DeepSeek’s emergence challenges the AI industry’s status quo, proving that innovation isn’t solely driven by billion-dollar investments. Developing a competitive model at a fraction of the cost signals a shift where efficiency and strategic execution rival raw spending power. This raises critical questions: Are we entering an era where lean AI models can compete with OpenAI, Google DeepMind, or Anthropic?
Despite its promise, DeepSeek faces security and ethical concerns, struggling with politically sensitive topics and content safeguards. Yet, its cost-effective approach could disrupt industries like cybersecurity, education, and small business automation.
In this interview, Neil Roseman, CEO of Invicti, discusses whether DeepSeek represents an inevitable trend in AI efficiency—or a strategic shift that forces U.S. companies and venture capitalists to rethink their approach to AI innovation.
DeepSeek is positioned as a reality check for the AI industry. What specific aspects of its development and pricing make it a wake-up call for U.S. companies and venture capitalists?
DeepSeek’s emergence highlights something crucial about the AI industry: innovation isn’t limited to companies with massive resources. Their ability to develop competitive models at a fraction of the cost challenges the assumption that AI advancement requires billions in investment. This serves as a reminder that efficiency and focused development can sometimes achieve what raw spending power cannot. For U.S. companies and VCs, it demonstrates that the barriers to entry in AI development are lowering, and success might depend more on strategic execution than pure capital deployment.
Do you see DeepSeek as part of an inevitable trend of AI tools becoming more efficient and cost-effective, or does it represent a strategic shift in how AI is being developed and deployed?
From my perspective, DeepSeek represents both trends simultaneously. The natural evolution of technology tends toward greater efficiency and cost-effectiveness – we’ve seen this pattern repeat across every major technological wave. However, DeepSeek’s approach also signals a strategic shift in AI development, emphasizing lean, focused development over the broader, more resource-intensive approaches we’ve seen from larger players. At Invicti, we’ve observed similar patterns in security technology, where targeted solutions often prove more effective than broader, more complex approaches.
In your view, how does DeepSeek compare to models from OpenAI, Google DeepMind, or Anthropic in terms of capabilities, efficiency, and real-world application?
While DeepSeek-R1 shows competitive capabilities in technical benchmarks, post-launch testing has revealed significant concerns. The model demonstrates poor performance in handling politically sensitive topics and preventing harmful content. Recent successful jailbreak attempts suggest its safeguards are less robust than other leading AI platforms. Though it offers promising capabilities in mathematics and coding, these security and safety issues highlight that benchmark performance alone doesn’t indicate real-world deployment readiness.
Are there particular sectors or industries where DeepSeek’s approach will have the most immediate impact?
The most immediate impact will likely be in sectors where cost sensitivity meets the need for specialized AI capabilities. This includes smaller businesses, educational institutions, and research organizations that previously found enterprise AI solutions prohibitively expensive. In the cybersecurity space, where Invicti operates, we’re particularly interested in how more efficient AI models could enhance security testing and vulnerability detection without adding significant overhead to our client’s operations.
You mention that AI is demanding enormous resources but hasn’t delivered revolutionary applications. Are there any areas where you see AI providing meaningful, tangible benefits today?
Absolutely. In cybersecurity, we’re seeing AI deliver substantial value in vulnerability detection and threat analysis. It’s enhancing our ability to identify security flaws in web applications at scale, something that was previously limited by human capacity. Beyond our industry, AI is making meaningful contributions to medical diagnosis, drug discovery, and process optimization across various industries. The key is that these successful applications tend to be focused, well-defined problems where AI augments human capabilities rather than attempting to replace them entirely.
If you had to predict AI’s role five years from now, what do you think will be the biggest misconception people currently have about its future?
The biggest misconception is probably the idea that AI will evolve as a monolithic force that either dramatically disrupts everything or fails to live up to its promise. What we’re likely to see instead is a landscape of specialized AI solutions, each carefully optimized for specific use cases. At Invicti, we’ve learned that the most effective technology solutions are often those that solve specific problems exceptionally well rather than trying to be all things to all people.
Is there a fundamental rethink needed in how AI is developed and deployed, or is this just a phase of recalibration before the next big leap?
Rather than a fundamental rethink, what we’re seeing is a healthy maturation of the AI industry. The initial phase of AI development was characterized by broad experimentation and resource-intensive approaches, which were necessary to understand what was possible. Now, we’re entering a phase where efficiency and practical application take centre stage. This isn’t so much a recalibration as it is an evolution toward more sustainable and practical AI development practices.
