Having spent the last two years deeply entrenched in determining how AI can and will be used in marketing, creativity, and digital product into the future—from ideation and strategy, to planning, tools, creativity, development, concepting, customized content creation and more—I thought I'd offer my two cents on the technological earthquake that is Deepseek, and discuss the resulting implications for the future of AI and AI development:
In the rapidly evolving world of artificial intelligence, a new player has emerged, stirring both admiration and controversy: DeepSeek. Unlike traditional models trained from scratch on massive datasets, DeepSeek was trained using outputs from existing, more powerful AI models, like OpenAI’s ChatGPT, Google Gemini, Anthropic Claude, Meta Llama and on. By focusing on data organization, subject-specific expertise, efficiency, and AI "specialists" within its core, DeepSeek aims to deliver a more streamlined, cost-effective, and scalable AI model.
Just days after launch, DeepSeek AI's assistant—a mobile app offering a chatbot interface for DeepSeek R1—soared to the top of Apple's App Store, dethroning OpenAI's ChatGPT. Its meteoric rise sent shockwaves through the market, triggering a major sell-off on January 27, 2025. Investors, suddenly questioning the dominance of U.S.-based AI giants, dumped shares in Nvidia, Microsoft, Meta Platforms, Oracle, and Broadcom, causing a ripple effect across the tech sector. The surge of DeepSeek forced a reassessment of AI valuations, proving that disruption in this space can come faster—and from more unexpected places—than anyone anticipated.
But this raises an important question: is DeepSeek a stroke of genius innovation, or is it blurring the ethical lines of AI development?
How DeepSeek Was Built So Cheaply?
Deepseek claims with only a $6M investment, it produced an industry leading AI, but how?
DeepSeek is a perfect example of standing on the shoulders of giants—it would not exist without the previous work of the industry’s most powerful AI models. Unlike OpenAI’s ChatGPT-4, Google’s Gemini, or Anthropic’s Claude, which were trained on hundreds of billions of proprietary data points, DeepSeek was able to shortcut the process by learning from already-trained AI models.
By leveraging model distillation, DeepSeek essentially compressed the knowledge and methodology of larger AI systems into a more streamlined, domain-specific intelligence and took the best results, capabilities, and data from each. Instead of wasting resources on redundant training, it has created a leaner, cheaper, and potentially more scalable alternative to the AI giants. This method significantly lowered the cost of development, while still maintaining high performance in specialized tasks.
In addition, one of DeepSeek’s most intriguing innovations is its use of AI specialists—a network of domain-specific expert models embedded within the broader system. Unlike monolithic AI models that attempt to be generalists, DeepSeek intelligently routes queries to specialized sub-models trained for distinct subject areas, whether it's medical research, legal analysis, coding, or creative writing.
This reduces computational waste, improves accuracy, and enhances efficiency by allocating tasks to the most relevant expert model. In essence, DeepSeek functions like an AI-powered think tank, where different “specialists” collaborate seamlessly to produce precise, contextually relevant responses. This architecture is not just a cost-saving measure—it’s a reimagining of how AI intelligence can be structured, pushing the industry towards a modular, efficiency-driven approach to AI development.
But where's the innovation? DeepSeek redefined the playbook for AI training. Instead of the traditional approach of training massive models from scratch on enormous datasets, DeepSeek introduces a more streamlined and cost-effective methodology while delivering exceptional reasoning capabilities. This efficiency comes from a series of key innovations:
Reinforcement Learning at Scale: DeepSeek leverages reinforcement learning to enhance reasoning, an approach that has significantly improved the model’s ability to understand complex tasks and provide more intelligent responses.
Reward Engineering: Unlike conventional AI training that relies on deep neural networks to define rewards, DeepSeek uses a rule-based reward system, which has been shown to outperform standard neural reward models.
Distillation & Compression: Through advanced knowledge transfer techniques, DeepSeek has been able to compress high-level AI capabilities into much smaller models—some as compact as 1.5 billion parameters—without significant performance degradation.
Emergent Behavior Networks: DeepSeek researchers discovered that complex reasoning patterns can emerge naturally through reinforcement learning, without needing explicit programming—a breakthrough that brings it closer to artificial general intelligence (AGI).
AI Specialists & Query Routing: DeepSeek integrates AI "specialists" within its architecture, optimizing query routing to deliver more precise and subject-specific responses while improving speed and efficiency.
Infographic: DeepSeek distillation model to AI "Specialist" Cores to data organization, subject-specific expertise, resulting in greater efficiency, and cheaper cost.
Deepseek AI Specialist distilled models, logic and methodology from OpenAI ChatGPT, Anthropic Claude, Meta Lama, and Google Gemini.
Imagine being able to take ChatGPT's ideation generation, Claude's stylistic copywriting capabilities, Wolfram Alpha's mathematics capabilities, and on and on, to produce a panel of experts from each field to answers every question, instead of just relying upon the capabilities from a singular competitor that tries to do everything by themselves. That's what Deepseek is.
However, this raises ethical questions—did DeepSeek innovate, or did it simply repurpose the intellectual labor of its competitors?
The Ethical Debate: Innovation or AI Plagiarism?
While DeepSeek’s efficiency is impressive, critics argue that it fundamentally relies on the intellectual labor of other AI systems without directly contributing to their development. The debate centers on whether AI models trained using distilled outputs from competitors constitute innovation or appropriation.
Is DeepSeek simply following best practices for AI training, akin to how human researchers build upon existing knowledge of each others research?
Or is this a new form of AI “plagiarism,” where a model’s success is built on the uncredited work of others?
The answer lies somewhere in the middle. AI development is inherently iterative, and DeepSeek is simply optimizing what already exists, much like how new artists remix, reinterpret, and evolve past works. However, as AI regulation grows, the ethics of model training will become an increasingly pressing issue.
The Underlying Technology: Limits as a Forcing Function for Creativity
Much of the conversation surrounding DeepSeek’s development revolves around its development costs and reported use of lower-end Nvidia’s H800 chips, a China-specific AI chip that was designed in response to U.S. export restrictions. The H800 is often framed as a "weaker" version of Nvidia’s flagship H100 GPU, with its primary limitation being slightly lower chip-to-chip bandwidth. However, this doesn’t actually mean DeepSeek was built using inferior technology—far from it.
Recent reports confirm that DeepSeek has access to at least 50,000 Nvidia H100 chips (this can't be openly discussed) in addition to its fleet of H800s. This debunks the narrative that DeepSeek was developed more cheaply using only limited hardware. Instead, it underscores a strategic blend of efficiency and raw power—utilizing H800 chips where high throughput isn't needed and leveraging the full might of H100 chips for advanced AI training and inference workloads.
This hybrid approach shows DeepSeek isn’t merely optimizing around limitations—it’s leveraging them as a strategic advantage.
"The enemy of art is the absence of limitations." —Orson Welles
Constraints breed creativity, and in this case, DeepSeek has turned resource allocation into an efficiency superpower rather than a handicap.
"Work smarter not harder," continues to be a lesson to learn for humanity. Perhaps because, a focus on efficiency only comes most naturally to us when you've reached a constraint in your resources, output, or your system. All of the major US AI firms have access to all the raw power they could want in hardware and financial resources, resulting in a focus on using brute force rather than data organization, information routing, and query pathing.
What this breakthrough unlocks is the ability to operate efficiently without requiring massive supercomputer clusters. Thanks to its advanced query routing, modular AI experts, and optimized data utilization, DeepSeek significantly reduces the computational overhead typically associated with large-scale AI models. Unlike its competitors, which rely on massive data centers and power-hungry GPUs, DeepSeek’s efficiency allows it to be deployed on more accessible hardware, including high-end laptops and desktop computers. This democratization of AI access means that businesses, researchers, and even individual users can harness its power without the need for multi-million-dollar infrastructure—marking a paradigm shift in how cutting-edge AI is utilized across industries.
What This Means for the AI Industry
DeepSeek’s success with lower-cost training, hybrid hardware strategies, chip efficiency, and targeted specialization presents a major challenge to AI incumbents like OpenAI, Google, and Anthropic. By proving that powerful AI models can be built more affordably and efficiently, DeepSeek is democratizing access to high-performance AI, potentially reshaping the competitive landscape of the industry.
However, its reliance on existing AI models, coupled with its massive H100-powered infrastructure, complicates the narrative. DeepSeek is not simply "doing more with less"—it is leveraging the best of both worlds, mixing efficiency and high-powered AI capabilities to compete at the top level.
Will AI companies be forced to copyright their model outputs to prevent distillation by competitors? Or will DeepSeek’s efficiency-first approach become the new industry standard?
One thing is certain: DeepSeek has opened Pandora’s box, and the AI world will never be the same.
What This Means for Marketing
DeepSeek’s development represents a new frontier in AI training methodology, proving that cutting-edge AI models don’t necessarily need the most expensive hardware or the largest datasets to be competitive. Instead, the company has given marketers a benefit in a number of financial and efficient to get more for less:
Hyper-Personalized Campaigns at Scale: DeepSeek’s optimized query routing allows for more precise and context-aware data analysis, making it significantly easier to develop hyper-personalized marketing campaigns. Instead of relying on broad audience segmentation, brands can use DeepSeek to create individualized messaging in real time, adapting to customer behavior, preferences, and purchasing intent with unprecedented accuracy.
Cost-Effective AI Deployment: The fact that DeepSeek can run on laptops and desktops means that even small marketing teams and startups can leverage enterprise-level AI capabilities without needing expensive cloud-based AI solutions. This levels the playing field, enabling lean teams to produce high-quality, AI-driven insights, creative assets, and automation with lower infrastructure costs.
AI-Powered Content Creation: DeepSeek’s architecture, which includes subject-specific AI experts, enables it to generate highly tailored content across different formats—blog articles, social media posts, ad copy, video scripts, and even interactive experiences. With better efficiency in its dataset use, DeepSeek could allow brands to create more compelling, brand-aligned content while significantly reducing turnaround times and production costs.
Real-Time Consumer Insights and Predictive Analytics: The model’s ability to process data more efficiently means brands can gather and act on real-time consumer insights faster than ever before. DeepSeek can identify emerging trends, predict shifts in consumer behavior, and provide actionable recommendations—allowing marketers to pivot campaigns dynamically and remain ahead of competitors.
AI-Powered Customer Support and Engagement: With its modular AI specialists, DeepSeek can transform chatbots, voice assistants, and automated customer engagement by making them more adaptive, nuanced, and conversational. Brands can implement AI-driven support that feels genuinely human, improving customer satisfaction and loyalty while cutting down on operational costs.
Ethical and Compliance Considerations: DeepSeek’s training methodology—leveraging other AI models—raises questions about originality, data integrity, and ethical AI practices. Marketers will need to consider how regulatory bodies and platform policies evolve regarding AI-generated content, ensuring transparency and compliance in their strategies.
Democratization of AI-Driven Marketing: With DeepSeek’s efficiency and ability to run outside of high-powered data centers, it could shift the landscape of AI in marketing from an elite tool reserved for tech giants to an accessible powerhouse for mid-sized businesses and independent creators. This could lead to more innovative and diverse marketing strategies, disrupting traditional advertising and brand-building approaches.
A New Era of AI?
DeepSeek is both a marvel of AI efficiency and a disruptor of AI ethics. It showcases the power of limitations, reinforcing Orson Welles’ famous quote: “The enemy of art is the absence of limitations.” By working within constraints, DeepSeek has created an AI model that is leaner, faster, and more adaptable.
I no uncertain terms, DeepSeek represents both a breakthrough and a challenge—an efficiency revolution born out of necessity, but also a controversial method of leveraging pre-existing AI. As the technology race continues, the balance between efficiency, ethics, and innovation will determine whether DeepSeek is remembered as a pioneering force or a disruptive wildcard in AI history.
Ultimately however, AI, no matter how advanced, is fundamentally a recycler of human creativity—it remixes, reshapes, and repackages ideas that already exist. It can mimic styles, predict patterns, and optimize efficiency, but it lacks the raw spark of originality that defines human innovation. AI doesn’t dream up entirely new concepts; it curates from the vast archives of human thought. True creativity—the kind that breaks boundaries, invents the unprecedented, and reshapes the world—remains the domain of people. It’s humans who see beyond the dataset, who take wild, intuitive leaps into the unknown, and who breathe life into ideas that AI could never predict. In the end, AI is an amplifier, a collaborator, but never the originator. The future belongs not to machines, but to those who wield them with imagination.
🚀 What do you think? Is DeepSeek a game-changer or an ethical gray area? Let’s discuss.