Attention Isn’t All You Need Anymore: The Upcoming Era of SSM based GenAI

This content originally appeared on Level Up Coding – Medium and was authored by Mohit Sewak, Ph.D.

Exploring the powerful counter-narrative of Mamba and other State Space Models leading to the dawn of hybrid intelligence.

Alright, pull up a chair. Let me pour you a cup of proper masala chai — none of that powdered stuff. The good kind, brewed with ginger and cardamom, the kind that makes you want to solve the world’s problems. Because today, we’re talking about a palace coup, a proper heavyweight title fight happening in the heart of artificial intelligence.

I. The Cold Open: The Day the King Stumbled

Back in 2017, a scroll was published that might as well have been carved into stone tablets. It was called “Attention Is All You Need,” and for years, that wasn’t just a catchy title; it was the law (Vaswani et al., 2017). The architecture it unveiled, the Transformer, was our Muhammad Ali. It was brilliant, powerful, and it floated like a butterfly and stung like a bee. It powered everything we brag about in AI: GPT-4, Llama, the models that write poetry, code, and stunningly sarcastic emails. The Transformer was the undisputed, undefeated heavyweight champion of the world.

But here’s the thing about champions, especially the ones that rely on brute force. They have a weakness. A tell. And the Transformer’s is a doozy.

Its secret weapon, the “self-attention” mechanism, is how it connects ideas. It’s a genius move, but it has a crippling flaw: a computational cost that scales quadratically.

Let me translate that from nerd to English. Imagine you’re proofreading a 100-page document. The Transformer’s method is like comparing every single word to every other word in the entire document. It’s exhaustive. It’s powerful. It’s also… insane. Now, if you double the document length to 200 pages, you don’t just double the work. You’ve quadrupled it. This is what computer scientists call the O(n²) bottleneck, and it’s been the fundamental roadblock preventing us from feeding an AI an entire library of books, a person’s complete genome, or a feature-length film and saying, “Go nuts” (Paul, n.d.).

For years, we just accepted this. We paid the price. We built bigger gyms (data centers) for our champ. But then, someone from a forgotten dojo stepped into the ring. A challenger, rooted in old-school 1960s control theory but re-engineered for the modern era: the State Space Model (SSM). And its star pupil, an architecture named Mamba, just did the unthinkable. It proved it could fight with the same intelligence as the Transformer but with linear scaling (O(n)). Double the pages, double the work. Not quadruple. This wasn’t just a new contender; it was a whole new fighting style (Gu & Dao, 2023).

So here’s my thesis, the hot take I’m serving up with this chai: The reign of the Transformer “monoculture” is over. We are charging headfirst into a glorious new era of architectural diversity. This is a story about a fascinating clash of titans: on one hand, the rise of hybrid fighters that mix the Transformer’s knockout power with the Mamba’s speed. And on the other, a powerful counter-narrative from pure SSMs that are scaling up and whispering, “Maybe that old champ’s moves are all you need to forget.”

This isn’t a replacement. It’s a renaissance. And it’s going to be a hell of a show.

“Everybody has a plan until they get punched in the mouth.” — Mike Tyson.

For years, the Transformer had the plan. Mamba just delivered the punch.

II. The Stakes: Why a Sluggish Champ Can’t Win the Fights of Tomorrow

So, this O(n²) thing — the “quadratic bottleneck” — why does it matter to anyone outside of a university computer lab? Because it’s a roadblock to the future we’ve been promised. This isn’t just about saving a few bucks on cloud computing; it’s about what AI can and, more importantly, cannot do.

The bottleneck fundamentally limits the “context window” — the amount of information a model can see and think about at once. Our current champion can only stay in the ring for a few rounds. The moment the fight goes long, it gasses out.

Think about the game-changing applications we’re leaving on the table:

• In Medicine: Imagine an AI that could analyze a patient’s entire multi-decade medical history — every doctor’s note, every lab result, every genetic marker — to spot a pattern no human could. Right now, we’re lucky if it can clearly read the last report.
• In Science: We want models that can process a full genomic sequence, which can have billions of base pairs, to find the origins of a disease. The Transformer, bless its heart, would fall over just looking at the table of contents.
• In Your Business: How about a financial AI that reads every annual report, every shareholder letter, and every press release a company has published for the last 50 years to make a truly informed prediction?

The low-hanging fruit — summarizing emails, writing short articles, translating paragraphs — has been picked. The next frontier of AI requires models that can reason over vast, unbroken rivers of information. And for that job, our classic, beloved Transformer is simply not the right fighter.

ProTip: When you hear “long context,” don’t just think “more text.” Think “higher fidelity.” It’s the difference between an AI seeing a single frame of a movie versus watching the entire trilogy. One gives you a snapshot; the other gives you the story. The quadratic bottleneck is what keeps our AIs stuck on snapshots.

III. The Challenger Arrives: Unpacking the Mamba’s One-Inch Punch

So, how did this challenger suddenly get so good? Mamba is a State Space Model, which means it processes information sequentially, one piece at a time, like the old-school RNNs and LSTMs we all cut our teeth on (Hochreiter & Schmidhuber, 1997). But it avoids all the reasons we abandoned those models, like their tendency to forget what happened five minutes ago (the infamous vanishing gradient problem).

Think of it this way: instead of comparing every word to every other word (the Transformer’s brute-force method), an SSM reads the sequence token-by-token. It maintains a compressed ‘state’ — a continuously updated summary of everything it has seen so far. It’s like reading a novel and keeping a running mental summary, which is inherently more efficient.

But the real magic, the stuff that makes Mamba a true title contender, comes down to two masterstrokes detailed in its groundbreaking paper (Gu & Dao, 2023).

1. The Selective Scan Mechanism: This is the secret technique, the “one-inch punch” that gives Mamba its devastating power. The model doesn’t just passively summarize the past. It can selectively choose what to remember and what to forget based on the word it’s reading right now.

Imagine a brilliant analyst reading a dense report. They don’t give equal weight to every sentence. They skim the fluff, glide over the intro, but then slow down and focus with laser intensity on key data points, unexpected findings, and the final conclusion. Mamba’s selective scan does the same thing computationally. It filters information on the fly, making its compressed “state” incredibly rich and context-aware. This is how it competes with attention’s reasoning power without the insane cost.

2. Hardware-Aware Kung Fu: This is where the nerds become legends. The Mamba authors didn’t just write a cool algorithm on a whiteboard. They knew that making the scan “selective” broke the mathematical trick that made older SSMs fast to train. A naive implementation would be dead slow on modern GPUs.

So, they did something incredible: they designed the algorithm and the hardware implementation at the same time. They wrote a custom, parallelized kernel that takes full advantage of a GPU’s memory structure, making this selective scan insanely fast (Dao, 2022). This fusion of software and hardware is like designing a new martial art and also inventing the perfect shoes to perform it in. It’s a masterclass in building for the real world.

IV. The Hybrid Imperative: Creating the Ultimate Fighter

So, the new challenger is fast, efficient, and has a killer move. Does that mean the old champ is finished? Not so fast. The Transformer’s haymaker — its ability to spot relationships between concepts no matter how far apart they are — is still the best in the business for certain tasks.

This realization sparked a “Rocky IV” training montage across the AI world. The new philosophy: why choose when you can combine? Let’s create a hybrid fighter with the best of both worlds.

Case Study 1: MambaVision’s Hierarchical Blueprint

The folks at NVIDIA came up with a beautifully intuitive strategy for computer vision called MambaVision (Hatamizadeh & Kautz, 2024). Vision models are a perfect place for hybrids because they deal with a ridiculous number of “tokens” (pixels or image patches).

Their blueprint is hierarchical:

• The Early Rounds (Mamba): The first few layers of the model, which have to process a huge grid of pixels, are all Mamba. This is the heavy lifting, the grunt work of spotting edges, textures, and basic shapes. Mamba’s efficiency is perfect for scanning these long sequences of patches without breaking a sweat.
• The Final Rounds (Transformer): As the model processes the image, it condenses it into more abstract ideas (“that looks like a wheel,” “that looks like a door”). Once you have these high-level concepts, the number of tokens is much smaller. Now, you bring in the champ. The Transformer layers at the top can use their global attention to efficiently connect “wheel” and “door” to form the concept of a “car.”

It’s a brilliant division of labor: Mamba for the high-volume, local-feature grind, and Transformer for the high-level, global-concept synthesis.

Case Study 2: TransXSSM’s Universal Translator

Other researchers wanted to weave the two styles together more tightly, interleaving the layers. But they hit a snag. A Transformer understands the position of a word using one method (explicit positional codes), while an SSM understands it implicitly through its sequential nature. It’s like trying to build a car with one engineer working in inches and the other in centimeters. Nothing lines up.

The TransXSSM paper solved this with a clever invention: the Unified Rotary Position Embedding (Unified RoPE) (Wu et al., 2025). Think of it as a universal translator, or a Rosetta Stone for positional information. It’s a mathematical framework that both the Transformer and the SSM components can understand, allowing them to share a single, coherent sense of where each piece of data belongs. It’s a deep, technical fix that allows for a much more seamless and powerful hybrid.

Case Study 3: StripedHyena and the Expanding Dojo

Just to prove the hybrid revolution is bigger than just one rivalry, models like StripedHyena from Together AI are mixing attention not with SSMs, but with another efficient technique called gated convolutions (Poli et al., 2023). This is like a fighter realizing that boxing and karate aren’t the only two martial arts. They’re adding in some Muay Thai kicks and Brazilian Jiu-Jitsu grappling. The goal is the same: create a diverse toolkit of computational moves that can be mixed and matched to create the perfect fighter for any given opponent.

Trivia/Fact Check: The “State Space” concept isn’t new AI hype. It comes from Control Theory, a field of engineering and mathematics that deals with the behavior of dynamical systems. It was formalized in the early 1960s, making it a proper old-school technique that’s suddenly fashionable again.

V. The Counter-Narrative: The Purist Who Just Trained Harder

Just as the world was getting hyped about these new hybrid fighters, a paper dropped that was like a quiet, devastatingly effective counter-punch. It was called “The First Competitive Attention-free 7B Language Model,” and it introduced us to Falcon Mamba (Zuo et al., 2024).

This model was a purist. Zero attention blocks. 100% pure Mamba architecture. The researchers didn’t try to teach it any of the old champ’s moves. Instead, they just put it through the most brutal training camp imaginable: 5.8 trillion tokens of data. For context, that is an absolutely colossal amount.

The result? It matched or surpassed leading Transformer models of a similar size.

This paper was a massive reality check, and it carries three critical implications:

1. A Reminder of the “Bitter Lesson”: This is a beautiful, painful reminder of AI researcher Rich Sutton’s famous “Bitter Lesson”: clever, human-designed architectural tricks are often less important than just throwing massive amounts of data and compute at a simpler, more scalable algorithm (Sutton, 2019). Falcon Mamba’s success suggests that maybe Mamba’s only weakness was that it had been undertrained compared to the pampered Transformer champions.
2. Establishing a New Baseline: Falcon Mamba is now the benchmark to beat. Any researcher who comes up with a fancy new hybrid model now has to answer a tough question: “Is your complex creation actually better than a pure Mamba that’s been trained on a metric ton of data?” It forces the field to justify its complexity.
3. The Enduring Allure of Efficiency: For real-world use — running AI on your phone, deploying a service to millions of users — the speed and smaller memory footprint of a pure SSM are killer features. Falcon Mamba proves you don’t necessarily have to sacrifice top-tier performance to get that efficiency.

This doesn’t mean hybrids are a bad idea. It just means the fight is more interesting now. We have two competing philosophies in the ring.

VI. The Ringside Debates: Navigating the New Frontier

So, here we are, ringside, watching this incredible title fight unfold. The arena is buzzing, but nobody knows for sure what the best strategy is. This is a new science, and we have more questions than answers.

The Unsolved Puzzles of Hybridization: Designing these new fighters is still more of an art than a science. As the review paper points out, we’re grappling with some big questions (Section 5.1):

• What’s the perfect mix? One Transformer block for every three Mamba blocks? Or the other way around?
• Is it better to stack them (like MambaVision) or interleave them (like TransXSSM)?
• Can we figure out which architecture is best for a task before spending millions of dollars on a full training run?

The Responsible AI Black Box: This one keeps me up at night. As a Responsible AI guy, I have to point out a huge challenge. Transformers, for all their faults, gave us a little window into their “thinking” through attention maps. We could at least see what words the model was “looking at” when it made a decision.

The compressed, continuously evolving state of an SSM is far more mysterious. It’s a more opaque black box. Understanding how it makes its decisions is a massive open research problem (Section 5.3). Before we deploy these new models in high-stakes fields like medicine or finance, where a wrong decision can have serious consequences, we need to figure out how to pop the hood and see what’s going on.

“The more I learn, the more I realize how much I don’t know.”

— Albert Einstein.

Welcome to the bleeding edge of AI research, folks.

VII. The Path Forward: Welcome to the Era of the All-Star Team

So, what’s the final verdict? Who wins the belt?

The most beautiful thing is that there might not be a single champion anymore. The key takeaway is that we’ve shattered the monoculture. The choice of architecture — Pure Transformer, Pure SSM, or a clever Hybrid — is now a critical strategic decision for any AI team, another dial to turn (Nebius, 2024).

The future of AI is a fascinating race between two philosophies:

• The Clever Hybrids: The strategic trainers, meticulously designing intricate fighters that combine the best moves from every discipline.
• The Scaled Purists: The hardcore engineers, betting that with enough raw data and brutal training, the sheer power of a single, highly efficient fighting style will win out.

For anyone in business or policy, this means the AI landscape is getting more specialized. The “one model to rule them all” dream is fading. It’s being replaced by the need for a stable of specialized fighters, each one perfectly tuned for a specific challenge — whether that challenge is speed, cost, or the ability to read a context window the size of a phone book.

VIII. The Final Bell

Let’s finish our chai. We’ve been on quite a journey. We started with the unchallenged king of the ring, the Transformer, whose reign seemed eternal. We saw the cracks appear in its foundation — that crippling slowness in the long game. Then, from a forgotten school of thought, the challenger Mamba emerged, lightning-fast and armed with a devastating new technique.

This arrival didn’t lead to a simple knockout. Instead, it sparked an explosion of creativity. Some are building hybrid warriors, convinced the future lies in synthesis. Others are training pure Mamba champions to their absolute physical limits, proving that purity and scale are a force to be reckoned with.

The end of the Transformer’s total dominance isn’t an ending. It’s the beginning of the most exciting, competitive, and innovative era in the history of AI. The ring is now open, the fighters are diverse, and the bell for the next round has just rung.

References

Foundational Papers: The Old Guard

• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. In Advances in Neural Information Processing Systems 30. https://arxiv.org/abs/1706.03762

The Rise of SSMs: The New Challenger

• Gu, A., & Dao, T. (2023). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. arXiv preprint arXiv:2312.00752. https://arxiv.org/abs/2312.00752
• Gu, A., Dao, T., Ermon, S., & Ré, C. (2020). HiPPO: Recurrent Memory with Optimal Polynomial Projections. In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2000). https://arxiv.org/abs/2008.07669
• Gu, A., Goel, K., & Ré, C. (2022b). Efficiently Modeling Long Sequences with Structured State Spaces. In International Conference on Learning Representations. https://arxiv.org/abs/2111.00396

The Hybrid Frontier: Blueprints for a New Intelligence

• Hatamizadeh, A., & Kautz, J. (2024). MambaVision: A Hybrid Mamba-Transformer Vision Backbone. arXiv preprint arXiv:2407.08083. https://arxiv.org/abs/2407.08083
• Poli, M., Massaroli, S., Nguyen, P., Fu, D. Y., Dao, T., Thomas, S., … & Ré, C. (2023). Hyena Hierarchy: Towards Larger Convolutional Language Models. arXiv preprint arXiv:2302.10866. https://arxiv.org/abs/2302.10866
• Wu, B., Shi, J., Wu, Y., Tang, N., & Luo, Y. (2025). TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding. arXiv preprint arXiv:2506.09507. https://arxiv.org/abs/2506.09507

The Counter-Narrative: The Case for Pure Scale

• Zuo, J., Velikanov, M., Rhaiem, D. E., Chahed, I., Belkada, Y., Kunsch, G., & Hacid, H. (2024). The First Competitive Attention-free 7B Language Model. arXiv preprint arXiv:2410.05355. https://arxiv.org/abs/2410.05355

Key Concepts, Scaling Laws, and Commentary

• Dao, T. (2022). Stanford MLSys Seminar — Hardware-aware Algorithms for Sequence Modeling [Video]. YouTube. https://www.youtube.com/watch?v=FPTw15z_O7E
• Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., … & Sifre, L. (2022). Training Compute-Optimal Large Language Models. arXiv preprint arXiv:2203.15556. https://arxiv.org/abs/2203.15556
• Nebius. (2024). Transformer alternatives in 2024. Nebius. https://nebius.com/blog/transformer-alternatives-in-2024/
• Paul, R. (n.d.). Post-Transformer Architectures: Innovations. Rohan’s Bytes. https://rohan-paul.github.io/blog/post_transformer_architectures/
• Sutton, R. S. (199). The Bitter Lesson. Incomplete Ideas (blog). http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Disclaimer: The views and opinions expressed in this article are my own and do not necessarily reflect the official policy or position of any of my affiliates. I am a human, but I gratefully acknowledge the use of AI assistance in the research, drafting, and image generation for this article. This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Attention Isn’t All You Need Anymore: The Upcoming Era of SSM based GenAI was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.

This content originally appeared on Level Up Coding – Medium and was authored by Mohit Sewak, Ph.D.