By 2024, if you were building a new language model, you didn’t even consider RNNs. Not because they were broken, but because they were too slow, too limited, and too tired. Transformers didn’t just improve on RNNs-they made them obsolete. The shift wasn’t gradual. It was a wall coming down.
The Problem with RNNs Wasn’t Just Speed
RNNs were the go-to for text for years. They processed words one at a time, like reading a book from left to right, remembering each word as they went. That sounds logical-until you try to understand a sentence like: "The cat that the dog chased, which ran across the street, finally climbed the tree."
RNNs struggled here. By the time they got to "tree," the memory of "cat" had faded. Gradients vanished. After 10 to 20 words, the model couldn’t connect the dots. This wasn’t a bug-it was physics. Backpropagation through time just couldn’t carry signals that far without them dissolving into noise.
And training? Brutal. If you had a 100-word sentence, you had to run it through the network 100 times, one step at a time. No shortcuts. No parallelization. Even with powerful GPUs, training an RNN on a decent dataset could take days. And that was just for a basic model.
Transformers Didn’t Try to Fix RNNs-They Ignored Them
The 2017 paper "Attention is All You Need" didn’t say "RNNs are bad." It said: "Why are we even doing this?"
Transformers threw out the sequential chain. Instead of processing words one after another, they looked at the whole sentence at once. Every word could talk to every other word. That’s the self-attention mechanism. It doesn’t care if two words are five apart or fifty. It computes a score: "How much does this word matter to that word?" And it does it for every pair, all at the same time.
That’s why training got faster. Not just a little faster-7.3 times faster on the same hardware. Google’s original tests showed a 100M-parameter transformer training in hours instead of days. Why? Because modern GPUs are built for parallel work. Transformers used them the way they were meant to be used.
Long-Range Dependencies? Solved.
Remember that cat-and-dog sentence? Transformers didn’t blink. They could handle 512 tokens easily. Later models like GPT-3 handled 2,048. Gemini 1.5? A million tokens. That’s a 300-page document in one go.
How? Because attention creates direct connections. No need to pass information through 50 layers of hidden states. If "cat" and "tree" are in the same sentence, the attention mechanism finds them instantly. No gradient decay. No memory loss.
On the Long Range Arena benchmark-designed to test how well models handle distant dependencies-transformers scored 84.7% accuracy at 4,096 tokens. LSTMs? 42.3%. That’s not a slight edge. That’s a demolition.
It’s Not Just Accuracy-It’s Scalability
Transformers didn’t just get better at understanding language. They unlocked scaling. You could make them bigger-way bigger-and they still worked.
Large RNNs? They became unstable. Training them with more than 100 million parameters was a gamble. Transformers? GPT-3 had 175 billion. Switch Transformer? Over a trillion. And they didn’t explode. They got smarter.
Why? Because attention scales differently. It’s not about memory depth-it’s about connectivity. Add more layers, more heads, more parameters, and the model just learns finer distinctions. RNNs hit a wall. Transformers kept climbing.
By 2024, 98.7% of new state-of-the-art language models were transformer-based. GPT-4, Llama 3, Claude 3-none of them used RNNs. Not even a single layer. The industry had moved on.
But Transformers Aren’t Perfect
Don’t get it wrong-transformers aren’t magic. They have real costs.
Memory? Quadratic. If you double the sequence length, memory use goes up by four times. A 4,000-token sequence needs 16 times more memory than a 1,000-token one. That’s why you see tricks like sparse attention, sliding windows, and mixture-of-experts in models like BigBird and Gemini.
Energy? Huge. Training GPT-3 emitted 552 tons of CO2. That’s like driving 120 cars for a year. And fine-tuning Llama 2 on AWS? One startup reported a $14,000 monthly bill. Sustainability isn’t a side note-it’s a crisis.
And understanding? Transformers are pattern matchers. They don’t reason. They guess what word comes next based on what they’ve seen. That’s why they hallucinate. That’s why they fail simple logic tests. One benchmark showed transformers scoring just 18.3% on logical reasoning tasks. Hybrid systems that combine neural networks with symbols? They hit 67.4%.
Where RNNs Still Hang On
Don’t write RNNs off completely. They still have a place.
On embedded devices with 1MB of memory? RNNs win. They’re tiny. Fast. Low power. If you’re building a sensor that predicts machine failure from a 10-timestep signal, an RNN is perfect.
On molecular biology datasets? A 2023 study showed RNNs outperformed transformers on predicting local chemical properties. Why? Because the patterns were short-range, local, and repetitive-exactly what RNNs were designed for.
And for real-time systems needing under 10ms response? RNNs still win. Transformers need to load the whole sequence. If you’re processing live audio or sensor data one frame at a time, RNNs are leaner.
But these are niches. The rest? Transformers are the default.
What Developers Actually Experience
On Reddit, a developer wrote: "I trained a BERT model in 8 hours on a single RTX 4090. My LSTM took 63 hours for the same accuracy." That’s the story everywhere.
But the learning curve? Steep. Getting attention mechanisms right isn’t easy. Positional encoding? Messy. If you don’t scale it by 1/sqrt(d_model), your model won’t converge. Hugging Face’s library makes it easier-but understanding what’s under the hood? That takes weeks.
Stack Overflow has over 8,000 transformer-related questions from early 2024 alone. The average answer time? 14.7 hours. For RNNs? 8.3. People still know RNNs better. But they’re choosing transformers anyway.
The Future: What Comes After Transformers?
Is this the end? No. But it’s not the beginning of the end either.
Researchers are already working on sparse transformers, quantum-inspired attention, and hybrid models that combine neural networks with symbolic logic. Google’s AlphaGeometry used a transformer to generate proofs-but it was guided by a rule-based system. That’s the future: transformers as engines, not minds.
Yoshua Bengio says we’re hitting the limits of scaling. Andrew Ng says they’ll dominate for another decade. Both might be right.
Transformers solved the two biggest problems in language modeling: speed and context. They made training feasible. They made long-range understanding real. And they made large language models possible.
For now, they’re not going anywhere. Even if something better comes along, it’ll have to be faster, smarter, and cheaper. And no one’s built it yet.
Why did transformers replace RNNs in large language models?
Transformers replaced RNNs because they process entire sequences in parallel, making training dramatically faster, and use self-attention to capture relationships between any two words in a sentence, no matter how far apart. RNNs processed words one at a time and lost track of context over long distances due to vanishing gradients. Transformers solved both problems at once.
What is the main advantage of self-attention in transformers?
Self-attention lets every word in a sequence directly interact with every other word, creating a map of relationships. This means a transformer can understand that "it" in a sentence refers to a noun mentioned 20 words earlier-something RNNs struggled with. It’s not about memory; it’s about connection.
Are RNNs completely obsolete now?
No. RNNs are still used in niche areas where memory and speed are tight-like embedded sensors, real-time audio processing, or molecular property prediction on short sequences. But for any serious NLP task-translation, summarization, chatbots-transformers are the only choice.
Do transformers need more data than RNNs?
Yes. Transformers thrive on massive datasets. GPT-3 was trained on 300 billion tokens. RNNs could achieve decent results with 10-100 times less. But the trade-off is performance: transformers scale better. More data = much smarter models. RNNs hit a ceiling faster.
Why are transformers so energy-intensive?
Because they process every word in relation to every other word, their computational load grows quadratically with sequence length. Training models like GPT-3 required over 3,600 petaFLOPs and 1,000 high-end GPUs. That’s a lot of electricity. A single training run can emit hundreds of tons of CO2-equivalent to dozens of cars driven for a year.
Can transformers understand logic and reasoning?
Not really. Transformers are pattern recognizers, not reasoners. On logic benchmarks, they score around 18%-far below humans or hybrid systems that combine neural networks with symbolic rules. They guess what comes next based on statistics, not cause and effect. That’s why they hallucinate facts or make illogical claims.
What’s next after transformers?
Researchers are exploring sparse attention (to reduce memory use), hybrid models (transformers + symbolic logic), and quantum-inspired architectures. Google’s AlphaGeometry and Meta’s Llama 3 are steps in this direction. But no replacement has matched transformers’ balance of speed, scalability, and performance yet.
