Is DeepSeek the New Open Source or the New Electricity

Electricity was one of the most transformative technologies in human history.

When it was first introduced, many focused on its technical marvels – the ability to light up streets, power machines, and create new efficiencies.

But the real value of electricity wasn’t in the electricity itself; it was in what it enabled. It powered industries, revolutionized communication, and made entirely new forms of work and life possible.

The companies and societies that truly benefited weren’t those that simply generated electricity, but those that built upon it to create new applications and businesses that transformed the world.

Fast forward to today, and we see a similar narrative unfolding in artificial intelligence. The recent open source release of DeepSeek V3, a massive new generative AI model from a Chinese startup, set off a wave of excitement and anxiety.

Observers hailed it as proof that open source could be the “great equalizer” in AI – a way for upstart innovators to rival tech giants at a fraction of the cost. DeepSeek’s model reportedly rivals the capabilities of OpenAI’s best, yet was built using far less money and compute than conventional wisdom deemed necessary. In theory, this is the open-source dream come true: shared technology lowering barriers and leveling the playing field.

But as the dust settles, it’s clear the reality is more complicated.

Is DeepSeek Making Open Source Fashionable Again

Why the reality behind DeepSeek’s open source model is more complicated than the hype

Electricity transformed America, driving industrial expansion, improving quality of life, and reshaping society. In the late 19th century, Thomas Edison’s Pearl Street Station (1882) and Nikola Tesla’s AC power transmission (1895) enabled city-wide electrification. Urban centers thrived as factories adopted electric motors, businesses extended hours under bright lights, and streetcars expanded transportation networks.

However, rural America remained in the dark. By 1930, only 10% of farms had electricity, limiting agricultural productivity and daily life. The Rural Electrification Act (1935) changed this, bringing power to the countryside, and enabling electric milking machines, refrigeration, and radios. By 1950, 90% of farms were electrified, boosting economic output and social well-being.

By the mid-20th century, electricity had become universal. Factories optimized production, households embraced electric appliances (refrigerators, washing machines, air conditioners), and communication flourished with radios and televisions. Electrification also spurred urban growth—electric elevators enabled skyscrapers, neon signs lit up city nightlife, and air conditioning fueled the Sun Belt boom.

As America entered the digital age, electricity-powered computers, the internet, and data-driven industries. Today, the focus is on modernizing the grid, integrating renewable energy, and expanding electrification into new areas like electric vehicles.

We are all looking at how AI might improve our quality of life, and right now, we need it more than ever. For more than a decade, U.S. economic growth has suffered from a lack of both automation and new general-purpose technologies (GPTs, yep that’s a convenient pun), which unleash “creative destruction” on a massive scale throughout the economy. But AI is set to reverse that trend.

Impact over Information Technology

Electricity transformed America not because of the technology itself, but what it enabled. Rural electrification bridged divides, powered industries, and created entirely new possibilities for society. The winners weren't those who simply generated power—they were those who built upon this infrastructure to create new applications that changed how we live and work.

Today, AI stands at a similar inflection point.

When DeepSeek released its massive 671 billion parameter AI model under an MIT license, the tech world erupted in celebration. Here, finally, was proof that open source could challenge Big Tech's AI dominance at a fraction of the cost. DeepSeek's model rivals OpenAI's capabilities while reportedly using significantly less compute than conventional wisdom suggested was necessary.

A true David versus Goliath story—or so it seemed.

It caused an unbelievable economic impact, dropping nearly a trillion dollars off the value of companies like NVIDIA.

How DeepSeek Is Reshaping the AI Landscape

DeepSeek's contribution goes beyond just releasing another model. They've fundamentally changed the efficiency equation for the entire industry. By demonstrating that state-of-the-art performance can be achieved with significantly fewer computational resources, they've challenged the assumption that AI progress requires ever-increasing amounts of computing and capital.

This recalibration benefits everyone:

1. The accelerating innovation cycles: When techniques like DeepSeek's reduce the resources needed for cutting-edge AI, the pace of experimentation and improvement increases across the board.

2. Lowering the barrier to entry: Academic researchers and smaller companies can now do more with less, potentially diversifying who contributes to AI advancement.

3. Improving sustainability: More efficient models mean less energy consumption and a smaller carbon footprint—critical for AI's long-term growth.

4. Creating competitive pressure: Established players must now respond to these efficiency benchmarks, potentially benefiting consumers through better or less expensive AI products.

By publicly sharing their approach, DeepSeek has effectively raised the bar for the entire industry. Their work isn't just a single step forward—it's a catalyst forcing everyone to rethink fundamental assumptions about AI development.

The Cookie Recipe Versus the Bakery

DeepSeek didn't just release a model; they shared the entire 'cookie recipe.' Their technical innovations are now available for researchers and developers to study and incorporate into their own models.

Understanding DeepSeek's Breakthroughs (No PhD Required)

What makes DeepSeek's model so remarkable isn't just its performance, but how it achieved that performance. Their innovations represent significant advances in efficiency and capability that could reshape how AI models are built. Each breakthrough addresses a specific challenge in developing large language models, essentially finding clever shortcuts that maintain or improve quality while reducing computational demands. Here's what their innovations mean in practical terms:

Multi-Head Latent Attention: Imagine traditional AI attention as looking directly at words in a sentence. DeepSeek's approach is more like understanding the underlying concepts and connections between ideas rather than just the words themselves—like reading between the lines. This helps it grasp meaning more effectively.

Better Expert Management: DeepSeek uses a "committee of experts" approach (called Mixture-of-Experts or MoE) where different neural networks specialize in different tasks. Their innovation makes these experts work together more efficiently without needing complicated rules to balance their workload—like a self-organizing team that naturally distributes tasks without a manager.

Multi-Token Prediction: Instead of predicting one word at a time, DeepSeek can predict multiple words simultaneously. Think of it as the difference between a person who needs to complete each thought before starting the next versus someone who can see several steps ahead in a conversation.

FP8 Precision Training: This is about doing more with less. By using a more efficient way to store numbers in the model (8-bit instead of 16 or 32-bit), DeepSeek dramatically reduces memory usage and speeds up processing while maintaining quality—like compressing a high-resolution image without losing important details.

These innovations aren't just technical curiosities—they're practical solutions to the industry's most pressing challenges: efficiency, cost, and accessibility. By publishing these techniques with working code, DeepSeek has effectively democratized access to cutting-edge AI research that would otherwise remain locked behind corporate walls.

The industry-wide benefits are already becoming apparent. We're seeing researchers at universities without billion-dollar budgets implementing these techniques. Startups are incorporating these methods to stretch their limited resources further. Even big players are reassessing their approach to model development using these efficiency techniques.

This is open source's greatest strength: breakthroughs by one team can quickly benefit the entire community. The recipe is out there for everyone to use.

But having a recipe doesn't make you a master baker.

Open Source Ambitions Meet Complex Reality

True open source success requires more than code availability—it demands an engaged community of contributors refining and building upon the foundation. Despite its MIT license, DeepSeek V3 remains largely driven by its original creators, lacking the robust ecosystem that defines transformative open source projects.

The model's enormous scale and complex Mixture-of-Experts design create significant barriers to entry. Running or fine-tuning such a model demands expertise and hardware that few possess. Without grassroots adoption and contribution, DeepSeek risks becoming a technological curiosity rather than a true democratizing force.

Enterprise Models: The Master Bakers Benefit Most

While DeepSeek has made headlines, established enterprise AI providers like IBM with its Granite models may be the real beneficiaries of this open source windfall. And they're not alone—Google's Gemini, Anthropic's Claude, and Microsoft-backed OpenAI all stand to gain from these publicly shared advancements.

The advantage lies in their ability to adapt the recipe. When IBM and other major players incorporate DeepSeek's innovations into their existing model architectures, they're not just copying—they're refining. They can selectively implement techniques like Multi-Head Latent Attention or FP8 precision training while keeping their company specific special ingredients that give their models their unique capabilities and efficiencies.

It's like master bakers taking a novel technique for creating flakier pastry and incorporating it into their signature recipes. The original innovation improves their product, but the end result remains distinctly theirs—and potentially better than the original.

For enterprise providers, DeepSeek's openness represents a research accelerator without the corresponding R&D costs. Meanwhile, their established infrastructure allows them to scale these improvements across industries in ways startup models simply cannot match. They have the computing resources, the enterprise relationships, and the deployment expertise to translate these technical advancements into business value.

This dynamic highlights a fundamental truth in the AI ecosystem: innovation and implementation often follow different paths. The companies that win may not be those making the most radical breakthroughs, but those best positioned to adapt and apply them effectively across real-world use cases.

Beyond the Hype: What This Really Means

Just as electricity's true value wasn't in the watts but in what people built with it, AI's transformative potential lies not in having models but in what we do with them. DeepSeek has shared valuable some techniques—the recipe—but building the "bakery" that produces real-world impact requires more.

The companies and societies that will truly benefit from AI won't be those that simply train large models, but those that leverage these technologies to create new applications and solve meaningful problems.

Open source can indeed be a great equalizer—but only when it fosters genuine community, accessibility, and collective innovation. DeepSeek has taken an important step, but the journey toward truly democratized AI has only begun.

Like electricity, AI's greatest impact won't come from the technology itself but from how we harness it to transform our world.

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I am currently working on expanding the Artificially Intelligent Enterprise. And I want to think through ways I might make that happen. So I created a prompt to help me with that.

This prompt would work well with any of the deep research capabilities in ChatGPT, Google Gemini, or DeepSeek. 

Also, you could turn this prompt into an agent and use sources from business, your website, and other knowledge. Just cut and paste the prompt into a CustomGPT and add knowledge or use an agent platform like CrewAI or Taskade.

# **Prompt: The Competitive Intelligence Matrix**

## **Role:**  
You are a **strategic consultant** specializing in competitive intelligence, market positioning, and data-driven strategy.

## **Objective:**  
Analyze **[MARKET]** by leveraging AI-driven insights to accelerate market research, reduce costs, and optimize competitive positioning.

## **Instructions:**  
1. **Map Competitor Weaknesses**  
   - Identify key vulnerabilities of top competitors.  
   - Assess product gaps, customer complaints, and operational inefficiencies.

2. **Measure Brand Perception**  
   - Evaluate sentiment analysis from customer reviews, social media, and industry forums.  
   - Compare brand trust, recognition, and loyalty.

3. **Calculate Market Share Potential**  
   - Estimate current market share of competitors.  
   - Identify growth opportunities based on industry trends and consumer behavior shifts.

4. **Identify Untapped Segments**  
   - Detect underserved demographics, geographies, or niche customer needs.  
   - Suggest new market entry points or tailored offerings.

5. **Recommend Positioning Strategy**  
   - Define a unique value proposition based on insights.  
   - Suggest differentiation tactics to capture market share effectively.

I appreciate your support.