Category Archives: SaaS

Primary ProcureTech Concern: (Gen-)AI Integration/Impact

The non-stop hype coming straight from the A.S.S.H.O.L.E. is continuing to cause market confusion and utter chaos.

Why?

Gen-AI is on the concerns list because it’s the tech-du-jour. Five years ago it was (advanced) (predictive) analytics. Ten years ago it was the fluffy magic cloud. Fifteen years ago it was SaaS. Twenty years ago it was the World Wide Web. And so on.

But not one of these technologies, all sold as the panacea that would solve all your woes, solved your problems because all of the promised capabilities were just silicon snake oil, and Gen-AI is no different. The hype cycle may be slowly coming to an end, but it will quickly be replaced by Some-BS-World-Model-Adjacent-Agentic-AGI that will be sold as the AI that finally solves all your problems but, in reality, still won’t be anything close (but, if narrowly applied in the right domains where the client has sufficient data might actually work quite well … but won’t do anything reliably in general and the failure rate will still be 80%+, which is the average tech failure rate for the last 25 years … and SI knows, because the doctor has been following tech failure for over 25 years).

Not only is Gen-AI no different than the previously over-hyped tech-du-jour offerings of the last two decades, but with a failure rate of 94%+ (McKinsey, and 95%, MIT), it’s arguably the worst yet! And, as per our predictions, it’s not going to get much better. If the failure rate gets as low as 90% this year, it will be the closest thing to a tech miracle that we can conceivably get. Like every other tech before, Gen-AI will only solve a relatively small set of problems.

Just like

  • The Web only solves remote connectivity
  • SaaS only allows solutions to be built in the cloud
  • Analytics only provides insight where you have the right, sufficient, data and the right algorithms to get useful insights
  • Gen-AI is just a next-gen probabilistic deep neural net that often does
    • better semantic processing
    • better search
    • better summarization
    • better potential pattern identification (but only if you can learn how to prompt it to do so and only if you have it trained on the right data subsets, not the entire web which is now more than half AI slop)

    but does so at the additional expense of

    • hallucinations
    • intentional falsehoods
    • thoughtless reinforcement
    • cognitive atrophy
    • etc. etc. etc.

As a result of this, as far as I’m concerned, the AI bubble can’t burst fast enough! It’s all hype, buzzwords, and hallucinatory bullcr@p. And, frankly, any (claims of) agentic AI built on it are fraudulent. (After all, we’ve already seen what happens when you let AI run your vending machine. The last thing you want is it buying for you!)

Especially when, on top of hallucinations, we have plenty of examples of:

We’ve said many times that LLMs are not helpful and ChatGPT (in particular) is not your friend, that if you have a headache you definitely shouldn’t take an aspirin or query an LLM, and that, frankly, you’d be better off with a drunken plagiarist intern because that’s the best case result from an LLM. Most are worse.

Frankly, it’s time to stop falling for the artificial intimidation, fight back against AI Slop, and remember cutting edge tech is NOT defined by the C-Suite or the incessant marketing from the A.S.S.H.O.L.E. that is targeting the C-Suite on a daily basis!

Impact Potential

Huge! Companies will continue to waste millions individually and collectively hundreds of billions on the next generation tech that, with a probability of 90%+, will generate a (huge) loss.

Major Challenges/Risks

The major challenge is not with the tech, it’s helping companies realize that they’re probably not ready for the tech. The reason that tech failure rate has averaged 80%+ over the last twenty years is that consultancies keep promoting, vendors keep selling, and companies keep buying advanced leading edge tech they are not ready for. The reality is that unless you are in the top 10% of buyers of tech, already on the latest tech, and have sufficiently mastered that tech, you are not ready for Gen-AI (which should not have left the research lab when it did and, in all honesty, should still be in the research lab since it still only works in a small number of well defined scenarios and is so bad that every year a couple of AI founders turn away from AI because of it — with Yann Lecun walking away from Meta and LLMs and reverting to world models, that can be thought of as next generation (Semantic) Web 3.0 models augmented with [deterministic and dependable] automated reasoning and, hopefully, very little dependence on hallucinatory probabilistic models [beyond what’s needed to semantically parse an input].)

The only place you should be using Gen-AI is where a non Gen-AI solution doesn’t exist, the task is well defined, and you can build a custom in-house model that works reasonably well in the majority of situations and that can be implemented with guard-rails. But that’s something you can only do if you have a high TQ (Technical Quotient) and have mastered last generation tech. Right now, you should be tripling down on E-MDMA and Advanced Analytics as this tech has improved to the point where it can allow you to optimize processes, spending, schedules, and anything else you can think of with high accuracy and low cost with basic analytics skills as so much comes pre-packaged and the visualizations and drill-downs are much more intuitive than they were a decade ago. Plus, these firms have figured out how to use multiple forms of AI to classify your data with high accuracy and minimize the work required by you to fix errors and reclassify to your preferred schemas. It’s literally drag and drop as compared to the complex rule-building that used to be required. In addition, you should be looking for the mature A-RPA (Advanced Robotic Process Automation) solutions that are highly customizeable and capable of “self-learning” such that the parameters that trigger exceptions will adjust over time based upon user acceptance or rejection of recommended actions and the platform will automatically encode new processing rules based upon the users’ actions on an exception. Much better than Artificial Iiocy that decides everything based on hallucinations.

THE FINAL WORD

If you haven’t mastered all of the tech that existed before Gen-AI, including classical machine learning AI that has been studied, optimized, and proven to work for over a decade, you’re not ready for Gen-AI, should treat it like the drug it is (as it does more damage to your cognitive abilities than many illegal drugs), and JUST SAY NO!

Dangerous Procurement Predictions Part II

As per our first post, if you read my predictions post, you know SI hates predictions posts. It fully despises them because the vast majority of these posts are pure optimistic fantasy and help no one. Why are the posts like this? Because no one wants to hear the sobering reality off of the bat in the new year and the influencers care more about clicks than actually helping you.

But the predictions are not only bad, they’re dangerous. And to make sure you don’t fall for them and make bad decision based on them, we’re going to tackle some of the most dangerous predictions, which include predictions that look innocuous at first glance (like the last prediction on how a big legacy suite will go out of business) but hide the dangerous consequences of what will actually happen if a big suite finds itself in big trouble. Today we tackle the next four, and you can be sure this won’t be the last post in our series. Feeds are still being flooded with prediction posts, and I’m done ignoring the insanity.

4. The jobs market will be tough for the first half of the year, but will start to pick up in Q3 and Q4.

The job market is tied to the economy, and everyone predicts the job market will rebound when the economy picks up. But here’s the thing. Even when the economy picks back up, the job market never does quite as well as the last time. And the economy isn’t going to magically improve half-way through the year. This is the exact same thing we’ve been told the last two years, and it hasn’t happened.

First off, most of the first world economies around the world are flat, borderline recession, or in recession. Secondly, the only thing propping the US economy up right now is AI, and the money circles keeping it afloat as all the AI, Hardware, and Software companies keep moving the same money around investing in each other to keep each other afloat. If the bubble bursts, the US is in trouble, and the economy will quickly flush itself down the toilet. And the job market will go with it.

Considering only the big tech giants who have been hoarding cash for the last few years are in good shape, and everyone else is trying to conserve cash to survive not only the current market but a potential recession, the last thing they are going to do is hire unless absolutely necessary to fill a critical role as a result of a departure. Remember, they’ve spent the last two years using AI as an excuse to lay people off and are always looking for the next excuse to lay people off, not hire them!

Jobs will continue to be super scarce, and only the best will have a chance to land one.

5. We’re in the early stages of a broader pushback (against unnecessary upgrades or technology investments).

A few companies smartening up and saying no to forced big provider upgrades, eight (8) figure consultancy projects, and big Gen-AI investments is not pushback. There have always been a few leaders who have broken away from the pack, did the math, and made the right decisions, but the pack is still charging ahead on Gen-AI. Every big software shop except IBM (who hired a CEO who can actually do math) has invested heavily in Gen-AI, which still loses four dollars for every dollar of revenue, despite any hopes of a real return in the near future and a 94% failure rate.

Let’s face reality. I warned this space about The Vendor In Black nineteen years ago and how he always Comes Back sixteen years ago, no one took heed then, and no one is taking heed now. The business model of the enterprise software space, which has not changed for the two decades I’ve been covering it, is to solve the problem created by the old sh!t by selling the customers the new sh!t that comes with new problems so they can sell even newer sh!t in three years to fix those (and so on). Same old story. Only the vendor names change.

6. We Won’t Buy Things; We’ll Orchestrate Ecosystems.

This prediction likely came straight from the A.S.S.H.O.L.E. and anyone who repeats it should be ashamed of themselves. There are no AI Employees. Claims to the contrary are false and anyone making those demeaning and degrading claims is simply dehumanizing you. And, as we have clearly explained, you definitely don’t want agentic buying because it will happily spend your money not only on stuff you don’t need but stuff that doesn’t exist and, if you’re super unlikely, stuff that is highly illegal. You need wood, it will buy up all the Minecraft wood because it’s cheap and call your problem solved. And that’s if you’re lucky. If you’re not, it will fulfill your resin need with an illegal purchase of hash (the drug) on the dark web (which is labelled resin so the poster can claim they never advertised an illegal drug). And so on.

Plus, as we have already noted, most of today’s “orchestration” platforms in Source-to-Pay are really ORCestration platforms and can barely connect a handful of major Source-to-Pay offerings. They’re nothing close to what is needed to orchestrate ecosystems.

7. Boards will Zero in on Supply Chain Security and Supplier Risk shifts from quarterly PowerPoints to continuous “signalops”.

Just like they won’t invest more in cybersecurity, they won’t invest more in supply chain security until they lose a shipment in the tens of millions. After all, they’ve got supply chain insurance, why should they care? Especially since their current security measures have been sufficient up until now.

But here’s the thing. When the economy goes down, jobs go down. And then two things happen. People get desperate and turn to crime. And criminals, when their investments in drugs, alcohol, gambling, prostitution, and other quasi-legal through illegal activities start losing money because unemployed people run out of money to spend on their vices, these criminals get desperate too — and high value theft becomes more attractive. A temporarily unguarded truck here. A container there. An entire warehouse. And so on.

If it’s critical raw materials they can move (like rare earths), in-demand finished electronics they can sell (like iPhones, where a single container will contain at least 20M worth), military equipment or weapon (component)s that are now in demand globally, they’ll take bigger and bigger chances, especially if there are weaknesses in security. It’s not just cyber attacks that are going to increase, it’s physical attacks, supply chains aren’t ready, and companies won’t even stop preparing them until they lose tens of millions, don’t recover it all through insurance, and risk losing their insurance entirely. No one likes the math of risk prevention because, when it works, you don’t see the return. Even though it’s so much cheaper than insurance! And that’s why, in the majority of organizations, nothing will change.

The Sourcing Innovation Source-to-Pay+ Cascading Mega Map! (2026 V2 Edition)

(c) 2026-01-10

Still useless, but still slightly less useless than every other logo map that clogs your feed!

1. Every vendor still offering software/services as of 4 days ago!

2. Every vendor logo is clickable and takes you to a live site (as of 4 days ago)!

3. Every vendor is mapped to a meaningful category as of the last date of analyst investigation!

So what’s the point?

To again make it utterly clear you can’t select a vendor based on a random grouping of logos on a map, even if they are categorized!

Not even if the map categorizes the vendors by market size, industry, and/or geography. Those are just proxies for organizational spend, solution needs and cultural requirements. And not every mid-market manufacturing plant in the USA is the same.

The only way to select a good vendor is to follow a proper assisted process and engage an expert who understands what vendors are out there to identify the right vendors to invite to the RFP process once your true needs have been identified.

Especially considering the true number of vendors out there is many times more than what an average big analyst firm will tell you, especially when they restrict their recommendations to their paying clients in their maps, and multiples of what an average big consultancy will tell you, especially when that consultancy only knows their partner solutions (that they need to maintain significant focus on to maintain their preferred partner status).

So, what’s the value?

As we explain in detail in the real value of the sourcing innovation mega map (2026 Ed), it shows you

  1. why you need proper proper assisted solution selection (and we can’t stress this enough)
  2. it shows how unstable the space is:
    • fixty-six (56) companies are gone
    • a dozen-plus (12+) companies have been acquired or renamed
    • there are just too many options for an average buyer to make sense of
  3. it (statistically) proves quite a few vendors ARE NOT GOOD (for you at least)

So let this be proof that there are a lot of logos in our ProcureTech+ space and that, if you want logos, you got logos!
666 of them!
Enjoy!

Source-to-Pay
Souce-to-Contract Procure-to-Pay Intake-to-Orchestrate
Sourcing + SXM + CLM Sourcing + Analytics SXM + Analytics e-Procurement Invoice-to-Pay / AP Expenses Payments (& P-Cards) Training
Sourcing + SXM Sourcing + CLM SXM + CLM Sourcing SXM CLM Analytics
Direct Supply Chain Cyber Monitoring ESG / Carbon Marketplaces Legal Marketing SaaS Intelligence

Source to Pay
corcentric coupa ebidtopay effigo
gep ivalua jaggaer onemarket onventis
raindrop sap simfoni synertrade zycus

Source to Contract
curtisfitch deepstream ensolva lgx
mercanis mercell merlin procol scanmarket
vendorpanel

Sourcing + SXM + CLM beneering buyingstation c1 cotiss
delta esm felix fullstep gainfront
intenda ionwave ispnext krinati lightsource
marketdojo marketplanet medius oalia oneadvanced
penny proactis proculy prokuria readytech
sourcingforce supplyon sustainment tradeinterchange vortal
workday zapro

SXM + CLM anydata birdseye brooklyn certa
convergepoint gatekeeper ignite itbid knovos
weproc

Sourcing + CLM aufait axya bidiful bonfire
cobblestone maistro prm360 safesourcing tradogram

Sourcing + SXM aerchain apadua archlet cimmra
cirplus cofactr inpromax k2 livesource
newtron oboloo opentrd pinpools pratis
procurekey procurementexpress promena prospeum qad
qcsolver sourcedogg srmeprocurement supplios teamprocure
tradebeyond truevaluehub valdera vendorful

Sourcing & Analytics curvo levadata requis

SXM & Analytics coglegal costbits everstream flowie
hivebuy lytica softconcis spendqube veridion

Contract Lifecycle Management (CLM) apporchid aavenir agiloft airflip
arteria atamis avvoka bonterms brightleaf
cipherace concord conga contracthound contractai
contractbook contractlogix contracts365 contractsafe dealsign
docfield docjuris docusign dsilo ebrevia
evisort icertis inhubber intelagree ironclad
joro lawgeex leahai legalrobot legalsifter
legartis lexcheck linksquares litera luminance
malbek opengov getoutlaw pocketlaw pramata
relativity simplicontract sirion spotdraft terzo
thinkingmachine thoughtriver tomorro trackado trakti
trueledger unimarket whitevision

 

Sourcing aestiva alpega amplio bamboorose
bestauction bideg bidlock bidso brainal
cosmoone enverus esupplier expenzing fairmarkit
keelvar lhotse loopio mysupply nextenders
onemoresource pagerduty partanalytics ply postrfp
procurementflow protendering responsive serex solvoz
supplychaincube supplyframe transfix wantex zivio

Supplier Management (SXM) achilles adaptone agora alpas
apexanalytix aravo askafox auditcomply avetta
axiscope bedrock canopy cmx craft
creditriskmonitor enlightaspice eProcure eved exigis
franconnect ghx globality graphite grms
haloai hellios hicx informatica integritynext
interos isnetworld itesoft jiga kodiakhub
kyriba leanlinking lexisnexis linkana lupr
matchory mycomplianceoffice meshworks mfg opuscapita
orbweaver partnerelement paymentworks perimeter planergy
processunity procurence qmsc relatico resilinc
riskledger scoutbee silex smartkyc sourcemap
sphera stateofflux stimulus suppeco supplhi
supplierday supplierio suppliersoft supplyhive supplyrisksolutions
tacto tealbook thomasnet transcepta transparencyone
trustyoursupplier vendorapp vendorscoreit venminder zumen

Analytics acquireinsights aera akirolabs alteryx
analytics8 anaplan anvilanalytical calculum creactives
cxonexus deliciousdata digitate electrifai greencabbage
hunterai ivoflow kiresult metricinsights mithra
neqo onetrust oversight partnerling prgx
proaact procurevue pulse robobai rosslyn
scalue sievo silvon sivuno sourcinginsights
spendata spendboss spendedge spendhq spendkey
smartcube spendscape spendworx sps suplari
tamr vanta

Procure-to-Pay (P2P) b2be birchstreet b1p compleat
curemint dynatos elcom equallevel esker
ezatlas fraxion inbuild kissflow marketboomer
modernpo oracle orderco pagero pairsoft
payem precoro proceedo procuredesk procurenode
ramp settle softco sutisoft tradecentric
tradeshift vroozi

eProcurement bellwether bill brex causeway
controlhub cordis enkash factwise unanet
finexio fluentcommerce idas inorder lojistic
markit nimbi openenvoy payhawk procurementpartners
punnchoutcatalogs purchasingplatform sovra spendmap spendwise
teampay uppler vurbis yaydoo

Invoice-to-Pay (I2P) / Accounts Payable (AP) abby airbase apexpress appzen
aria avidxchange basware billtrust bluechain
candex concur coreintegrator corpay dataserv
directcommerce dooap edenred edicom emburse
ezcloud fiscal freshbooks getpaid glean
iqinvoice lexmark makershub mineraltree nipendo
nium onphase opentext paid photoncommerce
procurify relish rillion sage servicenow
snapb2b snowfox sourceday spendconsole spendesk
stampli symbeo taulia tipalti xelix
xsuite yooz

EXPENSE airwallex deem expensify finetune
navan pleo pluto tangoe travelperk
worktrips

PAYMENTS & V/P-CARDS bluebean bottomline enable finix
payoneer previse transactis transfermate wise

Intake-Manage-Orchestrate
appian arkestro automationanywhere capto
celigo convergentis corvolo elementum focalpoint
levelpath netfira omnea ontra opstream
oro P2Cnnct pega pipefy pivot
procureai provalido qntrl sudozi tonkean
workfellow zflow zip

ESG/Carbon Scope 3
carbmee carbonaltdelete carbonanalytics carboncare
carbonchain carboncloud carbonfit carbonminds circularise
circulartree circulor climatecamp co2ai conserviceesg
cozero ctrls daato ditchcarbon ecovadis
emitwise greenkpi makersite measurabl minespider
responsibly sustainalytics Sustamize trustrace veriforce
verso vertaeon watershed

Cyber Monitoring
cybersecurityintelligence securityscorecard

Direct Supply Chain
approve athingz contingent ensun
exiger exostar findmyfactory facturee frdm
genlots kreatize marvo gosupply nimbly
omx overhaul owlsolutions partfox partspace
prewave qstrat rapidratings sayari shouldcosting
supplywisdom trademo versedai visotrust whistic
wholechain xometry zetwerk

Legal
apperio brightflag bryter fulcrum
lawvu mitratech persuit thomsonreuters wolterskluwer

Marketing
agencymania alliansis decideware hhglobal
mtivity moosh rightspend

SaaS
appdirect apptio auvik beamy
bettercloud calero cledara cloudeagle diminish
entrio flexera flywl hudled lightyear
lumos nachonacho najar npi productiv
saasrooms sastrify setyl spendflow substly
torii trelica trgscreen tropic varisource
vendr vertice viio zluri zylo

Training
eveneum lavenir positivepurchasing

MarketPlaces
auxionize axiom bizeebuy cimple
collectivespend droppe faire growinco iap
joor kaleida mercadolibre partstrader procureafrica
produceiq rheaply smartequip sourceit unite
wescale

Intelligence
apriori aranca beroe bipsolutions
brightfield buynamics capella chai consource
convergencedata costdata cottrillresearch covalyze diprima
dnb easykost evpsolutions expana fareye
freightos freightender fuelme importyeti LRQA EIQ
magayz metalminer mtisystems nvelop pando
paxly moodysanalytics procureforce procurementiq sourceintelligence
sourceful sovos spikefli totalbid trax
truevaluehub trustpair xeneta

Dangerous Procurement Predictions Part I

If you read my predictions post, you know SI hates predictions posts. It fully despises them because the vast majority of these posts are pure optimistic fantasy and help no one. Why are the posts like this? Because no one wants to hear the sobering reality off of the bat in the new year and the influencers care more about clicks than actually helping you.

But given how dangerous and costly the hopeful fantasy has become, not only did SI swallow its disgust and give you a realistic predictions post, but it’s going to collect and lay bare the most dangerous of the predictions that, even if seemingly innocuous, will lead you astray if you believe them. And now some of the influencers and LinkedIn aficionados are taking up the claims, and the charge, but like many other claims, they are overstated.

Today we tackle the first three, but you can expect this to be the first of many posts as dangerous prediction posts flood your feeds for the rest of the month.

1. The “Great Convergence” Accelerates

The claims of of the ORChestration providers is that all roads lead to them, the convergence will accelerate, and you won’t have to worry about what you need because, as long as you have orchestration, you’ll have it all!

For example, if you want to use the largest orchestration provider in S2P, your are limited to the platforms they have already integrated. The same goes for the second or third largest. Plus, if the providers you want to integrate aren’t reasonably sized Source to Pay providers, good luck expecting the workflow to support them appropriately.

Moreover, they were built to minimally support the existing solutions, not emerging solutions in the Source to Pay and extended Supply Chain Marketplace. In other words, the convergence will continue at a snails pace, but it will never be great!

2. “X” Finally Gets Modern Attention

It doesn’t matter what X is — if X has been needed, but ignored, for the last ten years, it’s NOT going to all of a sudden be addressed this year. For whatever reason, it will continue to be ignored.

Example #1, Cybersecurity.

As per my recent post on breaking down the risks: IP / cyberattacks, the risk of cyberattacks has been high since 2014, a year when 71% of organizations were affected by a successful cyberattack! Ten years later, 70% of small to medium sized businesses are still getting hit by cyberattacks. (Which means that if it was going to get major attention, shouldn’t 2014 have been the year?!?)

Nothing has changed — the reason? Cybersecurity is seen as a cost, not a return. So, when a successful attack results in significant losses, organizations spend on improved cybersecurity, and ignore it until the next significant successful attack hits, and that is the only time they will spend for new systems across the board, and that’s it. That’s why cybersecurity, inside and outside the organization, won’t get any more attention this year than last year.

Example #2, Risk Management.

There’s a big reason it’s been the exact same risks in the state of procurement studies and reports for at least the last five, if not the last ten, years. It’s because, despite the fact that risks keep increasing, no one ever does anything about it … there’s no additional investment in risk management software. Why? Again, it’s seen as a cost and not an investment. And when you’re already paying for insurance, why pay for what, at best, seems like more?

Even though the cost of insurance will soon be unaffordable given that natural disaster and fraud losses are going through the roof, if you can even get insurance at all, risk management solutions are still being ignored by every organization that hasn’t suffered a major loss as a result of a risk-related event. (And who knows if insurance will cover AI losses when AI escapes the vending machine? It’s a question you should definitely be asking!)

Example #3, Direct.

That’s supply chain, right? Right?

Wrong! But that’s the view that the vast majority of Source-to-Pay providers have taken since the beginning. Sure a few big suites picked up a few smaller players that specialized in direct sourcing, but that’s about it from the big players. And there are a few startups here and there, but they’re all overlooked, underfunded, and not getting any traction.

Because it’s hard. Damn hard. And the majority of S2P players don’t want hard. They want easy. They built easy. They sell easy. And that’s all they want to do. (And, often, all they can do!)

We could continue, but you get the point.

3. One of the big legacy S2P suites will go out of business.

This is a prediction straight from the genius of Gary Wright. Only a Dream Weaver would predict this! This has happened exactly once since our space began in the late 1990s, and it wasn’t exactly going out of business, it was a big acquirer deciding the space wasn’t profitable enough and shutting the vendor down. Specifically, it was IBM shutting down Emptoris and shunting all the customers to SAP Ariba in 2017.

Every big provider in this space is controlled by PE who have poured tens, hundreds, or thousands of millions (that’s billions) into the firm. If it starts losing money, and if they think they can’t turn it around, rather than shutting it down, they’ll flip it to another firm at a loss (to recover some investment) who will pick up some fire sale acquisitions, integrate them, update the UX, install a whole new management team, fluff it up, rebrand it, and bring it out with a whole new spin. Like ERPs, Suites never die. Even if they’re twenty years behind the times.

So if a new big player hits the scene, check under the covers, do a bit of research, and dig up those skeletons. PE knows how to make everything old new again, but tech is not like fashion, and you don’t want two decades old SaaS, as that’s just the same old sh!t.

Who’s Funding Your ProcureTech Vendor?

This question is more important now than ever! Not only is the RCD (Relative Corporate Debt) of many FinTech companies too high right now (See: Calculating RCD), signalling a decline in customer service and potential abandonment, if not outright vendor failure down the road, but the ongoing viability of many VC and PE firms, or at least their ability to support their investments, is also in question.

Many firms are too heavy on AI plays that are still losing as much as $4 (or more) for every $1 of revenue they take in, requiring massive ongoing investments to maintain. Even big PE funds only have so much cash to burn, and the only way they can do this is to liquidate assets and holdings if they can, or, in the worst case, simply write off losses (and associated future costs) of those holdings they can’t liquidate.

Softbank’s end-of-year investment in OpenAI really puts this into perspective, as chronicled by Mr. Klein of Curiouser.AI and Berkley in this LinkedIn post.

As far as I am concerned, this is bad news for any of SoftBank’s FinTech holdings that may require funding in the next few years, and a warning to make sure you don’t select / continue / depend on any of their FinTech holdings where they have a large or majority stake until verifying those holdings are profitable and likely to stay that way! (Now, SoftBank has traditionally had very good investment chops, so it’s likely the majority of holdings are profitable …)

However, they aren’t the only firm making huge over-investments in AI and weighting the portfolio down with companies that might never see a profit. This means that this warning also applies to many other Tech investment funds, starting with Thrive, Dragoneer, Altimeter, and Coatue who also have large stakes in OpenAI. They could all end up in the position where they are going to have to sell off / dump assets to maintain the ridiculous losses OpenAI is seeing, and any holdings not performing well will likely be the first to go / get dropped. (Remember that the average age of the first three of these groups is 15 years, and they are [becoming] modern SaaS/AI heavy, whereas Softbank Capital has been investing for 30 years, and is a lot more diversified. Softbank may be able to weather a complete crash in OpenAI valuation if it occurs. But these other firms may not!)

But, as we noted, the real warning is not for SoftBank or these other mega funds (in the significant 8 and 9 digit range) that have funds to weather a storm. It is for the smaller funds, especially those less than 1 Billion, that are too AI heavy.

As a result, when selecting any FinTech platform, you need look at the portfolio of any investment player with a substantial majority stake. If a large segment of the portfolio of a significant/majority investor is “AI” companies losing money hand over fist, then the vendor of that FinTech platform cannot be considered a stable vendor if it is not profitable. This is because you can’t count on the fund having the resources to support the vendor to profitability, even if vendor is a fund darling. This is the case even if the RCD calculation looks good! A lot of the smaller funds can’t afford an AI crash given the AI-heavy focus of their SaaS portfolio.

(Face it. An AI crash is coming. Too much valuation against too little return, and investors only have so much patience. The only thing we don’t know is how severe the crash is going to end up being. Is it going to be a minor drop across the tech markets or a major crash like the 2008 housing crash or the 1999/2000 dot com crash?)