In early 2025, Chinese AI firm DeepSeek burst onto the scene with a smarter, cheaper model that stunned tech giants and investors alike. Its bold innovations didn’t just shake up the AI race — it signaled a new era where speed, ingenuity, and openness, rather than the size of teams and investments, would redefine the future.
The unveiling of DeepSeek AI in early 2025 wasn’t just another headline in tech news — it was an earthquake that rattled the very foundations of the global AI industry.
Like the sudden roar of an unseen wave hitting the shore, the emergence of this young Chinese startup caught even the Big Tech players — OpenAI, Google, Meta — off guard, with a momentum few could have imagined.
DeepSeek didn’t simply announce another powerful large language model. It claimed it could match — and in some cases outperform — the West’s finest, like GPT-4, at a fraction of the cost.
This wasn’t bravado. It was backed by groundbreaking architecture and hardware wizardry so efficient that Nvidia’s stock nosedived by 17% in a single day, erasing nearly USD600 billion from its market value.
This wasn’t just Wall Street reacting to rumours; it was a gut-level reaction to a new reality — one where the old belief that bigger gadgets and budgets trump all.
Analysts quickly dubbed it a “Sputnik moment” for AI — recalling 1957, when the Soviet Union’s unexpected satellite launch stunned the United States and ignited the space race.
DeepSeek’s rise similarly jolted Western tech giants, exposing a future where brainpower, not just brute power, could dominate.
As Giuseppe Sette, president of Reflexivity Research, put it: “DeepSeek has taken the market by storm by doing more with less. And with AI, the surprises are just getting started.”
“DeepSeek is democratizing AI and sparking a global debate on cost, accessibility, and innovation in the field. Whether you’re a developer, a business leader, or simply an AI enthusiast, keeping an eye on DeepSeek is essential as it continues to influence both technological and geopolitical landscapes.” says the company on its website.
Founded by Liang Wenfeng, the tech company emerged from the research arm of the Chinese hedge fund High-Flyer, with a focus on optimisation. Their approach is akin to winning a race with the least fuel—aiming to deliver high performance efficiently.
The company’s open-source R1 model is revolutionising AI by providing a cost-effective, transparent alternative to pricey closed-source models.
The R1 model uses reinforcement learning and real-time reasoning to explain how it makes decisions, making it easier for developers to understand. It’s more accessible and cost-effective, allowing a wider range of users to benefit from advanced AI without the high costs.
“DeepSeek is democratizing AI and sparking a global debate on cost, accessibility, and innovation in the field. Whether you’re a developer, a business leader, or simply an AI enthusiast, keeping an eye on DeepSeek is essential as it continues to influence both technological and geopolitical landscapes.” says DeepSeek.
Geopolitically also, DeepSeek’s breakthrough handed China a major win — proving that in tech wars, ingenuity can outmanoeuvre even the toughest hardware restrictions.
Rather than locking powerful models behind expensive paywalls, they aim to provide open-source alternatives that empower startups, developers, and businesses.
While other AI giants focus on scaling up with bigger datasets and models, DeepSeek focuses on efficiency, delivering competitive performance with minimal resource consumption.
DeepSeek took a different route. It redesigned the engine.
Instead of relying on one massive model to answer every question, DeepSeek employed a Mixture-of-Experts (MoE) architecture — imagine a call centre where, instead of everyone answering every call, the system smartly routes your call to the best-suited specialist. Only a small subset of the model — the relevant “experts” — gets activated for each task, slashing computational costs without hurting performance.
Then there was their Multi-head Latent Attention (MLA). Handling long conversations in AI is like remembering the entire plot of a TV series — earlier models struggled after a few episodes. MLA is like giving the AI a better notebook and memory map, allowing it to keep track of far more information, more coherently, for longer.
Perhaps DeepSeek’s boldest move was in hardware. Instead of relying on Nvidia’s prized H100 or A100 chips — think of them as the Ferraris of AI hardware, powerful but ultra-expensive and restricted — DeepSeek optimized its models for H800 GPUs, the Toyota Camrys of the AI world: reliable, cheaper, and widely available.
But how did they get Ferrari-like performance from a Camry? Enter PTX (Parallel Thread Execution) — Nvidia’s special programming language for telling GPUs how to juggle multiple tasks at once.
If a normal program treats a GPU like a one-lane road — one task at a time — PTX turns it into a multi-lane expressway, guiding traffic smartly to maximize speed. DeepSeek’s engineers rewrote their systems at this low level, getting the absolute best performance out of these less glamorous chips.
It’s like a racing team tuning an ordinary sedan to outperform a supercar by optimizing every gear, every piston, and every tyre. Combined with reinforcement learning — where the AI “teaches itself” by trial and error — DeepSeek automated large parts of its model training, again cutting time and costs dramatically.
The results were stunning. DeepSeek reportedly trained its flagship model, R1, for under USD 6 million, compared to the estimated USD 100 million it cost OpenAI to build GPT-4.
The follow-up, V3, came in at just USD 5.58 million. Despite these lower costs, DeepSeek’s models hit — and sometimes surpassed — performance benchmarks set by giants like OpenAI, Google, and Meta.
When DeepSeek launched their API, offering similar power at much lower prices, developers flocked to it like shoppers spotting a huge sale.
Meanwhile, Nvidia’s stock crash signaled deeper fears: If powerful AI could be built with cheaper chips and smarter code, what did that mean for Nvidia’s future growth, or for the trillion-dollar AI hardware ecosystem at large?
Other semiconductor stocks like AMD, Broadcom, and TSMC were also caught in the blast radius.
Yet, not everyone forecast doom for Nvidia. Some pointed to the Jevons Paradox — the idea that when something becomes more efficient, overall usage often increases.
In other words, if DeepSeek’s methods make AI cheaper and faster, more people might build more AI apps, ultimately boosting demand for chips, even if those chips aren’t the ultra-expensive ones.
And even DeepSeek, for all its efficiency, still relied on Nvidia’s H800 GPUs — a reminder that Nvidia remains crucial to the AI landscape, at least for now.
Some scepticism lingered too. Critics argued that DeepSeek’s reported training costs might be underestimated. But even allowing for some margin of error, the impact was undeniable.
DeepSeek’s rise sparked a global rethink. While in China the tech giant Alibaba didn’t want to be left behind in the AI race, and unveiled an updated version of its Qwen 2.5 AI model, claiming it outperforms the widely praised DeepSeek-V3.
In India, the “IndiaAI” mission gained new urgency, pushing for indigenous, efficient models. Across Europe, countries like France and Germany reassessed their AI strategies, considering whether leaner innovation could offer an edge. The EU, deep into AI regulation efforts, now faced a landscape where newcomers could upend the rules. Even Canada, a longtime AI leader, recognized that success might hinge more on sharp innovation than deep pockets.
