AI is having its moment.
Every app is now “AI-powered.”
Every product demo, “revolutionary.”
Every roadmap, “on the path to AGI.”
But here’s the problem: when everything is called AI, we start to lose clarity, trust, and perspective. Below is a three-part case against this trend — and a sober critique of the hype around LLMs, AGI, and Agentic AI.
1. Not Everything Is AI, And That’s a Good Thing
AI used to mean something technical: machine learning, pattern recognition, optimization.
Now, even:
- Spellcheck is called AI
- Rule-based scripts are called AI
- A glorified autocomplete is sold as a superintelligence
Why this matters:
When we slap the AI label on everything, we:
- Undermine real innovation
- Confuse users and stakeholders
- Set up false expectations and eventual disappointment
Not every smart system is AI.
Not every software update is intelligence.
Let’s keep the term meaningful.
2. LLMs Are Not Thinking Machines
Large Language Models (like GPT, Claude, Gemini) are powerful, but they are:
- Statistical parrots, not thinkers
- Predictive engines, not reasoners
- Surface-level fluent, but contextually shallow
Yet they’re often presented as:
- Knowledge engines
- Logic machines
- Decision-makers
Why this matters:
We overestimate what LLMs understand and underestimate their hallucinations, biases, and lack of grounding.
Using them without human guardrails in critical applications (legal, healthcare, education) is irresponsible.
3. The AGI Fantasy Is Premature
The idea of Artificial General Intelligence — an AI that can learn and do anything a human can — is seductive.
But right now, we don’t have:
- True reasoning
- Transferable common sense
- Long-term planning
- Genuine understanding
What we do have is marketing language that suggests AGI is just one product release away.
Why this matters:
False proximity to AGI leads to:
- Misallocated resources
- Bad policy
- Existential distraction from real, present-day harms (bias, surveillance, manipulation)
Let’s build better tools before dreaming of artificial gods.
4. Agentic AI Is Not Just a Tech Upgrade, It’s a Governance Crisis
There’s a rush to build autonomous agents: AI that can take actions, make decisions, and pursue goals.
But:
- Agency without alignment = risk
- Automation without transparency = danger
- Systems that do, not just say, introduce accountability black holes
And we’re building them… fast. Often without:
- Ethical constraints
- Oversight
- Explainability
Why this matters:
The moment we give AI systems autonomy, we stop treating them as tools and start shaping them as actors.
Are we prepared for that shift?
AI is real. It’s useful. It’s evolving.
But it’s also being misrepresented, overextended, and romanticized.
Let’s stop:
- Calling every spreadsheet plugin “AI”
- Believing LLMs are sages
- Selling AGI as inevitable
- Treating agentic AI as just smarter assistants
Clarity is power. Precision is progress.
If we want to build a better future with AI, we need to stop hiding behind the buzzword, and start asking better questions.
We’ve reached a turning point in the AI narrative.
Today, anything from a chatbot to a spreadsheet plugin is labeled “AI.”
But if we’re serious about building technology that transforms economies, then we need a clearer definition, a grounded benchmark, and real-world proof.
In my recent essay, “From Hype to Utility”, I argued that not all intelligence is equal, and not all AI is truly transformative.
What AI product companies owe us now is more than bold claims — they owe us proof. That means sharing real-world use cases that reflect:
🔹 Clarity – Clearly define what qualifies as “AI” and what doesn’t
🔹 Evidence – Demonstrate how the system maps to the core dimensions of intelligence
🔹 Accountability – Be transparent about limitations, failures, and real-world outcomes
The future of AI is too critical to be shaped by slick demos and marketing decks.
We need systems that think less like slogans and more like solutions. Let’s demand AI that works, learns, and earns its name, not just AI that talks.