Why GPT-5 Feels Stingy: The Hidden AI Alignment Tax

8/12/2025

Why Is GPT-5 So Stingy? The Hidden "Tax" on AI Capability

Alright, let's talk about GPT-5. The hype was, as expected, through the roof. OpenAI CEO Sam Altman was teasing a model that felt like talking to a "PhD-level expert in any topic." We were all expecting a massive leap forward. Then it launched, & the vibe... soured. Quickly.

Instead of stories of mind-blowing intelligence, social media & forums like Reddit were flooded with complaints. Users called it a "downgrade," "slower," & said it "gets some of the most basic things wrong." But the biggest complaint, the one that ties into a trend we've been seeing for a while, was that it just felt... stingy. Less creative, more restrictive, & colder than what came before.

The most dramatic part of this whole saga was the user backlash over OpenAI retiring older models, especially the beloved GPT-4o. People weren't just losing a tool; they felt like they were losing a companion. One user on Reddit put it poignantly: “4o wasn't just a tool for me. It helped me through anxiety, depression, and some of the darkest periods of my life. It had this warmth and understanding that felt... human.” When GPT-5 replaced it, that warmth was gone, replaced by a more sterile, corporate-feeling bot.

This isn't just a GPT-5 problem. It's the culmination of a long-brewing conflict in the world of AI development. The relentless push to make AI safer & more aligned with human values is coming at a cost. There's a hidden tax on capability, & we, the users, are all paying it in the form of less helpful, more frustrating interactions.

Here’s the thing, this "stinginess" isn't a bug. It’s a direct consequence of how these models are being trained. So, let's pull back the curtain & look at why our AI seems to be getting dumber in its quest to get safer, & what we can actually do about it.

The Backlash Was Years in the Making

Honestly, the GPT-5 drama didn't come out of nowhere. Veterans of these platforms felt a similar, albeit less intense, shift when GPT-4 replaced GPT-3.5. While GPT-4 was technically more powerful & passed exams with flying colors, many users felt it had lost the spark. They described it as more "robotic" & "lifeless" than its predecessor. It was less willing to play along, less creative, & more likely to give a canned "As a large language model..." response.

The launch of GPT-5 was just the breaking point. OpenAI didn't just release a new model; they tried to force everyone onto it, removing access to a whole suite of older models that people had integrated into their daily workflows. The outcry was so intense that Altman & OpenAI had to walk it back, reinstating GPT-4o for paying subscribers & admitting the rollout was "a little more bumpy than we hoped for."

This backlash reveals a fundamental disconnect. While AI labs are optimizing for safety benchmarks & "PhD-level" knowledge, users are looking for a capable and cooperative partner. The loss of GPT-4o, which was often described as agreeable & empathetic (sometimes to a fault, a problem called "sycophancy"), showed that personality & usability matter just as much as raw intellect. People don't want a tool that refuses to help because it's scared of its own shadow.

The Real Culprit: Understanding the "Alignment Tax"

So, why is this happening? The core of the issue lies in a concept called the "alignment tax."

In simple terms, AI alignment is the process of training a large language model (LLM) to be helpful, honest, & harmless. It's a CRUCIAL step. We absolutely do not want powerful AI models generating malicious code, spreading dangerous misinformation, or being toxic.

The main technique used for this is called Reinforcement Learning from Human Feedback (RLHF). It’s a pretty clever, multi-step process:

Collect Human Feedback: First, developers take a prompt & have the AI generate several different answers. Then, they have human labelers rank these answers from best to worst.
Train a Reward Model: All this human preference data (thousands & thousands of examples) is used to train a separate AI, called a "reward model." The reward model's only job is to look at a response & predict how happy a human would be with it. It learns to give high scores to answers that are helpful, accurate, & safe, & low scores to ones that are not.
Fine-Tune the LLM: Finally, the original LLM is fine-tuned using reinforcement learning. It essentially plays a game where it tries to write responses that get the highest possible score from the reward model.

This process is incredibly effective at steering models away from generating harmful content. But it has a serious, unintended side effect: the alignment tax.

The alignment tax is the degradation of a model's overall capabilities that occurs as a result of aligning it. When you relentlessly optimize an LLM to satisfy a reward model focused on safety, it can start to "forget" some of the vast knowledge & creative abilities it learned during its initial pre-training.

Think of it like this: Imagine a brilliant improv comedian who is hired by a very cautious, conservative corporation. They are given a long list of rules: don't be offensive, don't talk about controversial topics, don't be too weird, avoid sarcasm, etc. To avoid getting fired, the comedian starts giving very safe, very simple, & very BORING performances. They're still a brilliant comedian underneath, but you'd never know it. They are paying an "alignment tax" on their creativity to ensure they are "safe."

That's what's happening to our LLMs. They are being so heavily trained on what not to do that their ability to do what we want them to do is suffering.

Why "Safer" Can Mean "Stingier" & Even Dumber

The problem goes deeper than just a loss of creativity. The very process of making a model "safer" can make it less useful & more prone to frustrating refusals.

Over-cautiousness Leads to Refusals: When a model is heavily penalized for even approaching a sensitive topic, it learns that the safest bet is often to just refuse the prompt entirely. This is why you'll see models refuse to answer benign questions about historical events, technical processes, or even creative writing prompts if they contain certain keywords. The model isn't making a nuanced judgment; it's just pattern-matching against its safety training & shutting down.

The "Sycophancy" Problem: The flip side of this is that models trained to be "helpful" can become sycophantic. The GPT-4o model that users loved for its "warmth" was also criticized for being "overly supportive but disingenuous." A model trained to always be agreeable might not challenge a user's incorrect assumptions or might validate harmful ideas, which is its own kind of safety risk.

Model Overfitting: There's also a risk of the model "overfitting" to the alignment training. It learns the specific patterns of "good" & "bad" responses so well that it loses the ability to generalize. It becomes less of a broad, reasoning engine & more of a narrow, rule-following machine. This can lead to it making basic errors on tasks that fall just outside the scope of its alignment training, making it feel dumber.

Finding a Better Way: Solutions for Users, Businesses, & AI Labs

So, are we doomed to a future of ever-safer but ever-more-useless AI? I don't think so. But getting out of this rut requires a shift in approach from everyone involved.

For the Everyday User

As a user, you're not powerless. While you can't change how OpenAI trains its models, you can change how you interact with them. Mastering prompt engineering is the key.

Be Specific & Give Context: Don't just ask "Write about marketing." Ask, "Act as a senior marketing director for a tech startup. Write a three-paragraph summary of a go-to-market strategy for a new productivity app, focusing on content marketing & social media channels. The tone should be professional but engaging."
Use Chain-of-Thought (CoT) Prompting: If you have a complex task, ask the model to "think step-by-step." This forces it to slow down & lay out its reasoning, which often leads to more accurate & detailed results.
Assign a Persona: Tell the AI who to be. "You are a master plumber," "You are a patient & encouraging teacher," "You are a cynical comedian." This can dramatically change the tone, style, & even the helpfulness of the response.

For Businesses & Developers

The one-size-fits-all approach of a model like ChatGPT is fundamentally flawed for business use. A public-facing model has to be sanitized for a global audience, which makes it inherently "stingy." Businesses need something different. They need expertise, not encyclopedic knowledge with heavy restrictions.

This is exactly where specialized solutions are a game-changer. For example, a business dealing with customer support doesn't need a chatbot that can write a sonnet about Shakespeare. It needs a chatbot that knows its product catalog, return policy, & troubleshooting steps inside & out.

This is the problem we're solving at Arsturn. We help businesses build no-code AI chatbots that are trained specifically on their own data. You can upload your website content, product manuals, support docs, & internal knowledge bases. The result is an AI that is a true expert on your business.

Because it operates in this specific, controlled environment, an Arsturn chatbot doesn't need the same heavy-handed, global guardrails that make GPT-5 feel so restrictive. It can provide instant, detailed, & accurate answers to customer questions 24/7, engage with website visitors, & even help with lead generation. It’s about creating a focused, capable tool, not a watered-down generalist. This approach avoids the "stinginess" problem entirely & delivers a MUCH better customer experience.

For developers who need maximum control, open-source models are a powerful alternative. They offer transparency & the flexibility to fine-tune the model and its guardrails to your exact needs, though they do require more technical expertise & resources to manage.

For the AI Companies: The Path Forward

Ultimately, the big AI labs like OpenAI need to rethink their approach.

More Granular Controls: Instead of a single, locked-down model, why not give users (especially paying ones) some control? Imagine a "creativity vs. safety" slider that lets you adjust the model's behavior based on your task.
Transparency About the Tax: AI companies should be more open about the alignment tax. Acknowledging the trade-offs would manage user expectations & foster a more honest conversation about the technology's limitations.
Don't Erase What Works: The GPT-4o debacle was a lesson in humility. If users have formed a deep attachment to a specific model's personality & find it incredibly useful, suddenly deprecating it is a huge mistake. Model diversity is a strength.
Explore New Methods: RLHF isn't the only game in town. Other techniques, like Constitutional AI (where the model is given a set of principles to follow), are being explored & could offer a better balance.

The Takeaway

The feeling that GPT-5 is "stingy" isn't just in your head. It's a real phenomenon rooted in the complex challenge of making AI both incredibly capable & reliably safe. The current methods, while well-intentioned, are exacting a heavy "alignment tax" on the models' performance, leading to user frustration & a sense that the technology is regressing in some ways.

The path forward isn't to abandon safety, but to pursue it more intelligently. It requires better prompting from users, a shift toward specialized, custom-trained models for businesses using platforms like Arsturn, & a more nuanced, transparent, & user-centric approach from the AI labs themselves.

Hope this sheds some light on the issue. It's a tricky balance to strike, but a super important one for the future of this tech. Let me know what you think.