Proponents of Gen-AI will argue that you should go all in on their next-gen LLMs because, unlike current systems and many humans (who are lousy keepers of record), their decisions, like their actions, are 100% auditable. And, again, that’s complete and utter bullcr@p.

Just because you can ask the LLMs to output their reasoning, and you can ask them to log everything they do from the minute you start the interaction, but, because the reasoning is all based on probabilistic math at a scale NO human can understand (and for which we have NO measurements yet), you have no idea WHY the LLM reasoned a certain something or IF the Gen-AI will reason the same way on the same request, even if that request is re-iterated only 5 minutes later!

You can simply search the internet for hundreds of examples out there of people giving the exact same prompt to the exact same LLM AI five minutes later and getting a slightly to completely different response.

Gen-AI LLMs don’t understand. They don’t actually reason. And they definitely don’t think! That’s why they are NOT auditable. And that’s also why they should NEVER make a decision. (However, since they can analyze more data, and for some tasks have, more often than not, achieved a competence beyond an average human happy to regress in IQ to the late neolithic era, they should definitely suggest decisions with their reasoning. But the LLMs should never, ever, execute on those decisions without human approval.)

The reality is that only an LLMs ability to log what was done in an immutable blockchain format is useful compared to an employee who knowingly did something wrong for a bad reason. Since the AI is not intelligent, and doesn’t have ethics, it has no reason NOT to log its reasoning and why an action was taken. But, as per above, the LLM is still NOT auditable.

… then, as Stephen Klein of Curioser.AI points out, you have a puppet show, “except instead of sock puppets, we’re using large language models and API loops”!

Just because it happens autonomously, looks social, appears to have an identify, and fakes a dialogue, it doesn’t mean there is anything more to it than the modern equivalent of a puppet show.

Gen-AI is the ultimate show and if P.T. Barnum were alive today, it would be his ultimate circus. But unlike the scarecrow, it doesn’t have a brain. It may have the ability to harness more compute power and data than any algorithm we have developed to date, but it is still dumber than a pond snail.

It has very few valid uses. I’ve discussed some of them before, but let’s make it perfectly clear what little it can actually do:

• natural language processing — and, properly trained, it can not only equal, but even exceed the best last generation tech in semantic and sentiment processing
• large corpus search — while it will never be 100% accurate, it can find just a few potentially relevant documents among millions with few false positives and negatives
• large corpus summarization — again, while it will never be 100% accurate, and most good summaries won’t be top tier, it can summarize large amounts of data, and usually extract just the relevant data in response to your query
• idea retrieval — not generation, retrieval of ideas based on a review and summarization of petabytes of data; very relevant for users dependent on LLMs who are suffering minor to severe cognitive atrophy; with proper prompting this can take the form of
  • strategy / workflow suggestion
  • devil’s advocate
• usage and workflow prediction during application development
• rapid PROTOTYPE generation for usability and efficacy analysis
  (not enterprise application development)

The reality is that Gen-AI

• cannot reason,
• is not deterministic, and
• is essentially nothing more than a meta-prediction engine;
• is providing ideas based on meta-pattern identification,
• is predicting based on a layered statistical model beyond ANY human understanding, and
• generates code riddled with security issues and possibly even boundary errors;
• and let’s not ignore the fact that hallucinations are a core function that CANNOT be trained out .

This means that often the only way to succeed with Gen-AI is to more-or-less abandon Gen-AI LLMs in production applications except as Natural Language Parsers (as they are easier to train to accuracy levels beyond last generation semantic parsers which could take months to train to high effectiveness — and I know this from personal experience), and revert back to the AI tech that was just reaching maturity and industrialization readiness that I was writing about in the late 2010s. The reality is that, if you are willing to use some elbow grease and put the hours in, you can create spectacular applications with last-generation tech, and then use Gen-AI as a natural language interface layer to simplify utilization, integration, and complex workflows. If you are willing to create the right guardrails, where the Gen-AI LLM can only trigger specific application services with specific data in specific contexts, with HUMAN approval, then you can use it responsibly. Otherwise, it’s a crapshoot as to the results you’ll get.

For example, you should never use it for negotiation, which can be as much as reading the other person, as this is a very risky application as the number of soft-based data points you need for a decent prediction typically far outnumbers what you have available … even for public figures where you believe you have lots and lots of data on them available to judge their reactions. But hey, if you want to lose your lunch money, and possibly your entire bank account, go ahead and let it act as your buyer (but if it can lose hundreds powering a vending machine, imagine how much it can lose on a seven to nine figure category).

Even though plenty of vendors will provide some very convincing demos that seem to indicate Gen-AI LLMs can do otherwise, don’t fall for the tricks. During the demo, The Wizard of Oz is hiding behind the curtain. The not-so-great thing about LLMs is that, for a very specific set of tasks/situations, they can be overtrained on a very specific corpus to over-perform against those tasks and greatly increase the chances that any demo they deliver to you works fantastically well.

However, what this also means, is that you definitely do not want to use the Gen-AI LLM for tasks that are quite distinct and significantly different than the tasks/situations the Gen-AI LLM was over-trained for as the Gen-AI LLM is going to perform quite poorly at best, and possibly quite disastrously at worst. The reality is that once the puppeteer is no longer pulling the strings, all bets as to efficiency and effectiveness are off.

The Gen-AI ringmasters are employing the same philosophy and same techniques that made some of the early spend auto-classification providers “leaders” with unheard of success rates compared to when the average organization employed similar auto-classification tech and got dismal results. (Because they just didn’t know what “AI” actually stood for!)

Don’t be fooled by the ringmasters. If you want results, lie its AI and buy solutions that work.

A few weeks ago, we told you that if you think you’re ready for AI, you’re not ready for AI because, even though the vast majority of you are chasing AI, only a minority of you are ready to even investigate it. And we mean investigate, not use. That depends on whether or not there are any relevant AI solutions for you needs — and despite the repeated BS claims by the big AI vendors, there may not yet be any!

And it’s not just because you haven’t

• admitted you’re only chasing AI because of FOMO and FUD
• assessed where you are
• realized you are generations of tech behind
• determined you just don’t have the right resources

But it goes beyond that.

In order to have any hope of succeeding with AI:

You need great data and great Master Data Management

… but you don’t even know where your data is! You have no governance policies, no management processes to ensure data is kept up to date (or even backed up unless you have already suffered a data loss and determined losing that specific data would be disastrous), and no clue about what that entails. And even if you realize that you need (master) data management, you won’t get the C-Suite to sign off on it, even if you call it E-MDMA and tell them they’re getting free samples!

You need a good IT infrastructure, with context-based integration and workflow capability

… but you have no central strategy for data integration, system orchestration, or enterprise workflows, and your IT infrastructure is whatever cloud your ERP runs on. AI, especially Gen-AI, requires massive data and massive compute and, guess what, that requires massively powerful, solid, infrastructure — and yours is probably held together with spit, glue, and duct tape!

You need an in-depth understanding of not only the problem you want to solve, but what AI algorithm will actually work reliably and with measurable confidence

… but guess what? In order to properly evaluate AI, you need an advanced understanding of the technology, which usually requires an advanced, graduate level, understanding of the underlying mathematics as well as deep understanding of the problem and how to mathematically model it.

You need a strong technical quotient (TQ) to implement, train, and verify those AI algorithms

… and that’s more than just a single expert who can evaluate, but a strong bench of architects and developers to make it work — you can’t rely solely on the vendor as they can go away, their bench can leave, or they can get pressured by their investors to just sell, sell, sell (and pretend you don’t exist once they get the cheque) and that leaves you to your own skillsets.

You need domain experts on hand to verify the results

… and this goes double for critical results. If you are using an augmented intelligence to help with sourcing, market analysis, strategy recommendations, etc. you can’t let an agentic system execute on a computation without verifying it. No system ever has all the data, no system ever knows all of the options, and no system has the soft information (and how you might be able to work a sales rep to your advantage). And if someone messed up the data, considering just one wrong number can entirely throw off a hundred thousand variable model, you’re in deep doo-doo if the system executes an order without your verification.

You need to redesign your processes to optimally take advantage of AI

… because your processes come from the time before office machines existed, so obviously they weren’t designed for modern technology. And while traditional workflow / RPA can easily automate what you have (even though it shouldn’t), since AI requires good data, good structure, properly designed models, etc. — it’s not going to work with whatever Guilded Age process you’re using now.

And so on. The reality is, despite what all the big vendors, big consultancies, and big analyst firms tell you — you’re just not ready for AI. (And definitely NOT ready for big bang projects that will end in big busts!) It’s just the latest silicon snake oil panecea — like all purpose predictive analytics, the fluffy magic cloud, SaaS, and the World Wide Web and every other panacea that has come before. (Just remember the last time silicon snake oil was hyped this much, it resulted in the dot com bust!)