Your Large Language Model – it’s as Dumb as a Rock

© Jason Flaks -Initially generated by DALL-E and edited by Jason Flaks

Unless you’ve been living under a rock lately you likely think we’re entering some sort of AI-pocalypse. The sky is falling and the bots have come calling. There are endless reports of ChatGPT acing college-level exams, becoming self-aware, and even trying to break up people’s marriages! The way  OpenAI and their ChatGPT product have been depicted, it’s a miracle we haven’t all unplugged our devices and shattered our screens. It seems like a sensible way to stop the AI overlords from taking control of our lives.

But never fear! I am here to tell you that large language models (LLMs) and their various compatriots are as dumb as the rocks we all might be tempted to smash them with. Well, ok, they are smart in some ways. But don’t fret—these models are not conscious, sentient, or intelligent at all. Here’s why.

Some Like it Bot: What’s an LLM?

Large Language Models (LLMs) actually do something quite simple. They take a given sequence of words and predict the next likely word to follow. Do that recursively, and add in a little extra noise each time you make a prediction to ensure your results are non-deterministic, and voila! You have yourself a “generative AI” product like ChatGPT.

But what if we take the description of LLMs above and restate it a little more succinctly:

LLMs estimate an unknown word based on extending a known sequence of words.

It may sound fancy—revolutionary, even—but the truth is it’s actually old school. Like, really, really old school—it’s almost the exact definition of extrapolation, a common mathematical technique that’s existed since the time of Archimedes! If you take a step back, Large Language Models are nothing more than a fancy extrapolation algorithm.  Last I checked nobody thinks their standard polynomial extrapolation algorithm is conscious or intelligent. So why exactly do so many believe LLMs are?

Hear Ye, Hear Ye: What’s in an Audio Sample

Sometimes it’s easier to explain a complex topic by comparison. Let’s take a look at one of the most common human languages in existence—music.  Below are a few hundred samples from Bob Dylan’s “Like a Rolling Stone.” 


If I were to take those samples and feed them into an algorithm and then recursively extrapolate out for a few thousand samples, I’d have generated some additional audio content. But there is a lot more information encoded in that generated audio content than just the few thousand samples used to create it.

At the lowest level:

  • Pitch
  • Intensity
  • Timbre

At a higher level:

  • Melody
  • Harmony
  • Rhythm

And at an even higher level:

  • Genre
  • Tempo

So by simply extrapolating samples of audio, we generated all sorts of complex higher-level features of auditory or musical information. But pump the brakes! Did I just create AI Mozart? I don’t think so. It’s more like AI Muzak.

An AI of Many Words: What’s Next? 

It turns out that predicting the next word in a sequence of words will also generate more than just a few lines of text. There’s a lot of information encoded in those lines,  including the structure of how humans speak and write, as well as general information and knowledge we’ve previously logged. Here’s just a small sample of things encoded in a sequence of words:

  • Vocabulary
  • Grammar/Part of Speech (PoS) tagging
  • Coreference resolution (pronoun dereferencing)
  • Named entity detection
  • Text categorization
  • Question and answering
  • Abstract summarization
  • Knowledge base

All of the information above can, in theory, be extracted by simply predicting the next word, much in the same way predicting the next musical sample gives us melody, harmony, rhythm, and more.   And just like our music extrapolation algorithm didn’t produce the next Mozart, ChatGPT isn’t going to create the next Shakespeare (or the next horror movie villain, for that matter).

LLMs: Lacking Little Minds? 

Large Language Models aren’t the harbinger of digital doom, but that doesn’t mean they don’t have some inherent value. As an early adopter of this technology, I know it has a place in this time. It’s integral to the work we do at Xembly, where I’m the co-founder and CTO. However, once you understand that LLMs are just glorified extrapolation algorithms, you gain a better understanding of the limitations of the technology and how best to use it. 

Five Alive: How to Use LLMs So They Don’t Take Over the World


LLMs have huge potential. Just like any other tool, though, in order to extrapolate the most value, you have to use them properly. Here are five areas to consider as you incorporate LLMs into your life and work. 

  • Information must be encoded in text
  • Extrapolation error with distance
  • Must be prompted
  • Limited short-term memory
  • Fixed in time with no long-term memory

Let’s dig a little deeper.

Information Must Be Encoded in Text

Yan LeCun probably said it best:

Humans are multi-modal input devices and many of the things we observe and are aware of that drive our behavior aren’t verbal  (and hence not encoded in text). An example we contend with at Xembly is the prediction of action items from a meeting. It turns out that the statement “I’ll update the row in the spreadsheet” may or may not be a future commitment to do work.  Language is nuanced, influenced by other real-time inputs like body language and hundreds of other human expressions. It’s entirely possible in this example that the task was completed in real-time during the meeting, and the spoken words weren’t an indication of future work at all.

Extrapolation Error with Distance

Like all extrapolation algorithms, the further you get away from your source signal (or prompt in the case of LLMs), the more likely you will experience errors. Sometimes a single prediction that negates an otherwise affirmative statement or an incorrectly assigned gendered pronoun, can cause downstream errors in future predictions. These tiny errors can often lead to convincingly good responses that are factually inaccurate. In some cases, you may find LLMs return highly confident answers that are completely incorrect. These types of errors are referred to as hallucinations.

But both of these examples are really just forms of extrapolation error. The errors will be more pronounced when you make long predictions. This is especially true for content largely unseen by the underlying language model (for example, when trying to do long-form summarization of novel content).

Must Be Prompted

Simply put, if you don’t provide input text an LLM will do nothing. So if you are expecting ChatGPT to act as a sage and give you unsolicited advice, you’ll be waiting a long time. Many of the features Xembly customers rave about are based on our product providing unsolicited guidance. Large Language Models are no help to us here.

Limited Short-Term Memory

LLMs generally only operate on a limited window of text. In the case of ChatGPT, that window is roughly 3000 words. What that means is that new information not already incorporated in the initial LLM training data can very quickly fall out of memory. This is especially problematic for long conversations where new corporate lingo may be introduced at the start of a conversation and never mentioned again. Once whatever buzzword is used falls out of the context window it will no longer contribute to any future prediction, which can be problematic when trying to summarize a conversation.

Fixed in Time with no Long-term Memory

Every conversation you have with ChatGPT only exists for that session. Once you close that browser or exit your current conversation, there is no memory of what was said. That means you cannot depend on new words being understood in future conversations unless you reintroduce them within a new context window. So, if you introduce an LLM to anything it hasn’t heard before in a given session, you may find it uses that word correctly in subsequent responses. But if you enter a new session and have any hopes that the word will be used without introducing it in a new prompt, brace yourself—you will be disappointed.

To Use an LLM or Not to Use an LLM

It’s a big question. LLMs are exceedingly powerful, and you should strongly consider using them as part of your NLP stack. I’ve found the greatest value of many of these LLMs is that they potentially replace all the bespoke language models folks have been making for some time.  You may not need these custom entity modes, intent models, abstract summarization models, etc. It’s quite possible that LLMs can accomplish all of these things at similar or better accuracy, while possibly greatly reducing time to market for products that rely on this type of technology.  

There are many items in the LLM plus column, but if you are hoping to have a thought-provoking intelligent conversation with ChatGPT,  I suggest you walk outside and consult your nearest rock. You just might have a more engaging conversation!

“If you start me up I’ll never stop …” Until We Successfully Exit

“Hey, our fledgling startup is on path to being the next *INSERT BIG TECH COMPANY NAME HERE* and we think you’re a great fit for our CTO role”. Find me a technical leader who hasn’t been enticed by those words and you’ll have found a liar. So, what happens when one succumbs to the temptation and joins an early-stage startup? Well, if you have been wondering where I’ve been for the past couple of years, I was fighting the good fight at a small, early-stage NLP/Machine Learning based risk intelligence startup. And while I’m not retired or sailing around the world in my new 500-foot yacht, we were able to successfully exit the company with a net positive outcome for all involved. My hope with this post is that I can share some of my acquired wisdom, and perhaps steer the next willing victim down a similar path of success.


If I could sum up my key learnings in a few bullet points, it would boil down to this:

  • If you don’t believe … don’t join
  • Be prepared to contribute in any way possible
  • Find the product and focus on building it
  • Pick the race you have enough fuel for and win it


What I’d like to do in the rest of this post is break down each one of these items a little further.

If you don’t believe … don’t join


Maybe this goes without saying, but if you don’t believe in the vision, the people, and the product you shouldn’t join the startup approaching you. The CTO title is alluring, and it is easy to fool yourself into taking a job for the wrong reasons. But the startup experience is an emotional slog of ups and downs and it will be nearly impossible to weather the ride if you don’t wake up every day with an unyielding conviction for what you’re doing. As I’ll explain later in this post, you don’t need to believe you’re working for the next Facebook, but you do need to believe you are building a compelling product that has real value for you, your coworkers, your investors, and your customers.

Be prepared to contribute in any way possible


For the first few months on the job I used to go into our tiny office and empty all the trash bins because, if I didn’t, that small office with 5 engineers started to smell! It didn’t take long for someone to call out that I was appropriately titled, CTO (a.k.a. Chief Trash Officer). You might be asking why anybody would take a CTO job to wind up being the corporate custodian, but that is what was needed on some days.


While I have steadfastly maintained my technical chops throughout my career, I hadn’t really written a lick of production code for nearly two decades prior to this job. But with limited resources, it became clear I also needed to contribute to the code base and so I dusted off those deeply buried skills and contributed where I could. When you join a startup with that CTO title, it is easy to convince yourself that you’ll build a huge team, be swimming in resources, and have an opportunity to direct the band versus playing in it. But you’ll quickly find that in the early stages of a startup, the success of the company will depend on your willingness to drop your ego and contribute wherever you can.

Find the product and focus on building it


Great salespeople can sell you the Brooklyn Bridge. And if you’re just lucky enough, you might have a George C. Parker in your ranks. But the problem with great salespeople is they will do almost anything to close the sale and that comes with a real risk that they’ll sell custom work. If that happens over an extended period of time, you will be unable to focus on the core product offering and you’ll quickly find you’re the CTO of a work-for-hire / consulting company.


Startups face real financial pressures that often drive counterproductive behaviors. That often means doing anything necessary to drive growth in revenue, customers, or usage. But as illustrated in the graph below, high product variance will often ultimately lead to stagnant growth.

That’s because with every new feature comes a perpetual support cost. And if you keep building one-off features, and can’t fundraise fast enough, that cost will eventually come at the expense of delivering your true market-wide value proposition. If you allow this to happen, you’ll wind up with a company that generates some amount of revenue or usage but has no real value.


Companies that find true product/market fit should see product variance gradually decrease over time and this should allow the company to grow. Your growth trajectory may be linear when you need it to be exponential, but no per customer feature work will fix that problem and you may need to consider pivoting. If pivoting isn’t an option, it may be time to look for an exit.

As the CTO, a critical part of your job is to help the company find its product/market fit and then relentlessly focus on it. You need to hold the line against distractions and ensure the vast majority of resources are spent on features that align with the core value proposition. If you’ve truly found a product offering that is valued by a given market segment, and you can keep your resources focused on building it, growth will follow.

Pick the race you have enough fuel for and win it

I am an avid runner, and one of the great lessons of long-distance running is, that if you deplete your glycogen store, you’ll be unable to finish the race no matter how hard you trained. In other words, you can’t win the race if you have insufficient fuel. This is also very true of startups. If you’re SpaceX or Magic Leap, you’re running an ultra-marathon and you need a tremendous amount of capital in order to have sufficient time and resources to realize the value. But fundraising is hard, and even if you have an amazing product and top-notch talent, there can be significant barriers to acquiring sufficient capital.


The mistake some startups make is that they continue to run an ultra-marathon when they only have fuel for a 5k and that can lead to a premature or unnecessary failure. If funding becomes an issue, start looking for how your product might offer value to another firm. Start allocating resources towards making the product attractive for an acquisition. Aim to win a smaller race and seek more fuel on the next go around.

Final Thoughts


Taking on a CTO role at an early stage startup can be a great opportunity and lead to enormous success, but before you take the leap make sure you know what you’re getting into. Along the way don’t forget to stop and smell the roses. In the words of fellow Seattle native Macklemore, “Someday soon, your whole life’s gonna change. You’ll miss the magic of these good old days”.

Final Final Thoughts


No startup CTO is successful without support from an army of people. So I’d like to offer some gratitude to the following folks:

  • Greg Adams, Christ Hurst: Thanks for giving me an opportunity and treating me like a cofounder from day one.
  • Shane Walker, Cody Jones, Phil LiPari, Pavel Khlustikov, David Ulrich, Julie Bauer, Jason Scott, Carrie Birmingham, Rich Gridlestone, Bill Rick, Zach Pryde, Amy Well, David Debusk, Mikhail Zaydman, Jean-Roux, Bezuidenhout, Sergey Kurilkn (and others I may have forgotten): Thanks for being one the greatest teams I’ve ever worked with.
  • Brandon Shelton, Linda Fingerle, Wayne Boulais, Armando Pauker, Matt Abrams, Matthew Mills: Thank you for being outstanding board members, mentors, and investors
  • Ziad Ismail, Pete Christothoulou, Kirby Winfield: Thank you for the career advice during my first venture into the startup world.

*Note: You can read more about Stabilitas, OnSolve, and our acquisition at the links below:

https://www.onsolve.com/solutions/products/stabilitas/

https://www.geekwire.com/2020/seattle-based-threat-response-startup-stabilitas-acquired-onsolve/