Why AI tools seem impressive at first but fade over time
Have you ever experienced the excitement of trying out a new AI tool? The initial launch of an AI application often feels revolutionary. It dazzles with its capabilities, solves problems you didn’t know you had, and seems poised to transform the way you work. For a few days or even weeks, it becomes your go-to solution for whatever task it promises to address.
But then, something changes. The initial thrill fades, and your use of the tool diminishes. Responses that once seemed insightful or helpful start to feel generic or, worse, incorrect. Slowly but surely, the tool becomes just another app gathering dust in your digital toolkit.
This cycle isn’t unique - it’s a pattern we’ve seen across the AI landscape, especially with consumer-facing tools. What starts as a "game-changer" often devolves into a source of frustration. So, what’s going on here?
The honeymoon period
When we first try out a new AI tool, we’re typically exploring its best-case scenarios. Developers design these tools to shine in specific tasks or contexts, often showcased in carefully curated demos or marketing materials. We’re encouraged to test them with relatively straightforward use cases, where they excel and impress.
In this phase, novelty and the tool’s apparent ability to "understand" us manage our expectations. Its responses feel fresh, and we’re inclined to forgive any minor shortcomings as we’re still learning to use it effectively.
The slow decline
As time passes, we naturally start pushing the tool’s boundaries. We feed it more complex, nuanced, or less predictable inputs, and its limitations become glaringly obvious. Responses that seemed innovative before now feel repetitive or superficial. Worse, the AI might start making outright errors or producing irrelevant, biased, or nonsensical results.
Even when the tool isn’t outright failing, its value often diminishes as we become more familiar with its quirks and workarounds. What initially felt like a productivity boost can begin to feel like extra effort - revising its outputs, double-checking its facts, or constantly prompting it in just the right way.
AI tools replacing developers
One of the most ambitious promises of AI tools is their potential to replace developers. Tools like Devin AI are marketed as solutions that can handle tasks traditionally performed by human programmers. At first glance, they seem promising - generating code snippets and automating repetitive tasks. But for anyone who has used these tools extensively, the reality is far less perfect.
AI-generated code often comes with a host of issues. Users find themselves spending as much time writing precise prompts and debugging the AI’s output as they would if they had written the code themselves. This is the problem because developers like writing code, more than chatting with AI or debugging others' mistakes.
Even high-profile figures like Mark Zuckerberg have weighed in on the future of AI and software development. Recently, he suggested that during 2025, AI agents will write code on par with mid-level developers, predicting that a significant portion of Meta’s codebase will be AI-generated. I don’t agree with that prediction, AI tools are currently too far away from being independent in their work, a characteristic that is important for mid-level developers. And even if AI reaches that level, it raises critical concerns about the downstream effects on the development process.
For instance, if AI tools begin generating a substantial portion of the code, the responsibility of reviewing and refining that code will likely fall on senior developers and tech leads. Unlike reviewing code from human developers, reviewing AI-generated code lacks the mentorship and collaborative aspects that make the process rewarding. There’s no sense of pride in seeing an AI tool "improve" over time or watching it grow into a more capable contributor. Instead, the task risks becoming tedious, monotonous, and demoralizing.
Imagine a future where a single tech lead oversees six AI agents working on various tasks. This setup might sound efficient on paper, but in practice, it could be far from fulfilling. Without the human connection and opportunities for learning and growth, such a role might feel more like managing machines than leading a team.
For those considering becoming an AI software developer, understanding these limitations is crucial. While AI can automate some coding tasks, it still requires human oversight, creativity, and problem-solving skills. Developers who specialize in AI will not only build these tools but also shape their ethical use and improvement over time.
AI tools should be seen as accelerators for developers, not replacements. Historically, every tool that has allowed developers to work faster - from compilers to IDEs to version control systems - has ultimately led to increased demand for developers, not less. Faster development cycles have been accompanied by more ambitious projects, more complex requirements, and a greater need for skilled professionals.
Instead of replacing developers, AI tools should aim to enhance their productivity and creativity. The future of software development lies not in eliminating human programmers but in empowering them to achieve more.
Self-driving cars: Accountability in the age of automation
Self-driving cars are often touted as the future of transportation - a world where machines, not humans, take the wheel. The promise of reduced accidents, smoother traffic flow, and efficient commutes makes the idea incredibly appealing. Yet, despite the significant advances in autonomous driving technology, a fundamental question looms: who is accountable when things go wrong?
Accidents are inevitable - Even for self-driving cars
Even when self-driving cars become better drivers than the average human, they will still cause some accidents. Perfection is an unrealistic expectation, whether we’re talking about humans or machines. However, the legal and ethical frameworks we use to address human-caused accidents don’t neatly apply to machines.
When a human driver causes an accident, the process is straightforward: investigations are conducted, responsibility is assigned, and in severe cases, the driver may face legal consequences, including imprisonment. But when a self-driving car causes an accident, things become murkier. Is the manufacturer to blame? The software developer? The owner of the car? Or does the fault lie with the algorithm itself?
The liability shield of "supervised" driving
Tesla’s "Full Self-Driving (Supervised)" feature exemplifies the industry’s struggle with this accountability problem. The very term "Full Self-Driving" implies complete autonomy, yet the qualifier "Supervised" signals otherwise. On Tesla’s website, the word "(Supervised)" appears 14 times in a single article, reinforcing the message that these vehicles still require a human’s constant oversight.
Why this emphasis on supervision? Liability. By requiring the driver to remain attentive and ready to intervene, Tesla shifts the responsibility for any accidents back onto the human behind the wheel. Even if the car is making all the decisions, the driver’s role as the "supervisor" ensures that they are legally accountable in the event of a collision.
Shifting the responsibility
This dynamic raises troubling questions. If the car is driving itself and the driver’s primary role is to monitor and intervene only in emergencies, how can we expect humans to maintain the same level of attention as they would when actively driving? Studies on automation complacency suggest that as machines take over more tasks, humans become less vigilant. The more capable the self-driving system, the harder it is for the human to remain alert, which ironically increases the likelihood of delayed intervention when something goes wrong.
From a consumer perspective, this accountability shift feels unfair. If the technology is marketed as "Full Self-Driving," it’s reasonable to assume the car should take full responsibility for its actions. But in practice, this isn’t the case, and manufacturers carefully word their marketing to avoid accepting liability for accidents.
The future of "teams" between humans and machines
Self-driving cars are not just a technological challenge but a societal one. As long as manufacturers insist on "supervised" autonomy, they will continue to rely on humans to shoulder the blame when accidents occur. While this might protect companies from lawsuits, it undermines the trust and clarity needed for mass adoption.
If we’re truly moving toward a future where machines take over the driving, manufacturers must step up and take responsibility for the outcomes of their systems. Until then, self-driving cars will remain stuck in a limbo - not quite autonomous, not quite trustworthy, and not quite ready to take on the responsibility they’ve promised to relieve us of.
The gap between AI improvement and expectations
AI advancements over the past few years have been remarkable, bringing tools that can generate human-like text, create images, and solve complex problems. Yet, despite these breakthroughs, many feel underwhelmed. The reason? Expectations are growing even faster than the technology itself.
The promise of progress
Every new AI tool arrives with bold promises about its future potential. These promises are key to attracting investors and users, but they also create a high bar that the present state of AI often struggles to meet. AI is great for building promising Proof-of-concept applications, which are great for attracting investors to help build the finished product. I’m not saying that there are no good AI products at this moment, but there are far more products that seem like they will be awesome in the future but are just not there yet.
Why disappointment happens
AI capabilities are improving rapidly, but marketing often emphasizes the best-case scenarios, ignoring current limitations. Tools that initially amaze can become frustrating when users encounter their flaws. Promises like “AI will transform industries” or “AI will replace programmers” set unrealistic expectations, making even impressive progress seem inadequate.
Balancing reality and expectations
To bridge the gap, we need to recalibrate:
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Appreciate progress: Celebrate what AI tools achieve today, rather than focusing on what you expect from them in the future.
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Scrutinize claims: Approach marketing promises with skepticism and focus on what tools can realistically deliver.
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Embrace incremental growth: Recognize that meaningful advancements often happen gradually, not overnight.
AI’s potential is vast, but progress is non-linear. By aligning expectations with reality, we can appreciate today’s achievements while staying grounded about the future.
The challenge of ChatGPT Pro and AI tool sustainability
ChatGPT Pro, OpenAI’s premium subscription tier priced at $200 per month, offers advanced features and extensive access to its cutting-edge models. However, despite this hefty price tag, OpenAI has admitted that the service remains unprofitable due to the high usage levels of its subscribers. This raises serious questions about the sustainability of AI tools: if a $200 monthly fee cannot cover operational costs, how can these tools become more affordable while supporting growth?
The problem of cost vs. usage
At its core, the issue with ChatGPT Pro stems from a mismatch between operational costs and user behavior. High-usage users place significant demands on OpenAI’s infrastructure, resulting in expenses that outpace revenues. While the premium pricing may deter casual users, it hasn’t prevented heavy usage from power users, ultimately making the model’s current pricing structure unsustainable in the long term. For AI tools to grow and thrive, they need to strike a delicate balance between affordability and profitability - a balance that OpenAI has yet to achieve.
The hype and reality of o3 demonstrations
OpenAI’s o3, often marketed as having "PhD-level knowledge and reasoning skills," promises unparalleled sophistication. Yet, the demos shown often fail to live up to this lofty claim. For instance, one widely shared demo involved creating a simple app that connects to a REST API - a task easily achievable by beginner programmers. This disconnect between marketing and practical demonstrations undermines trust and leaves users questioning the true capabilities of the tool.
The issue isn’t that o3 lacks potential; rather, it’s that the scenarios chosen for demonstration do not reflect the model’s purported advanced capabilities. To foster confidence in its tools, OpenAI needs to showcase applications that highlight o3’s ability to tackle genuinely complex problems, rather than focusing on tasks that feel underwhelming.
The limitations of OpenAI operators
OpenAI Operators are another example of promising technology that has yet to meet expectations. These tools are designed to perform tasks by interacting with specific websites, guided by user input. However, current demos primarily showcase simple "happy-path" scenarios, where the Operator is directed to a specific website and completes straightforward tasks.
While OpenAI claims that Operators will eventually handle more complex workflows and autonomously decide which websites to use, these capabilities were notably absent in the demos. This lack of demonstrable complexity has led to skepticism among users, who remain unconvinced that Operators can deliver meaningful efficiency gains in real-world applications.
Apple Intelligence: A misstep in marketing and execution
Apple’s foray into AI, branded as Apple Intelligence, has faced significant backlash, highlighting two major issues: an ill-conceived marketing strategy and a disappointing product launch.
A marketing strategy that backfired
Apple’s marketing for Apple Intelligence has been puzzling at best and alienating at worst. Their advertisements portray users as lazy and incompetent individuals who only succeed by relying on AI at the last minute. While this was likely intended as humor, it has not landed well with audiences. Many saw the ads as condescending, leading to a wave of dislikes on YouTube and forcing Apple to disable comments on the videos. Instead of celebrating the potential of their AI tools, Apple’s campaign managed to alienate its user base.
A product that feels unfinished
Adding to the frustration, Apple Intelligence’s launch has been underwhelming. Users report that many of its features feel incomplete, lacking the polish expected from an Apple product. Instead of delivering a seamless and intuitive experience, the AI tools seem under-tested and poorly refined. This has led to widespread disappointment, with critics arguing that Apple should have spent more time perfecting the product before releasing it to the public.
The takeaway
Apple Intelligence’s struggles highlight the critical need to align marketing strategies with user expectations and deliver products that uphold Apple’s reputation for innovation and quality. Without swift improvements, Apple risks being perceived as a follower in the AI space, rather than the trailblazer its audience expects.
The reality behind the AI hype: Why AI often fails to deliver
Artificial intelligence (AI) tools often follow a predictable cycle: they launch with great fanfare, impress early adopters, and then slowly fade into the background as their limitations become clear. This cycle isn’t unique to any single AI program - it’s a broader trend that exposes fundamental flaws in AI development, marketing, and real-world applicability.
One reason why AI is bad at maintaining long-term value is its dependence on training data. AI models thrive in controlled environments but struggle with complex data and unpredictable scenarios. As AI systems attempt to automate repetitive tasks, they often make mistakes due to biases in their training data, ultimately requiring human workers to step in and correct errors. Rather than eliminating human error, AI algorithms can introduce new risks, sometimes amplifying biases or generating misleading outputs.
Another overlooked challenge is AI safety. While AI tools promise efficiency by automating repetitive tasks in our everyday lives, they also open the door for bad actors to exploit vulnerabilities. Weak security measures and flawed AI models can be manipulated to spread misinformation, execute harmful automation, or compromise sensitive data.
Beyond security risks, AI’s failure to replicate human intelligence highlights its shortcomings. While AI excels at processing vast amounts of complex data, it lacks true understanding, reasoning, and adaptability. This gap becomes especially evident in human-computer interaction, where AI struggles to respond to nuanced inputs or make ethical decisions. AI systems may assist with repetitive tasks, but they cannot replace the creativity, intuition, and contextual awareness that define human intelligence.
So, where does that leave AI’s role in our world? The future of AI depends not on exaggerated claims but on its ability to integrate meaningfully into workflows. AI development must prioritize reliability, transparency, and practical utility, rather than just short-term hype. AI programs that genuinely enhance human capabilities - rather than merely replacing human workers - will be the ones that endure.
As we move forward, it’s crucial to approach AI with a balanced perspective: appreciating its potential while remaining aware of AI risks. AI isn’t inherently good or bad - but its impact depends on how we develop, implement, and regulate it. The tools that survive will be those that empower humans, enhance productivity, and address real-world challenges, rather than just promising an artificial revolution that never quite arrives.
