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The Hype Meets Reality
Every upgrade gets a drumroll. Screenshots flood LinkedIn. Demos go viral. The hype machine insists that this release will finally change everything.
ChatGPT-5 was no different. The marketing spotlight promised more power, smarter reasoning, and game-changing features. What academics got instead was… confusion. So, Vugar and I had a look behind the hype machine of the GPT-5 release. New thinking modes. Hidden settings. A nagging suspicion that cheaper mini models were being used behind the scenes.
Expectation: Fireworks. 🎆
Reality: Frustration. 😣
Why Some Academics Feel Downgraded
I tested ChatGPT-5 against Claude Opus, Gemini, and a couple of free tools most people haven’t touched yet. The results shocked me.
For daily tasks like lecture prep, brainstorming, or grant drafting, the difference was subtle. But in creative or structured outputs, the downgrade was obvious. Paying more didn’t guarantee better results.
Here’s where the frustration deepens. Many researchers don’t want the system auto-selecting models. They want control.
The older o3, for instance, is still widely regarded as sharper in reasoning tasks. Yet some users noticed ChatGPT-5 defaulting to mini versions without warning. Saving compute might be good for OpenAI’s bottom line, but for academics relying on precision, it feels like a bait-and-switch.
Trust is fragile. Once it cracks, it doesn’t matter how many features you stack on top.
Compression Wars and Tiny Models
While ChatGPT-5 hogged headlines, a small startup in Spain quietly released something very different: ultra-compressed models nicknamed “chicken brain” and “fly brain.” Funny names, serious work.
These models run lean. They consume a fraction of the energy. They prove that efficiency might matter more than raw horsepower. For educators worried about cost and environmental impact, this could be the real breakthrough.
It’s not always the biggest brain that wins. Sometimes it’s the smallest one that runs everywhere, from phones to classrooms, without draining batteries or budgets.
Agents, Black Boxes, and Lost Transparency
One of ChatGPT-5’s biggest draws is its agent mode. A little browser spins in the corner, thinking out loud (at least visually) as it fetches data. For casual users, it looks magical. And honestly, we were both impressed. Even though waiting gets a little bit old after a while.
Of course, as academics, we felt that there was just one catch. You can’t see what’s happening under the hood. Replication is impossible. You run the same prompt twice and get two different results. For science, that’s poison.
Academics need transparency. They need logs, methods, a paper trail. I guess LLMs are really not built for that. Instead, we’re given a show. The wow factor sells. The black box frustrates. Research thrives on replicability, and right now the agent features make that harder, not easier. But it certainly can do a lot of different things and perform a lot of different tasks, and it is great to see all of that coming together for complex workflows. We definitely are aware that this is a beneficial feature for many.
Free Tools Quietly Outperforming
Here’s where the story flips. While ChatGPT-5 stumbles, free tools are stepping up.
Take Z.ai. It generates lecture slides in minutes. Icons, layouts, visuals pulled in automatically. Man, when we saw that for the first time, it was almost like an instant GenSpark flashback. The first time I tried it, the slides looked sharper than anything my teaching assistant would draft for me with more time given. That’s not just convenient. That’s disruptive. And really, I feel that is kind of one of the killer features that most academics have been waiting for, especially as we're heading into a new term.
For academics under pressure to prep faster, tools like this feel like a real power move. Anything that can reduce a little bit of the lecture prep grind is a most welcome addition to my tool stack. No subscription. No upgrade. Just results. And really, both of us don't think it's going to stay that way. For now, this is I think one of the least talked about tools with some of the coolest features. Sometimes the best technology isn’t behind a paywall.
The Illusion of Better Output
Upgrades are supposed to mean progress. But if your upgrade feels slower, less transparent, or less trustworthy, is it really progress?
Many users have started toggling back to older models like GPT-4.1 or o3 when it launched with the ability to do so. Not because they’re nostalgic. Because those models still seemed to outperform in reasoning and structure. That should raise alarms. An upgrade that makes power users retreat is an upgrade in name only. Maybe there’s a lesson learned here for OpenAI.
The Energy Question
AI isn’t free. Every prompt burns energy. Every model consumes resources. ChatGPT-5 is no different.
That’s why the work in Spain matters. Compressed models cut energy use. For universities scaling AI across thousands of students, that’s an important innovation that is likely going to have a huge impact. Running massive models in every classroom everywhere locally wouldn’t be sustainable (and the data centres are exploding in cost). Leaner, lighter systems could be the hidden solution.
Sometimes the quiet breakthroughs matter more than the loud ones.
So what does all this mean if you’re an academic? Don’t chase hype. Test before you pay.
Dedicated tools outperform general ones in specialized tasks. Consensus is still stronger for sourcing academic papers. SciSpace shines in structuring related work. Claude is excellent for building study plans and artifacts. Z.ai is shockingly good at slides. Genspark now has a design mode.
ChatGPT-5 can still help. It drafts. It edits. It brainstorms. But it is not the only (or even best) option.
The smartest workflow isn’t loyalty to a single tool. It’s building a toolkit that mixes strengths. And maybe one that also mixes tools depending on what they are specialized in.
Where This Leaves Us
AI isn’t static. What feels like a downgrade today may be patched tomorrow. New releases will keep coming. Hype will keep rising. AI isn’t done with its hypecycle.
But here’s the lesson we want you to take away: Don’t assume new always means better. Don’t assume paid beats free (even though it usually does). And don’t let marketing videos decide how you teach, research, or publish.
The real winners are the academics who test, adapt, and stay pragmatic in their choice of models. If ChatGPT-5 helps, use it. If it slows you down, switch. If a free slide generator saves you hours, don’t dismiss it.
The future of academic work belongs to the ones who test, the ones who adapt, the ones who refuse to sit back and accept whatever the latest update throws at them. It belongs to the curious. It belongs to the builders. It belongs to you.
What did you think of this week’s issue?
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