When it comes to technology, I’m an optimistic fellow. Not only do I believe the ongoing innovation in tech is inexorable, I’d go so far as to posit that technology is the key to solving all the ills of the human race, from global warming to income inequality.
Sometimes, however, I get into a crabby mood, as everyone does from time to time. In those dark times, I divine the wretched underbelly of all the technology progress that has buoyed my career and so many of yours lo all these years.
Here, then, are my most pessimistic takes on modern tech – not because I truly believe these Debbie downers, but rather because I’m taking the opposing viewpoint as though this were a high school debate competition.
Or maybe I’m just having a bad day.
Downer #1: Another AI winter is coming
The maelstrom of innovation, hype, and funding continues to swirl around artificial intelligence (AI). You might think such innovation is moving forward at a fast clip, but in reality, true innovation has stalled, just as it did in the 1980s.
Sure, there are incremental improvements here and there, and we’re getting better at throwing ever larger data sets at our AI models, which improves the end results.
But we still have the same cast of characters we’ve had since the last AI winter – machine learning, deep learning, natural language processing, neural networks, and a few others. It’s been years since anyone has come up with a disruptively new idea.
Maybe there aren’t any to be had?
Downer #2: Blockchain simply doesn’t work
Blockchain has been on the workbench for several years now, but companies that offer blockchain-based solutions that run at scale are scarcer than hen’s teeth. What’s up with that?
Even Bitcoin – the best known blockchain proof of concept – faces its own scalability and management issues. Transactions can take many minutes (or hours?) to complete, miners suck up more electricity than Ireland, and the entire kit and caboodle has become a playground for organized crime.
Sure, it’s possible to strip away all the bits of the blockchain story that make it impractical, in order to end up with something that works. But that something is little more than a secure distributed database. We’ve had those for years, and we certainly don’t need blockchain to make them work.
Downer #3: Hackers are winning the cybersecurity battle
To secure an enterprise, you must secure every entry point and vet every employee, and still you have to cross your fingers. In contrast, the bad guys simply need to find one way in to run off with the crown jewels.
The weak point in every organization, of course, is its people. Especially Madge in accounting and Bruce in marketing, who will click on any link in any email they get from anywhere in the world.
Sure, vendors are furiously innovating in the cybersecurity space, and now it’s de rigueur for that cybersecurity tool to sport high-powered AI sure to nab the malefactors in the act.
Just one problem: the hackers are using AI as well. Madge and Bruce won’t know what hit them.
Downer #4: Technology is useless against fake news
Fake news threatens the very democracies the modern world order depends on, but we techies are at a loss as to how to address the problem.
Wouldn’t it be great if we had a ‘veracity button’? Simply push the button and your browser would automatically highlight (or better yet, delete) all the fake news that comes your way.
Just one problem: we don’t have a clue how to build such a thing. The best we can do to ensure data veracity with today’s tech is verify the source of information, or perhaps single out data that are inconsistent with our other data.
Even our best AI, however, has no idea what is actually true in the real world, and thus has no way of evaluating if some bit of news is fake or not.
Downer #5: Social media are making everyone antisocial
I remember the early days of social media in the early 2000s – what we naively called ‘Web 2.0.’ Pundits were falling over themselves to tout this new world order of communication and collaboration. Social media were supposed to bring us all together into one global melting pot of mutual understanding.
Only the opposite happened. You and your closest buddy from high school befriended each other on Facebook, only to realize you now have differing political opinions. Today you won’t speak to each other.
Now, multiply that problem by six billion. Exposure to differing viewpoints doesn’t lead to calm discussion after all. It only leads to recrimination and polarization.
Of course, it’s all the fault of the people who don’t agree with me, am I right?
Downer #6: Ancient, fragile legacy applications still run our enterprises
In the world of enterprise IT, we love talking about all the hot new trends in big company tech. Multicloud. Hybrid IT. Serverless computing. Containers and microservices. AI-driven big data analytics. The list goes on and on.
However, in spite of all this innovation and concomitant hullaballoo, if you scratch the surface of any big company’s tech, what do you find underneath?
An ancient ERP system. Even the name ‘enterprise resource planning’ brings back memories of data entry hacks pecking away at green screen terminals. Along with ERP are a plethora of TLAs (that is, three letter acronyms) like SCM. HRM. BPM. CRM. The list goes on and on.
Sure, vendors born in the 21st century have rolled out perfectly adequate, cloud-native alternatives to these dinosaurs, and to be sure many companies have cut the cords to their 1980s tech.
Ha! Who are we kidding? Those monolithic monstrosities are still there, chugging away – and we daren’t touch them for fear that everything will come crashing down around us.
Downer #7: Digital transformation is too difficult
If you listen to the pundits – you know, people like us – you’ll soon realize that simply updating your technology to the latest ‘digital’ version isn’t digital transformation at all. That’s digitalization.
If only digital transformation were that easy. But no! It requires that people change – people across your entire organization.
Good luck with that. Sure, people can take classes and go through orientation sessions out the wazoo in order to update their day-to-day work habits, but we all know the only way to exact real organizational change is to wait for everybody to die, and then replace them with new people who aren’t even born yet.
No sense waiting around for that, of course – since we’re all on the short end of that stick.
The Intellyx Take
OK, I feel better. I’m less crabby already just getting all that off my chest.
As I said at the top, I don’t really believe any of it – at least most of the time. But when I wake up at two in the morning in a cold sweat, wondering what the meaning of life is? That’s when it all makes sense to me.
In all seriousness, all of the considerations I list in this Cortex are valid concerns that many people have – perhaps more people than we’d care to admit. Unless we air such concerns, we deprive ourselves of the opportunity to discuss them, and hopefully dispel them, at least in part.
So feel free to disagree – but do so thoughtfully. If you do, this article will have been successful.
Copyright © Intellyx LLC. Intellyx publishes the Agile Digital Transformation Roadmap poster, advises companies on their digital transformation initiatives, and helps vendors communicate their agility stories. As of the time of writing, none of the organizations mentioned in this article are Intellyx customers. Image credit: Jelene Morris.
Finally, a thoughtful article about the actual history of technological innovation. Someone who has a historical understanding of what we face. I was COO of MIndmaker in the early 1990s. We won “Best of Comdex” two years in a row. Back then this meant something, but today not many people know about the Comdex Event. Back then we were the A/I company. Guess what: Watson and the other A/I efforts of today are only doing what we were doing back then, almost 25 years ago. NO real progress in A/I yet. Same ole, same ole. But there is a lot of market hype.
This blog says it all. Thinking about Newkirk’s Law #3: Every advance in Science is preceded by an advance in methodology; every advance in methodology is preceded by advance in Philosophy; every advance in Philosophy is preceded by an advance in human curiosity about generality and specificity. Unfortunately, only about 42% of supposed scientific discoveries are repeatable. THIS IS NOT GOOD. So everyone begins to belief the hype….without understanding the technologies. I wrote a book about this in 1977. It has come true. Darn it. This is really a good blog by someone who understands the difficulties before us. We face many opportunities for progress. Really. Someone has to clean up the mess. Quantum Computing may have promise, goodbye blockchain….Ray Newkirk
Jason, this is a really good rant. But I am not sure that I believe you when you claim that you don’t really believe any of what you had written above, which would imply that you are still stuck in the first stage of the Kübler-Ross model, and you wouldn’t allow yourself to be stuck there for very long at all.
May I suspect that you actually do believe those seven downers to be a broadly accurate description of the state of things, but that you are still optimistic that all of those are just temporary obstacles that will be overcome in the end. I am with you on the first part, and am not sure yet how permanent the obstacles are going to be. Certainly the solutions aren’t obvious.
Your article makes an important contribution in that it spells out those downers. That helps all of us to move to stage five of Kübler-Ross, and start discussing how exactly each of these problems can indeed best be overcome.
Along those lines, let me suggest Downer #8: As systems become more complex and integrated, they are less flexible and harder to change.
It used to be that data was transferred manually, or at least with a lot of human control and checking, which could correct any errors and apply any implicit knowledge of how the data is really supposed to show up in the receiving system. That is no longer true when all data is processed and propagated automatically, and at some point we just have to trust the results. We are gaining speed and efficiency but we may just be building the newest generation of legacy applications whose behavior we can no longer understand, and therefore also will be afraid to ever replace. Throw AI in the mix, and the problem really becomes intractable.