2025, and other doc­u­ments filed with or fur­nished to the Securities and Exchange Commission. Statements in this news re­lease speak only as of the date they were made, and Accenture un­der­takes no duty to up­date any for­ward-look­ing state­ments made in this news re­lease or to con­form such state­ments to ac­tual re­sults or changes in Accenture’s ex­pec­ta­tions.

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