This week, a Chinese AI company called Deepseek dropped a bombshell: they created an AI model matching OpenAI's capabilities at a fraction of the cost¹ . While this news sent tech stocks tumbling and caused the largest single-day valuation drop of any company in history² , I believe it's actually fantastic news for AI startups. Here's why.

The AI landscape has been evolving at a dizzying pace since ChatGPT shocked the world in November 2022³ . We've witnessed Nvidia's meteoric rise⁴ , a parade of increasingly powerful models, and now, Deepseek's R1 breakthrough that's reshaping the economics of AI. While market turbulence has some investors nervous, I see this as an incredible advancement for startups like Attrove and countless others building AI-powered applications.

Think of it this way: just as the plummeting cost of computer chips enabled the personal computer revolution, the rapidly declining cost of AI computation is about to unlock a new wave of innovation. And unlike the early days of computers when only large companies and governments could afford the technology⁵ , today's AI breakthroughs are actually leveling the playing field between tech giants and nimble startups.

The implications? They're profound, and they follow a pattern we've seen throughout tech history. Let’s unpack that.

As History Shows: Technology Costs Always Drop

Since I mentioned the personal computer revolution, let's dig deeper into this pattern. One of the most consistent trends in the tech world is the downward trajectory of costs over time. While this phenomenon stretches back through history (from the printing press to smartphones), the computer industry offers the perfect parallel to what we're seeing with AI today.

In the early days of computing, IBM mainframes cost millions⁶ and filled entire rooms. That all changed due to two relentless forces:

• Moore’s Law (1965): Gordon Moore observed that the number of transistors in an integrated circuit doubles roughly every two years, leading to exponential improvements in computing power and simultaneous declines in relative costs. This pattern has held remarkably steady for over half a century.⁷
  

• Data Storage Costs: In the 1950s, 1 MB of storage cost nearly USD $10,000 (adjusted for inflation). As of the 2020s, storing 1 MB costs a fraction of a cent. This million-fold decrease enabled everything from smartphones to cloud computing.⁸

How does this affect AI startups today?

Just as falling hardware costs enabled the PC and internet revolutions, we're seeing the same pattern with AI. As hardware, cloud computing, and infrastructure become cheaper, the barrier to entry for running sophisticated AI models continues to fall. The Deepseek R1 announcement this week isn't an anomaly—it's part of this same historical pattern of technological deflation.

This creates a perfect environment for innovation: smaller companies can now spin up advanced AI capabilities for a fraction of what it cost even a few months ago. And just like the PC revolution didn't stop at cheaper hardware, the AI revolution won't stop at cheaper models. The capabilities keep improving while the costs keep dropping.

LLMs: Getting Better, Faster, and Cheaper

Have you tried ChatGPT or Microsoft (or GitHub) Copilot recently? The improvement from their early 2023 releases is striking. What used to struggle with basic coding tasks can now architect entire systems (with a bit of human-in-the-loop guidance), and what once gave superficial answers now provides nuanced analysis. This rapid improvement in Large Language Models (LLMs) isn't just about quality—it's about economics.

Let's break down what's happening:

• Model Improvements: From OpenAI's GPT series to Google's Gemini, each new iteration brings better reasoning, improved accuracy, and more natural communication. But here's what's really exciting: these improvements are arriving faster than predicted and often come with surprising efficiency gains. Models that once required massive computing resources can now run on much lighter infrastructure.⁹

• Cost-Performance Ratio: Early AI deployments were prohibitively expensive, requiring massive data centers for even basic tasks. Now, breakthroughs in model compression (think of it like zip files for AI), distributed training, and specialized hardware have slashed the cost per query. What once cost dollars now costs pennies—and with Deepseek's breakthrough, we're seeing another dramatic drop.^{10, 12}

What does this mean if you're building an AI startup?

First, you can now process more user queries and handle larger datasets without breaking the bank. Tasks that would have burned through your seed funding in weeks are now economically viable. Second, and perhaps more importantly, you can iterate faster. When each test run costs less, you can experiment more freely, finding what really works for your users.

Companies are automating everything from legal document review to customer support to preliminary data analysis, freeing up humans for more strategic work. And with each cost reduction like Deepseek's R1, new use cases become viable.

The Deepseek R1 Panic and Jevons Paradox

Let's dive a little deeper into this week's Deepseek R1 announcement. While some headlines focused on the market panic that ensued¹¹ , the real story is what this means for the AI ecosystem. The R1 model is a reasoning model that’s roughly on-par with OpenAI’s GPT-o1 model. Why all the excitement if it isn’t some fancy new frontier model with better capabilities? In one word: cost. It took only $6 million to train this model, while OpenAI’s GPT-4 model cost around $100 million¹² .

To put this in perspective, imagine if someone figured out how to make a Tesla that performs just as well but costs the same as a Smart Car. That's essentially what Deepseek accomplished in the AI world.

The immediate market reaction was vast uncertainty—particularly for companies like Nvidia whose chips power most AI systems. The logic went: "If we can do more with less computing power, won't that hurt chip makers?" But this thinking misses a fundamental economic principle known as Jevons paradox.

Jevons Paradox in Action

William Jevons, a 19th-century economist, noticed something counterintuitive about coal usage: as steam engines became more efficient, coal consumption increased rather than decreased.¹³ Why? Because improved efficiency made steam power cheap enough to use in new ways nobody had considered before.

We're about to see the same thing with AI:

1. Lower Barriers: Reduced costs mean more startups can experiment with advanced AI features, leading to more innovation

2. Deeper Integration: As AI gets cheaper, companies will embed it into more workflows and products

3. New Use Cases: Applications that weren't economically viable at previous price points suddenly become possible

For example, a startup might now be able to run sophisticated AI analysis on every customer interaction rather than just high-priority ones. Or process massive loads of communication data rather than just recent emails. The cheaper it gets, the more we'll use it.

Now Is the Time for AI Application Startups

"But wait," you might be thinking, "isn't all this rapid change actually a risk? What if what we build today is obsolete tomorrow?" This is exactly why now is the perfect time for AI application startups—if you position yourself correctly.

Here's the key insight: For every new breakthrough like Deepseek R1, well-positioned AI applications get an automatic upgrade in capabilities, often without changing a line of code. Think of it like building a car company just as gas engines were getting dramatically more efficient—you get the benefits of better engines without having to invent them yourself.

Build Beyond the Interface

The most successful AI applications today succeed by expertly applying foundation models to specific problems, rather than trying to build new AI from scratch. But there's a catch: I often hear concerns that being "just a wrapper" around language models is risky—that you'll get displaced as models improve. Here's how to ensure that doesn't happen:

• Focus on solving specific industry problems that require deep domain expertise

• Create proprietary data workflows that get better with each user interaction

• Build features that take advantage of cheaper AI in ways generic tools can't

Let me share how we're putting this into practice at Attrove. We're building organizational AI for busy people, but our value goes far beyond the AI models we use. Our competitive advantage comes from:

• Understanding each user's unique workflow and creating personalized experiences

• Deep expertise in how product organizations, managers, and leaders actually work

• Specialized workflows designed for different roles and team structures

• Interfaces built around real-world product team collaboration

• A growing dataset of workplace interactions that makes our system smarter

This approach means that every AI breakthrough (like Deepseek R1) makes our core features better, while our specialized knowledge creates a moat that generic AI can't easily cross. We're not competing with foundation models—we're making them more valuable for a specific use case.

Closing Thoughts

We're witnessing something remarkable in the AI industry—a virtuous cycle where costs plummet while capabilities soar. The Deepseek R1 announcement last week is just the latest evidence that this pattern is accelerating. While market turbulence makes headlines, the underlying trend is clear: AI is becoming more accessible to companies of all sizes.

For startup founders, this creates a unique opportunity:

• Each breakthrough in foundation models automatically improves your application

• Declining costs let you serve more customers and try new features

• The pace of innovation means first-movers have a real advantage

• Domain expertise becomes more valuable as the technology becomes more accessible

Every morning, I read about new models, improved frameworks, and better infrastructure that make AI more powerful and cost-effective. Rather than finding this pace overwhelming, I'm energized by it. Each advancement expands what's possible for those of us building AI applications.

At Attrove, we're putting these principles into action, leveraging each AI breakthrough to enhance our workplace intelligence platform. Features that were cost-prohibitive just months ago are now central to our product. And that's the real opportunity here—not just to use AI, but to make it truly useful in specific, valuable ways.

The AI revolution isn't just about the technology getting better; it's about making it more accessible to entrepreneurs who can apply it in novel ways. If you're considering building an AI startup, there's never been a better time to start. The tools are getting better, the costs are dropping, and the opportunities are expanding.

Interested in seeing how we at Attrove are putting these ideas into practice? Our closed beta is currently accepting new users. Reach out and let's discuss how these AI advances could help transform your workplace.

1: https://www.reuters.com/technology/artificial-intelligence/what-is-deepseek-why-is-it-disrupting-ai-sector-2025-01-27/

2: https://www.forbes.com/sites/dereksaul/2025/01/27/biggest-market-loss-in-history-nvidia-stock-sheds-nearly-600-billion-as-deepseek-shakes-ai-darling/

3: https://simple.wikipedia.org/wiki/ChatGPT

4: https://markets.businessinsider.com/news/stocks/nvidia-stock-price-soars-record-valuation-overtakes-apple-nvda-ai-2024-11

5: https://en.wikipedia.org/w/index.php?title=History_of_personal_computers

6: https://www.righto.com/2019/04/iconic-consoles-of-ibm-system360.html

7: https://en.wikipedia.org/wiki/Moore%27s_law

8: https://mkomo.com/cost-per-gigabyte

9: https://www.forbes.com/sites/markminevich/2023/12/20/googles-gemini-challenging-openai-chatgpt-and-changing-the-game/

10: https://brisktechsol.com/cost-to-setup-ai-data-center/

11: https://www.msn.com/en-za/news/other/deepseek-forced-nvidia-stock-plunge-sparks-market-panic/ar-AA1y1RIj?ocid=BingNewsSerp

12: https://mashable.com/article/what-ai-experts-saying-about-deepseek-r1

13: https://en.wikipedia.org/wiki/Jevons_paradox

There’s a reason 400,000 professionals read this daily.

Join The AI Report, trusted by 400,000+ professionals at Google, Microsoft, and OpenAI. Get daily insights, tools, and strategies to master practical AI skills that drive results.

Sign up now for free and work smarter, not harder.