Since artificial intelligence entered our lives, it has been advancing with a powerful, fast, and assertive narrative. However, a large part of this narrative is shaped by AI companies' motivation to increase market share and keep economic hype alive.
Terms like "more powerful," "faster," and "smarter" have become the common language of nearly all product narratives. At this point, we need to pause and ask this question:
Are AI companies really as objective and trustworthy as we think?
In this article, I examine why AI companies' views should not be our primary focus—yet why they remain an important milestone. I also discuss how we can evaluate artificial intelligence from a more balanced perspective.
The Economic Reality of AI Companies
Like all other sectors, the ultimate goal of AI companies is to generate revenue. This is neither inherently wrong nor unethical; on the contrary, it's a natural consequence of market reality. However, this reality raises the question of how objective the views presented to the public can be.
The Hype Machine
When companies have billions of dollars in valuations at stake, their public statements inevitably carry bias. The line between genuine innovation and marketing-driven hype becomes increasingly blurred.
From my perspective, when discussing artificial intelligence, we should give weight to actors who are on the scientific and application side of the work. I categorize these actors into three main groups.
The Three Pillars of Trustworthy AI Discourse
1 Researchers
Professional and independent researchers are the core actors who determine the direction AI will evolve in the future. They base their work on scientific metrics, experiments, and verifiable results. In this way, they draw the boundaries of real progress in the AI field.
Of course, not all research today is completely unbiased. For this reason, I believe we should focus on the work of researchers with high academic reputation, especially those working within reputable universities and trustworthy research institutions.
Why Researchers Matter
Researchers are bound by peer review, replicability, and scientific integrity. Their careers depend on accuracy, not hype. When a researcher publishes findings, they stake their reputation on the validity of those results.
2 Academics and Scientists
Academics and scientists are the actual chefs in the kitchen where research outputs are processed. New techniques, methods, and theoretical foundations are developed by this group. Mathematical models, algorithms, and architectural structures gain their true capabilities at this stage.
In short, if we want to understand "why AI works" or "why it doesn't work," we must look at the academic framework.
The Academic Foundation
While companies may claim breakthrough capabilities, academics provide the theoretical grounding and mathematical proofs. They explain limitations, edge cases, and fundamental constraints that marketing materials conveniently omit.
3 The Practitioner Community
This group—consisting of software developers, doctors, teachers, and other practitioners—directly experiences artificial intelligence and tests its real-world implications. The concrete impact of the theoretical and technical work put forward by the first two groups emerges here.
Has a new algorithm been developed? The real question is this:
Are people actually using it?
If the general consensus is "it didn't really help me," that structure, however theoretically strong, won't find practical application. However, if it's widely adopted and creating value, then we can truly talk about a successful and reliable solution.
The Reality Check
Practitioners provide the ultimate validation. They tell us whether AI actually delivers on its promises in messy, real-world conditions—not in controlled lab environments or cherry-picked demos.
Where Do AI Companies Fit?
Looking at this three-part framework, I think AI companies should not be our primary focus. However, this doesn't mean AI companies should be completely ignored.
What AI Companies Tell Us
AI companies show us valuable information about the business side of AI:
- Is the product scalable? Can it handle millions of users, or does it only work in demos?
- Does it find a market response? Are real customers paying for it?
- Is it economically sustainable? Can the technology support a viable business model?
The answers to these questions are valuable for understanding the real-world business implications of AI. They tell us what's commercially viable, even if they don't tell us what's scientifically accurate or ethically sound.
The Balanced View
AI companies are neither villains to be dismissed nor prophets to be blindly followed. They're one voice in a multi-voiced conversation. The key is to weight their contributions appropriately—neither ignoring them nor treating their marketing as gospel truth.
Navigating the AI Hype Cycle
In practice, this means developing a more nuanced approach to AI information:
- When an AI company makes a claim, ask: "Has this been peer-reviewed? What do independent researchers say?"
- When reading about breakthroughs, look for academic papers, not just press releases
- When evaluating a tool, prioritize practitioner reviews and real-world case studies over vendor testimonials
- When assessing AI's impact, consider multiple perspectives: technical limitations, ethical implications, and practical applications
A Framework for Critical Thinking
Ask yourself: Who benefits from this narrative? What evidence supports it? What counter-evidence exists? Who has been left out of this conversation?
Conclusion: A Multi-Voiced Perspective
From my viewpoint, we should learn about the future of artificial intelligence not solely from AI companies' rhetoric, but from the collective wisdom of independent researchers, academics, and practitioner communities.
We should evaluate the steps taken by AI companies within this framework and form our own synthesis.
Ultimately, artificial intelligence is one of humanity's most impressive technological achievements. For this reason, to truly understand it, we don't need hype—we need a multi-voiced and conscious perspective.
The AI revolution is real, but it's not happening in the way marketing teams would have you believe. It's happening in research labs, academic journals, and on the laptops of practitioners who are quietly solving real problems.
That's where we should be listening.