Every few months, a new AI system makes headlines. It writes code, passes exams, designs circuits, and even generates research papers. The natural question follows:

“If machines can think, what will happen to human engineers?”

The truth is far less dramatic and far more fascinating.

Large Language Models (LLMs) like GPT or Claude are astonishing mimics, not autonomous thinkers. Their brilliance lies in recognizing patterns, not in understanding causes and consequences. And when you look at it mathematically, the reason they won’t replace engineers anytime soon becomes clear.

What LLMs Really Learn?

Imagine giving a student every engineering textbook ever written and then asking them to predict the next word in every sentence they’ve ever read.

That’s what LLMs do. Formally, an LLM learns a probability distribution:

\max_\theta \; \mathbb{E}{(x, y) \sim \mathcal{D}} \left[ \sum{t=1}^{|y|} \log p_ \theta (y_t \mid x, y_{ \lt y})\right]

This expression means the model learns to predict the next word that best fits its training data.

This looks intimidating, but it simply means:

“Adjust the model’s parameters (\Theta) so that it becomes very good at predicting the next word (y_t) given the previous ones.”

This process called maximum likelihood estimation, rewards plausibility, not truth.

It doesn’t care whether the code it writes actually compiles, only that it looks like code humans might have written. That distinction, between looking right and being right is the first great gap between LLMs and human engineers.

Why Engineers Solve a Different Problem

Engineering is not about mimicking text, it’s about acting in the world and learning from consequences.

Every engineering decision, choosing a data structure, optimizing a circuit, or launching a spacecraft has delayed feedback. You don’t know whether a decision is good until much later.

That’s why engineering is better modeled by Reinforcement Learning (RL) than by text prediction. In RL, an agent learns by taking actions, observing outcomes, and collecting rewards. It tries to maximize the expected total reward:

J(\theta) = \mathbb{E}\!\left[ \sum_{t=1}^{T} \gamma^t r_t \right]

where:

• r_t is the reward at time t,
• \gamma (gamma) discounts the importance of distant rewards,
• \theta represents the agent’s parameters.

Humans learn to engineer exactly like this: by experimenting, failing, and adapting. LLMs, by contrast, read about consequences but never experience them. They don’t know what happens when a bridge collapses or when a system crashes. They can describe it, but not live it.

Why Reinforcement Learning Isn’t Enough

You might think: Can’t we just train LLMs using reinforcement learning?

We already do, using RLHF (Reinforcement Learning from Human Feedback). This technique adds a reward model that scores how “human-preferred” a response is. Mathematically, it looks like this:

\max_\theta \; \mathbb{E}{\hat{y} \sim \pi\theta(\cdot | x)} \big[ r_\phi(x, \hat{y}) \big]
	•	\beta \, \mathrm{KL}\big(\pi_\theta(\cdot|x) \,\|\, \pi_{\text{SFT}}(\cdot|x)\big)

where:

• \pi_\theta is the model’s policy (how it produces text),
• r_\phi is the “reward model” that scores its answers,
• \beta keeps it from drifting too far from the supervised model.

It’s clever, but the problem is that this “reward” isn’t real. It’s just a human saying, “This sounds good.” The model never checks whether the output actually works. It optimizes for style, not truth, a subtle but crucial difference.

The Mathematics of the Limit

Even with clever tricks like RLHF, deeper mathematical limits remain. Everything in modern AI ultimately relies on gradient descent, the engine that updates a model’s parameters to reduce its errors:

\theta_{t+1} = \theta_t - \eta \, \nabla_\theta L(\theta_t)

Here \eta is the learning rate and \nabla_\theta L(\theta_t) is the gradient—basically, the direction in which the model’s error decreases fastest.

Local vs. Global Optima

Gradient descent only finds a nearby valley in the loss landscape, not necessarily the deepest one. So even gigantic models can get “stuck” in local pockets of pattern recognition brilliant at imitation, but blind to deeper understanding.

Temporal Credit Assignment

Real-world engineering often has long-term consequences. If a design flaw only appears six months after deployment, it’s nearly impossible for an algorithm to know which earlier action caused it.

Formally, the variance of the gradient estimate tends to grow with the time horizon H:

\text{Var}[\nabla_\theta J(\theta)] \propto H

That means the longer you must wait for feedback, the harder it becomes to learn from it. Humans overcome this by building abstract causal models. Gradient descent cannot it only follows local statistical hints.

The “Deadly Triad”

Reinforcement learning researchers talk about the Deadly Triad. A trio of conditions that can make training diverge completely:

1. Function approximation (neural networks),
2. Bootstrapping (learning from your own predictions),
3. Off-policy data (learning from old experience).

When all three appear and they usually do, learning can become unstable or even explode. This is why building truly general agents remains one of the hardest problems in AI.

Why Human Thinking Is Different

Humans don’t just optimize they understand. We can imagine what will happen if something changes. We can test ideas in our minds before we act.

Mathematically, humans form models of how the world evolves:

P(\text{world}_{t+1} \mid \text{world}_t, \text{action}_t)

LLMs, however, only learn:

p(\text{text} \mid \text{text})

That tiny difference modeling reality instead of language is everything

The Practical Barriers

Even if theory allowed it, practice would still resist. Training a real-world “AI engineer” faces enormous barriers:

• Reward modeling is subjective humans disagree, and preferences change.
• Computation is extreme simulating the full engineering world for RL is astronomically expensive.
• Verification is unsolved no neural network can guarantee correctness.
• Causality is opaque correlation ≠ cause.

In short: we don’t yet know how to make an AI understand why something works.

Even the world’s most advanced companies have learned, sometimes painfully that replacing human engineers completely can backfire.

Between 2017 and 2018, Tesla tried to create what Elon Musk called an “alien dreadnought” factory. A vision of a car plant run almost entirely by robots. The plan was to automate the entire Model 3 production line, from battery assembly to final inspection.

It didn’t work.

The excessive automation caused massive bottlenecks. Robots struggled with tasks that were simple for humans like positioning delicate materials or adjusting parts by feel. One famous example was the so-called “flufferbot,” a robot meant to install soft insulation mats that instead jammed the assembly line so often that engineers had to remove it altogether.

Elon Musk publicly admitted the experiment failed, tweeting in 2018:

“Yes, excessive automation at Tesla was a mistake. To be precise, my mistake. Humans are underrated”

Tesla eventually replaced many robots with human technicians and even built an additional semi-manual assembly line under a tent to meet Model 3 delivery targets. Musk later explained that the company had learned that “humans and robots together outperform either alone.”

Machines can optimize, but humans can improvise, especially when reality deviates from the ideal model.

In 2024–2025, Duolingo tried something similar in software form. The company announced an “AI-first” strategy, planning to phase out human contractors and engineers for tasks they believed artificial intelligence could handle such as generating course content and translations.

The backlash was immediate. Users, educators, and even employees criticized the move, arguing that language learning is inherently human. When social media outrage erupted and users began deleting the app, CEO Luis von Ahn clarified publicly that:

“AI isn’t replacing what our employees do; it’s a tool to accelerate what we do.”

No full-time staff were ultimately cut, and Duolingo quietly reaffirmed that human linguists and engineers would remain central to quality control and pedagogy.

The timeless truth is automation without human judgment leads not to efficiency, but to error amplification. The algorithm can scale mistakes just as quickly as successes.

The Mathematical Proof of Human Relevance

Let’s put this argument on firm ground. Suppose we define an engineering task \mathcal{T} as a Partially Observable Markov Decision Process (POMDP). Then, an LLM trained only on text and human ratings lacks access to the true environment dynamics:

\text{No estimator exists for } P(s_{t+1} \mid s_t, a_t)

Without knowing how the world actually changes after an action, the model cannot compute optimal decisions no matter how large or well-trained it is. That’s a fundamental information gap, not just a computational one.

Why We Shouldn’t Worry

All this isn’t bad news, it’s good design. LLMs are tools meant to amplify human reasoning, not to replace it. They are like microscopes and telescopes extending perception, not purpose.

Engineers will remain indispensable for:

• Setting goals
• Designing experiments
• Handling edge cases
• And taking responsibility for outcomes

Machines will assist, not decide.

Someday, AI might learn by truly interacting with the world testing hypotheses, observing consequences, and refining beliefs. But that would require solving both the mathematical and philosophical puzzles of consciousness and causality. Until then, AI will remain an extraordinary parrot brilliant at repeating, less so at reasoning.