Is AI Really Moving Too Fast? Or Are We Just Not Ready For It?

AI seems to be everywhere these days. It’s in our offices, on TV ads, on the streets, and if you’re online as much as I am, it’s all over the internet. The technology is developing at a blinding pace. But it begs the question: is AI moving too fast?

Every few weeks, we hear about a new AI startup popping up, or a shiny new model being launched. Panic quickly follows as businesses rush to figure out how to use it, while consumers voice their confusion and concerns. This cycle has become familiar over the past few years.

AI is undoubtedly advancing, but is it really the game-changer that big tech wants us to believe, or are we just being dazzled by speed and hype?

Faster Models, Slower Adoption

As mentioned earlier, the pace of AI development is nothing short of insane. In just a few months, new models surge ahead in capability, often making previous versions look outdated overnight. People, by contrast, take years to learn, adapt, and integrate new tools into their routines. For example, many organisations were slow on the uptake on video conferencing. Only after the 2020 pandemic did we see a massive normalisation of videotelephony.

This gap isn’t just a one-off coincidence, it shows up in how we actually use AI. Most interactions remain shallow, experimental, or inconsistent. We try it out, marvel at what it can do, and then move on, without really understanding its full potential.

That raises a bigger question: what happens when technology outpaces understanding? People may skip over the latest AI releases not because they aren’t interested, but because they haven’t fully grasped the power, or the limitations, of what came before. Every new model promises breakthroughs, but without time to absorb and adapt, we risk piling new tools on top of incomplete knowledge.

And that’s where the tension lies: AI can sprint ahead, but human understanding and human systems can’t always keep pace. The result is a world where powerful tools exist, but we’re not yet sure how to use them responsibly, effectively, or safely.

Adoption Isn’t Just Slow, It’s Uneven

Despite vocal advocates highlighting the benefits of AI, not everyone is eager to jump on board. Some teams have fully embraced AI, restructuring workflows and building systems around it to boost efficiency and expand what they can do. Others have gone in the opposite direction, banning AI tools entirely and choosing to rely on traditional methods instead. Then there’s a large group caught somewhere in the middle; organisations with no clear policy, unsure whether to commit fully or proceed with caution.

For those who have invested the time to understand AI deeply, the rewards can be significant. They’ve developed processes that automate repetitive tasks, introduced new capabilities into their work, and reshaped how teams operate. But these success stories often belong to a smaller group who have managed to navigate the steep learning curve and experiment extensively.

For many others, AI remains more of a novelty than a necessity — something to tinker with rather than fully integrate. The gap between experimentation and meaningful adoption continues to widen, leaving organisations unsure where they stand.

Uneven adoption, of course, is nothing new. Nearly every major technological shift has faced resistance, confusion, and debate before becoming mainstream. Yet AI feels different in one key way: the hesitation isn’t just about learning a new tool, it’s about trusting systems that can produce convincing outputs without always being transparent or reliable.

So is this simply the messy early phase of a transformative technology? Or does it point to a deeper lack of trust, fueled by unclear guidelines, inflated expectations, and growing uncertainty about when AI is helpful, when it’s flawed, and who is ultimately responsible for its mistakes?

The Trust Issues

AI’s biggest issue right now might not be what it can do, but whether we trust it enough to let it do those things. One of the quirks of AI is that it sounds incredibly confident, even when it’s completely off the mark. It doesn’t hesitate, it doesn’t second-guess itself, and it certainly doesn’t say, “I’m not sure.” It only really tends to acknowledge these mistakes when a user points them out. One high profile example came from the US where ChatGPT once gave a lawyer citations from cases that did not exist.

The speed doesn’t help either. AI generates answers instantly, which makes it tempting to accept the output and move on. But when people don’t fully understand how a response was formed, or assume it must be correct because it sounds polished, mistakes can spread quickly. Some AI models are better than others in this regard. Claude, for example, tends to let the user know exactly what it is analysing to give you its answer.

On the other hand, when things go wrong, they tend to go wrong loudly. A single viral example of an AI blunder can travel across the internet in hours, undoing months of quiet progress behind the scenes. The technology might work well most of the time, but people tend to remember the spectacular failures more than the everyday successes. Once trust starts to wobble, everything else gets harder. Teams become hesitant, policies get stricter, and even genuinely useful tools are met with a raised eyebrow.

The Obscene Cost Of AI

For all the excitement around AI, there’s one awkward detail that doesn’t get talked about enough: it’s incredibly expensive to run. Training models takes enormous computing power (and apparently, a lot of water as well), maintaining them requires constant updates, and scaling their capabilities means even more infrastructure. Behind every seemingly effortless AI response sits a mountain of servers quietly burning through electricity and budgets.

To be fair, AI is making decent money. But as revenue grows, so do the costs. Infrastructure expenses, research investments, and the race to stay ahead of competitors make profitability a complicated and ongoing challenge. For investors expecting quick and massive returns, the reality may feel less glamorous than the headlines suggest.

And where investors want growth, companies feel pressure to deliver fast. New features are pushed out quickly, new models are launched frequently, and everyone races to be first rather than cautious. But speed can be a double-edged sword. Shipping products before they are fully understood or trusted may result in the slow adoption AI companies want to avoid in the first place.

In that sense, the cost of AI isn’t just financial. The rush to justify enormous investments can encourage companies to move faster than users are ready for, widening the gap between technological capability and real-world confidence.

Backlash And Resistance

Not everyone is welcoming AI with open arms. Many creators and consumers have pushed back, uneasy about how quickly the technology is spreading and how it’s being used. Workers worry about being replaced or having their roles quietly reshaped, while everyday users hesitate, unsure whether AI is genuinely helpful or just another system collecting their data and changing how they work and live.

Some of this resistance may simply be fear of change. After all, every major technological shift has faced scepticism in its early days. But the speed and scale of AI’s rollout feel different. When change arrives faster than people can understand or adapt, hesitation starts to look less like stubbornness and more like caution. The question then becomes: is this just natural resistance, or a sign that the shift is happening faster, and more forcefully, than many are ready for?

After all the debates, panic, and pushback, it’s worth asking whether “Is AI moving too fast?” is even the right question. Speed alone doesn’t tell the full story. Technology rarely moves at the same pace for everyone, and what feels overwhelming to one group may feel overdue to another.

Maybe the more useful questions are: too fast for who? Too fast for workers trying to keep up with shifting expectations? Too fast for businesses still figuring out policies and responsibilities? Or perhaps too fast for the systems meant to catch mistakes before they cause real harm?

Framing the conversation around speed alone risks oversimplifying a much messier reality. The real issue might not be whether AI is moving quickly, but whether the people, processes, and safeguards around it are evolving fast enough to keep things under control.

That is not to say that everyone is against the use of AI. NVIDIA’s CEO, Jensen Huang is noted to have praised Claude immensely over its reasoning and programming ability, which greatly helps NVIDIA’s business.

Just Keep Swimming

Whether we like it or not, AI will keep improving. The pace may change and opinions may swing, but the technology isn’t going away anytime soon. The real question isn’t whether AI will evolve, it’s how we choose to live and work alongside it.

For the most part, people seem to not want AI to take over everything. Rather, they want AI to do the mundane and tedious work so that they themselves can focus on doing the things they love and enjoy.

Right now, it’s impossible to say if the current path is right or wrong. We’re still in the middle of figuring things out. What’s clear, though, is that progress isn’t just about what AI can do, it’s about how we use it, and how we respond when things don’t go as planned.