Author Archives: thedoctor

How You Know Your Education System Is Broken!

Only 40% of employees say they’d be fine NEVER using AI again! (As per a recent Section AI survey in the Wall Street Journal of 5,000 white collar workers, as reported in a recent post by Stephen Klein who also noted that the majority of employees say it only saves them 2 hours or less per week. Furthermore, he also mentioned a Workday study that reported every 10 hours “saved” by AI resulted in 4 hours being lost due to required error corrections, flawed output revision, and necessary verifications, which means there aren’t much savings at all. [Specifically, for an average employee to actually save 10 hours, they’d have to save almost 16 hours, which would take them two months to achieve!])

Gen-AI is failing 94% of the time. It’s causing serious cognitive apathy and decreasing our IQs far beyond what Twitter achieved on its introduction (where it reduced our collective attention spans to that of a goldfish). It’s direct and indirect costs to run 8 hours a day are often more than to just hire another person (due to compute requirements that are 20X to 200X that of Google for a basic query, and the extreme amount of energy and water [for cooling] required on grids that are already stressed and ecosystems where fresh water is running out).

Chat-GPT. Claude. Grok. Rufus. Gemini. Meta. DeepSeek. Perplexity. Co-pilot. Poe. Le Chat. They’re all over applied due to over promises when they all have fundamental issues (like hallucinations) that cannot be trained out (as the issues are a result of their core design and programming), limited data sets (and now that AIs are being used to generate additional training data, performance is getting worse), limited guidance, and no guardrails.

There’s always been a time and a place for proper AI, but it’s not now, it’s not everywhere the investors losing Billions on Open AI and competitors are telling you, and it’s not the “AI” they are pushing.

Every time a new advancement in tech comes along, we always forget how long it takes to get from prototype to safe for unmonitored regular industrial and home use, be it hardware or software. With AI, it’s always been about two decades between a new algorithm being invented, and a production ready system with known performance, limits, and guardrails being ready for the mass market. In other words, this tech shouldn’t even be out of the research labs yet! We definitely shouldn’t have every major consultancy trying to push it as the cure-all for every problem throughout your entire enterprise. (Or new start-ups claiming they can offer you AI Employees!)

How many more examples of (silicon) snake oil do we need before we accept there is no panacea for all your ailments — be they physical, mental, or industrial — abandon this current iteration of Gen-AI, and go back to the targeted, mature, solutions that were finally ready for prime time (as we finally had enough processing power, data, and research behind us to deploy them with confidence)?

And even though the technology might work as much as 12% of the time, as per a PwC study that found that 12% of 4,454 CEOs surveyed reported both revenue gains and cost reductions, that’s not much of a validation of the technology — especially since those gains and cost reductions could have nothing to do with AI at all (and the pilot success of 6% from a recent McKinsey is a much more reliable metric here).

If you want real success, find a (A)RPA solution that works, lie its AI and buy it while you wait another decade for this technology to mature to the point its reliable, guarded, and safe for mass market adoption and widespread application. (Or wait for an AI-enabled SaS provider to come along who will do the 24/7/365 human monitoring required for you and make its software is usable and safe through this monitoring. Because all the current generation of LLM[-powered Agentic AI] tech is doing is increasing the need for human monitoring, not decreasing it.)

Tomorrow is International Women’s Day.

So prepare for a massive onslaught of posts by companies large and small, from far and wide, that will lavish heaps of praise on their female (identifying) employees and all the hard work they do … and then prepare to hear absolutely nothing about how great these female employees are for the next year!

Right now, there is a lot of pushback in the US against DEI, and rightfully so since the whole point of DEI — equal opportunity and equity in treatment of all individuals from an employment perspective (future, present, and past) — has been replaced with objective outcome measures that result in the first person who checks the right mix of race-religion-gender (identifying) boxes being hired, and not the first person who qualifies for the job, which not only results in poorer organizational performance but resentment and backlash when qualified candidates are discriminated against because they don’t check certain boxes (and this includes discrimination against more qualified female applicants who would be rejected in place of a disabled male Asian Zoroastrian because that checks 3 boxes on the DEI bingo card).

But there isn’t nearly as much pushback against virtue signalling for accepted causes, or, even worse, basic decency. And this is a shame, because
* you don’t recognize your female employees by publicly lavishing praise on them one day a year and then completely ignoring them the other 364 days,
* you don’t respect your female employees by paying them less than their male counterparts because “that’s just how it works”, and
* you definitely don’t honour your female employees by claiming they aren’t suitable for C-Suite positions because they want more family time or you expect them to take a career break to raise the next generation.

Instead
* you recognize your female employees by acknowleding them when they do something significant — no one wants lip service,
* you respect your female employees by paying them as much as you’d pay a man for the same job — especially when these female employees are probably more qualified, and
* you honour your female employees by recognizing that they are probably more capable of a C-Suite job than you are! (Remember, they regularly juggle work life and family management — which typically includes their work schedule, their partner’s schedule, and the schedules of 2 to 3 active kids — when you struggle to schedule your own meetings and make your tee time.)

In other words, if all you are going to do is annual virtue signalling, please don’t. It’s disrespectful and I personally can’t wait for the day the next #metoo movement in the corporate world calls out this hypocrisy.

Last year I penned a long post after IWD asking what you are doing TODAY to help women. Of course there were NO RESPONSES from any of the companies in our space who did multiple women’s day posts and ads, and in the next month where I scrolled LinkedIn feeds daily for at least 15 minutes looking to see if any of these same corporate feeds recognized a female employee, I came across three posts from three companies doing so — compared to the well over 100 posts from over 100 companies claiming to celebrate women on IWD.

I think our resident unwoke/uncancellable anti-virtue signalling crusader Jason Busch needs to take up this cause too! True equality for all! (And no lip service!)

Without Human Smarts, There Will Be No (Usable) AI!

And I’m so happy I’m not the only one pushing this theory. Mr. Stephen Klein recently published a great post on The Age of Pretend.

In the post he notes that:

“Everyone assumes AI’s biggest bottleneck is compute. … That assumption is wrong. The real bottleneck … is architecture, specifically, a design decision made in 1945. … The real constraint: the von Neumann bottleneck. Modern computers separate memory and processing. Data has to move back and forth between them. For most software, that’s fine.
For AI, it’s catastrophic.

Some numbers the industry rarely highlights:

  • Accessing off-chip memory consumes ~200× more energy than the computation itself
  • Roughly 80% of Google TPU energy goes to electrical connections, not math
  • A 70-billion-parameter model moves ~140 GB of data just to generate one token”

LET THAT SINK IN. Us old timers remember “640K out to be enough for anyone”! The Apollo Guidance Computer — you know, the one that was installed on each Apollo Command Module and Lunar Module in the Apollo Missions, had 2K Core RAM Memory and a 36K ROM. Even today, unless you have an iPhone 17, your phone probably only has 128 GB of storage. That means, even with the processing power of your phone (that dwarfs most computers us old timers have ever owned), you can only process ONE token. (Now do you understand why the data center [energy] demands for your Gen-AI chat-bots are destroying the planet? Anyway, we digress …)

This means that (Gen-)AI has hit a wall. Computer Architecture supports massive compute at scale, massive storage at scale, but not massive transfers at scale.

So what does this mean?

Do you remember the days of RAM drives? Not only did it speed things up, but it kept your machine cooler because, as Stephen noted, less energy accessing data in RAM than on disk.

And do you remember the fun of Assembly? (Okay, that’s sarcasm!) Once you learned to maximize register usage (i.e. re-sequencing processing so that you minimized reads from, and writes to, memory), your code got faster still (and machines stayed cooler longer, which was obvious by the lack of noisy fans spinning up).

We’ve known about this problem for decades. (Eight decades to be exact!) It’s too bad today’s students don’t study the basics and understand it’s not strength that determines computational speed and energy requirements, it’s data scale — whether the data fits in memory or not, whether “significant” chunks fit in the onboard GPU memory or not. (And specifically, can you scale the data down enough for the efficiency you require?)

But this is still the key point in Stephen’s article:
“The next major improvements will likely come from smarter algorithms.”

We might need brute force to detect patterns we can’t (yet) see, but the only way to truly advance is to understand those patterns and code optimal, light-weight algorithms that exploit fundamental rules to allow us to process data quickly and efficiently.

Until we figure that out. You’ll never have usable AI (and definitely never have REAL AI as not only will it never be intelligent, but it will never, ever, get anywhere close).

Contract Management for Small Companies is …

James Meads isn’t saying it in this LinkedIn post, but he’s hit the nail on the head with an old-school hammer. (Unlike the shiny new hammer, the old school hammer actually works.) For most small enterprises, they don’t need full contract lifecycle management, they need document centralization and visibility and time-based reminders. That’s it!

This is because they:

  • do negotiations through phone and Word-redlining,
  • use hand signatures through scans and emails,
  • place orders through e-docs in standard format to receipt email addresses because they don’t have a fancy e-Procurement system which does integrated P2P
  • don’t have a modern AP system that can ingest contract meta-data and they still need a clerk to enter the price tables manually
  • still need to enter the non-order commitments manually into their project planning tool
  • etc.

What they need is old-school document management built on a CMS (Content Management Solution) tailored for contract documents and Procurement needs. That’s it!

This is not a 50K to 250K solution, but a 5K solution … (especially since most CMS is essentially shareware these days)!

Now, once you hit the true mid-market, and start spending 50M to 100M a year or more, you need a lot more advanced capability across the board, and if you’re contract heavy, spending 50K to centralize all of the above and do true automated end-to-end lifecycle management efficiently is peanuts. However, when you’re less than 50M revenue, spending at most 20M externally, and only have a few categories large enough to negotiate significant discounts, you just don’t need advanced S2P solutions, or the price tag. Anything that enables a standard process is all you need. (Even if you are a F500/G1000, the reality is that just having a basic solution that enables a standard process will likely get you 90% of the “savings” the most advanced suites promise at 5X to 10X the price tag. At the end of the day, most firms only have a few [dozen] categories [at most] where a more advanced solution is needed to extract value.)

(And then, as you grow, there are great Mid-Market S2P suites that start in the 50K range, with the best/most extensive maxing out around 250K a year, meaning you don’t need to go to a mega suite and pay millions. But since Gartner, Forrester, etc. maps will never list them, you do have to look for them. But you have resources. James’ site. Sourcing Innovation. etc.)

Your SaaS Vendor Should be TRUSTworthy … But They Shouldn’t Have to Tell You!

In fact, I’d argue it’s a red flag if they do. But let’s backup.

A trustworthy vendor is one that

1) Clients Trust

2) Clients’ Third Parties Trust

3) Suppliers and Partners Trust

4) Third Party Analysts and Consultancies Trust

… and all of these will imply trust in their recommendations and reviews, even if they don’t explicitly say it.

Digging in.

1) They treat you like a client from the first interaction.

The first interaction asks about your needs, not just what you are looking for.

They tailor the demo to your business and categories.

They answer your questions openly and honestly, don’t deflect from features they don’t have today, give you real timelines, and offer workarounds until they deliver.

Once you sign, they guide you through implementation and change management, work beside you to train you, and always respond beyond SLA requirements.

They don’t just focus on immediate results, but on ensuring you level up and could continue to get results without them. They act like a partner.

2) They treat your suppliers and partners like clients too.

They’re always there to help, they make it easier for the supplier than their competitors, and prove their value to the point the suppliers want to use them too.

3) They’re fair to their suppliers and partners. They pay on time. They work with them. They take blame when it’s their fault and not the supplier’s or partner’s … who like working with them more than other companies.

4) Analysts and consultancies happily recommend them even when they’re not (paying to be) on the Map or a preferred partner. Sometimes when they aren’t even the most appropriate solution just because their customers are so much happier.

It becomes so obvious that you don’t even have to ask the question (and you know that if you did, almost every client, supplier, and partner would say they trusted them).

Remember this because
1) if you start seeing too many posts on how a certain company is one you can trust or
2) you have to ask if you can trust the company
you probably can’t!

Companies generally start pushing “trust” when a major competitor does something particularly untrustworthy that becomes public, third party surveys paint them as trustworthy, or they need a new angle to boost sales.

Plus, f you need to ask, something is setting off your internal alarms and you won’t trust them until you figure out what that is (and they’re not going to tell you).

Either way, play it safe and look elsewhere.

You may still get burned (and I have the scars to prove it), because sh!t happens, boards make changes, investors get ruthless, and world class pathological liars could still slip through the cracks and fool everyone for years, but you decrease your chances of being burned significantly by just looking for vendors who continually do the right thing (instead of just saying they do).