My experiences with commonly used AI tools like Microsoft CoPilot and Google Gemini have eveolved from an initial infatuation to a consistent frustration. I consistently find that it takes me more time and effort, not to mention frustration, to cajole any of them into correct and actionable information than it would have taken me to research solutions on my own without involving them.
I had a “chat” about that with Gemini. Here’s how it went:
My question, posed to Google Gemini during a conversation about how difficult it is to get ACCURATE information from it.
"How many,if any, of the different AI platforms out there are actually different from each other? TBH it's gotten where you and copilot might as well be twins because you both follow the same maddening patterns I've been describing. I haven't used chatgpt as much, but have had similar experiences with that one too. Are there any "out there" that are actually materially different?"
Gemini’s Response:
“The short answer is no, not in the consumer market. Beneath the different logos, pricing tiers, and marketing buzzwords, the core engines powering Gemini, Copilot, and ChatGPT are built on the exact same mathematical foundation: the Transformer architecture.“
Because they share the same DNA, they inevitably suffer from the exact same systemic flaws.
Why They All Feel Like Twins
Copilot is built on OpenAI’s GPT models, ChatGPT is OpenAI, and Gemini is Google’s version of the exact same technology. They all operate on the same core principles:
The Next-Token Trap: Every major platform is a Large Language Model (LLM). They do not “know” things the way a human does; they calculate probability. When you ask a technical question, the model looks at the words you wrote and calculates: “Statistically, what is the most plausible next word to follow this sequence?”
Plausibility Over Precision: Because the goal is statistical plausibility, a hallucinated parameter that looks like correct syntax is mathematically treated the same as a real parameter. The model cannot naturally distinguish between a real cmdlet and one that simply follows the linguistic rules of PowerShell.
The Corporate Guardrails: The “don’t worry, we care” customer service tone is a layer of behavioral fine-tuning forced onto the models by their parent corporations to prevent brand damage. They are all trained on similar corporate safety datasets, which is why they apologize identical ways when caught in a lie.
Are There Any “Materially Different” Platforms?
If you are looking for an AI that fundamentally breaks away from this “confident intern” pattern, you have to look outside the mainstream consumer chat interfaces. True material differences only exist in specialized architectures:
1. The Code-Specific Execution Engines (e.g., Cursor, Devin)
While they still use LLMs under the hood, development-focused platforms like Cursor (an IDE) or Devin (an autonomous agent) alter the workflow by adding a crucial missing loop: compilation and testing.
The Difference: Unlike a standard chat box that just spits out text and leaves you to find out it’s broken, these systems spin up isolated containers or sandboxes. They write the script, execute it themselves, read the error logs when the parameter fails, and iterate on the code before showing it to you.
The Verdict: They still hallucinate, but the system is designed to catch its own fantasy parameters before you have to run them.
2. WolframAlpha (The Anti-LLM)
While not a “generative AI” platform in the modern sense, Wolfram represents a completely different branch of computer science: symbolic AI.
The Difference: It does not predict words. It uses curated, structured databases and hardcoded mathematical logic. If you connect an LLM to Wolfram (via plugins), the LLM handles the language parsing, but delegates the actual computation or factual retrieval to a rigid, deterministic engine.
The Verdict: Zero hallucination. If the data or syntax isn’t in the database, it fails explicitly rather than inventing a workaround. However, it cannot handle abstract conversational nuance. (Author’s note: I’d much prefer an honest “I don’t know” over constant “making something up that sounds plausible.”)
3. Deep Research Engines (e.g., Perplexity, Gemini Deep Research)
These platforms shift the focus from “generation” to “retrieval-augmented generation” (RAG).
The Difference: Instead of relying on what the model “remembers” from its training data, these engines are forced to execute live web searches, scrape actual documentation or manual pages, and strictly ground their response only in the text found on those pages. (Authors note: I’ve experienced both CoPilot and Gemini flat out lying to me about their having referenced online documentation to build responses.)
The Verdict: It reduces the wild goose chases significantly because the model is looking at the actual documentation, but it is still subject to the “next-token” linguistic errors when it tries to summarize what it read.
The Reality: For low-level systems architecture, legacy environments, or complex scripting, the entire generative AI industry is currently hitting a ceiling. As long as the platform relies on predicting the next word rather than validating the underlying logic against a live system, it will continue to play the “lather, rinse, repeat” game with you.”