Part 3: Staying sane during (AI) Uncertainty: Be Highly Aware. Act Normal.
Three steps to survive AI insanity, FOMO and urgency.
Crossing a “shaky” suspension bridge feels terrifying the first time. The second time, it’s pretty normal. Rationality sets in and blows away the FUD of emotions. You will not die!
You feel the insane drop beneath you, the planks are wobbling, cables are swaying, wind is catching you. Your soul and body are exposed. Fear is 100% natural. Still nearly everyone makes it across and once you’ve done it, the fear is replaced by familiarity. The exposure is normalized.
That’s where we are with AI. It feels dangerous, unpredictable, full of risk. FOMO and urgency escalate. But then, step by step, it becomes normal.
While the last two posts laid out the context of wild predictions and opened up the parallel to 1995 – pre the Internet revolution – this post is about that paradox: why awareness is a must, but panic is useless. Why hype and prophecy don’t help, but hands-on learning does. What helps is trusting your acquired foundation and being hyper aware of new options and grabbing some once a good one comes around the corner.
Stuck between hype and disillusion: Discovery time, not ROI
The numbers seem damning: According to most of the studies, the value creation around AI projects in companies fails in 80% – 95% of cases / projects. Some people think that’s a terrible number. I think that’s normal. People are learning when and where to apply AI step by step.
The first wave was simply using LLMs directly and wrapping a conversational interface or a chatbot around whatever service or product one provided. It’s the most straightforward, most direct way. But most of the time this simply ignores the customer: Does the customer want to have non-deterministic behavior, does he want a conversational interface? Does it make things better or worse? Or does it solve our own problem as a provider, that we actually don’t really know what the customer wants and think the LLM will figure it out and thus customize our mass product for our customer. But that’s not how things work. It does not solve a new customer problem. Mostly it solves an old problem, but worse. Gartner sees this wave of GenAI already on the “Trough of Disillusionment.”
The hype thing these days – still on the up – are agentic systems. As I said: Agents always follow quickly, because it’s a very tech idea. We can do it, so we build it and it satisfies the tech world dream of building autonomous machines. But for what? While this might open up new use cases, it will certainly not satisfy end users’ needs, given the limitations of LLMs listed above. Except in non-mission critical contexts. But then, it’s also really complicated.
The topics “agentic” and “AI-ready data” (being the basis of the work of agents) are listed in the phase of “Peak of Inflated Expectations.”
Vibe coding is already declining. And while the talk of the town is getting rid of those nasty human devs and customer agents, reports of what’s working successfully and sustainably are not overwhelming. A ton of CTOs are probably currently recalculating their 2026 budgets.
Interlude: price of tokens?
A funny, not often talked about side topic that I really like is the price of tokens. Currently no one selling tokens is making money off them. All of them are burning money and no one has a clue how OpenAI will ever become cash positive. The current business model is hope, and I don’t mean this cynical or negative. We need those pioneers. But in my fantasy, the day will come where a) those providers will realize that there is a certain vendor lock-in and b) that they could raise the prices for their tokens. Just like in cloud computing, the cost will rise. In cloud, companies first found that having people simply trigger the next instance and all that dynamic being unmonitored had a huge negative effect on cost (COGS). Providers realized that packaging and bundling and other ways of nifty price increases are a big deal. The same will happen to the price of tokens once the lock-in is sealed. Let’s see what this means for the millions of tokens that companies now blow liberally through the ether.
AI won’t go away. Just like the Net in 1995. The problem is you don’t yet know what works for you. Just like us in 1995. The way to deal with it is: go play. It is 100% essential that you make your whole company AI knowledgeable. It does not have to be very directed. You just mingle in AI and observe and become AI-literate. If you see one, commit on a small bet. And another one. You just need to show up, stay close to the development. You cannot miss it.
Three steps to keep up
1 – Immerse the whole company in AI
Involve the whole company in understanding AI. From payroll (“how can we reduce unnecessary manual routine labor?”) over marketing (“automated market and channel tracking?”), support (“which tickets are repetitive enough for AI to handle and can engineering help us automate?”) to finance (“in which areas of reporting can AI’s pattern recognition help us without dangers of hallucinations?”) and engineering (“how can we support other departments with our tech knowledge in reducing toil and how can we responsibly handle code with the support of AI, where does AI completely change our technical options?”).
2 – Treat it as discovery, not instant ROI and extraction
It will feel unsatisfactory. To some it will feel too fast, to some too slow. Too extreme, too undecided. It doesn’t matter too much. By focusing inside on reducing repetitive human labor first, you will instantly see some very pragmatic benefits and learn the technology and also its boundaries hands-on rather than from paper or LinkedIn half-knowledge.
By immersing yourself, be highly aware of bigger options to use your acquired knowledge towards outside-facing options: to really improve your customers’ lives.
This requires a strong stance on the next point:
3 – Stay anchored on your original vision and customers
All of this works only if you are stable in your vision and strategy. Stick to your vision. Stick to your niche, to the customer problems you want to solve. It’s tempting to get distracted by new options. It’s expensive and mostly damaging to simply switch the problem space. Your customer knows you for a certain problem you are solving. Stick to your long-term goals and be creative on short/mid-term options where there is an obvious bet. Stick to the rule that a good option has huge upside and low downside. Which firstly means that it can’t sink the ship. Don’t be blinded by tech optimism and change long-term trajectory for short-term enthusiasm-based tech solutions. It never pays off. Discipline is the word. It now pays off to have been explicit in your vision and what makes you different from the competition. Don’t surrender those past efforts.
Stay aware, act normal
Those who had the first successes after 1995 did not know what they were doing. They totally fulfilled the
“Stockdale Paradox”: “confronting the brutal facts of your current reality while maintaining unwavering faith that you will prevail in the end”!
Another one of those dialectic truths. Go all in with heads off, not knowing the outcome but hoping for the best. It sounds like handwaving and haha-ing our way through life, but remember: there is no certainty except: AI is here to stay and you better learn to co-exist. But there is no direct way to making it a success. You need to learn and be aware.
When the only certainty is uncertainty: Don’t listen to the prophets – instead immerse yourself in the uncertain reality (you know it best), look for small bets, fast learning, and the humility to be wrong. That’s how revolutions are not only survived – but used for your good.