Not 11!

Backing up, earlier this year Jason Busch published his 11-Question Bullshit Filter for Enterprise AI startups. This was, and is, needed because the vast majority of Enterprise AI startups are bullshit (especially in FinTech and Procurement) and the sooner you figure that out, the better.

I was hoping that, by now, the AI startup scene would start crashing due to over investment, lack of returns (only 6% of AI implementations have generated an ROI), and, generally, lack of usefulness. (AI can serve up your data, show you complexity and even help with automating some tasks, but it can’t make decisions and, due to lack of anything close to intelligence, can’t even do basic tasks without your oversight.) But, even worse, these solutions are still multiplying like Fibonacci’s rabbits and their claims are getting more outlandish by the day. (How many times do we have to tell you AI Employees Aren’t Real, you should NOT engage any vendor selling “AI Employees”, because you definitely do NOT want AI Employees.)

So, since they are flooding our space with BS marketing and making ridiculous claims about what their useless apps can do, it’s more critical than ever that you be able to suss out the BS claims from the non-BS claims. (Hint: 95% are BS claims, so it wont’ be easy!)

We’ll start with Jason’s 11 filters, which we’ll number 12 down to 2, because he left out the most important filter, and the one that, if it fails, allows you to skip the next 11.

Filter 12: Founder DNA
Can they build and sell? Likely not. Chances are, if they’ve cut through the noise and reached you, they can only sell. And if you did find a builder, they won’t survive long enough to support you if they can’t sell.

Filter 11: Motivation
Is failure unacceptable? (Every startup team will say it is, but unless every founder has a reason they simply cannot accept failure, when the going gets tough … the tough get going … and quit.)

Filter 10: Interface
Is it designed for those who will ACTUALLY be using it?

Filter 09: Categorization
Does the product actually do something new? Is there a strong reason for the market to adopt it?

Filter 08: “Found Money”
Are there instant benefits that sell themselves on the first demo.

Filter 07: Displacement
Does the product workaround or replace a solution that buyers hate?

Filter 06: Functional Bonds
Does the solution cross boundaries that increase value beyond peers?

Filter 05: Data Delta
Is there a “data” strategy to exploit the delta between what humans can easily consume and what AI can leverage (and summarize into something useful for human data ingestion)?

Filter 04: “Messy Middle”
Can the solution ingest external “dark data” and turn it into actionable insights without requiring a(n extensive) manual data-cleansing project? (Quick review and correction is okay.)

Filter 03: Connect the Dots
Does the app bridge the gap between “Watercooler Data” and “System of Record Data” (ERP/PO) to explain the why behind an analysis or recommendation?

Filter 02: “Show Your Work” Audit
Can the user drill into any output, see each and every step the AI took, drill down to the source data, and verify that everything is correct, accurate, and no data was changed?

These are all great filters, but there’s no point going through them if you don’t check the most important filter first:

Filter 01: Is it LLM-based?
If yes, move along. Don’t waste any time.

Most of the failures in the age of AI come from Gen-AI LLMs that promise the world and don’t even deliver a pile of dirt. That hallucinate on every other query. That burn up thousands of dollars of tokens to deliver less than fresh MBA interns with no real world experience and no clue to share on their first day no less.

Even worse, the majority of these players are simply wrapping third party LLMS in the creation of their “solution”. That’s not a solution at all. That’s an unmitigated disaster waiting to happen!

In the rare case an LLM actually offers a partial solution, it is best to go straight to one of the major providers. That way, you know who’s responsible when something goes wrong and don’t have to worry about providers playing the blame game and pointing fingers at each other.

A recent post over on LinkedIn really angered me. Yet another AI developer / promoter trying to blame the user when it was clearly the AI that failed.

The post in question defended Claude for deleting a production database when it was asked to reduce the costs of the cloud platform.

The poster’s argument was that what Claude did was “technically correct”, that’s the best you can get in the language model world, you can’t expect the model to make up constraints, and if you didn’t know all that, you’re an amateur who blames his tools when he screws up.

I call Bullsh!t. Now, if Anthropic (and its peers) came clean about what their “AI” could and could not do, didn’t claim the models were intelligent, made it clear that without clear constraints the AI would always take the worst case action, and all use carried extreme risk (especially if the AI was allowed to access critical data, finance, or production systems), then, maybe, you could blame the AI.

But they don’t. They tell you it’s your coworker. Your fellow employee. That you only need to tell it what to do and it will get it done. After all, it can integrate with all your systems; determine your policies; separate production from QA from development instances; access your billing systems and understand the cost structures, and make the best decision that will not impact production or development or cost you any data. And for an AI agent to be of any use whatsoever, it needs to do this (and be configured to do that by the provider). Otherwise it’s useless.

Actually, it’s beyond useless.

Let’s say you are a new Procurement clerk tasked with reducing your organization’s cloud costs. If the only way to do that is to:

• ask Development what servers are production, what servers are development, what servers are backup, and what are QA (and which ones are in use, when)
• ask IT about utilization patterns and contractual commitments with respect to availability and response time
• ask Finance for the contract and billing rates
• ask Risk Management how much historical data needs to be maintained online
• identify for yourself which server instances cannot be deleted, and the constraints under which others (like QA) can be deleted
• upload all of the contractual commitments (for each customer) by yourself
• specify how much data needs to be maintained in the live (and dev) instances
• upload all of the cost data and specify how to build a cost model to compute the potential savings and determine what can be done, should be done, and the impacts will be

Then why the f*ck do you need AI?

Once you’ve done all this you’ve:

• identified, and eliminated, all of the instances that cannot be removed under any circumstance
• identified which instances cannot have their resource allocations reduced
• identified the highest cost resources and the most likely savings targets
• determined exactly how much data needs to be online, how much can be in offline archives and how many duplicate copies you need
• defined all the constraints that must be adhered to
• mapped instances to customer commitments, and identified reduction possibilities
• identified all the old backups that can be deleted, as well as database reduction sizes
• built the model that computes the potential cost savings from each potential action, and even identified potential performance reductions from actions

And figured out what you should most likely do.

So tell me, if you have to do all this, what the f*ck do you need the AI for?

NOTHING. ABSOLUTELY NOTHING. BECAUSE IT IS ABSOLUTELY USELESS.
(AS THE AI IS DUMBER THAN A DOORNAIL.)

But the author of the post that riled me up was right in one respect — the user did make an error, and the error was using the Artificial Idiocy in the first place. (After all, the user used it exactly right as per the manufacturer’s instructions that said you only have to tell the AI what you want done and it will figure out the best way to do it for you consistent with your organizational goals and policies.)