The release of Deepseek R1 has made headlines, but the unsung hero is its sibling: R1 Zero. While R1 showcases reasoning through human feedback, R1 Zero demonstrates something far more transformative—reasoning without human feedback. This works only in analytical tasks like math and coding, but it removes the cost of hiring humans for feedback. This shift is poised to divide the AI landscape into two:

  1. Analytical AI that will thrive without human feedback (like Deepseek R1 Zero).
  2. Creative AI that relies on human feedback for understanding human ambiguity (like ChatGPT-4).

This moment marks AI’s "Vertical SaaS" like transformation, but not in the way we expected. For years, we assumed AI verticalization would be domain-specific—targeting industries like healthcare, finance, or logistics. Instead, the future of AI lies in intelligence-specific verticals: analytical and creative intelligences, much like how people are—some are more left-brained (analytical), while others are more right-brained (creative).


How R1 Zero Achieves Deterministic Reasoning

Traditional AI models like GPT rely on probabilistic reasoning, predicting the most likely sequence of words or outcomes. This requires human feedback to address the ambiguities of human language through a technique called supervised fine-tuning (SFT). R1 Zero, however, focuses on verifiable outcomes like chess or math using reinforcement learning (RL). Here’s how it works:
  1. Reinforcement Learning with Binary Rewards:
    R1 Zero trains on tasks where outputs are either correct or incorrect, like solving math problems or determining the best chess moves to win a game. This allows the system to refine its reasoning iteratively without relying on expensive, human-labeled datasets.
  2. Execution and Validation:
    After generating a solution, R1 Zero executes it (e.g., running a program or applying a formula) to confirm its correctness. This ensures repeatable, deterministic outputs—a stark contrast to the variability of LLMs.
  3. Narrow Focus:
    R1 Zero excels in structured tasks like coding or financial modeling by avoiding ambiguous, subjective problems. Its narrow specialization enables higher accuracy and efficiency.
For example, in the endgame, chess engine - Stockfish acts like a deterministic algorithm, using precomputed tablebases to make perfect moves with absolute certainty. Similarly, R1 Zero determines the best move or solution in structured tasks like math or coding—without human feedback—ensuring repeatable, correct outputs every time. (For more details, read chess as a leading indicator of superintelligence article).


Why R1 Zero Matters More Than R1


  1. Analytical Intelligence Will Be a Cottage Industry:
    By avoiding reliance on costly data labeling or human feedback, R1 Zero allows smaller companies to build low-cost, deterministic AI systems. Startups can now create AI for niche tasks like coding or math because they don’t need to hire people to clean up the data for training. They can run reinforcement learning, which only requires compute costs instead of people costs.
  2. Superintelligence Will Be Like a Ferrari:
    While LLMs like GPT excel in creative and ambiguous tasks, R1 Zero specializes in precision and logic. Together, they could form a 'mixture of experts' (MoE), where analytical tasks would be routed to an AI model like R1 Zero, while creative tasks would be handled by a GPT-like AI model. This combination is what I call as the hybrid approach in my superintelligence article. This would be invaluable for high-end tasks like scientific invention, but doing both might be expensive. Analytical intelligence startups may have a pricing advantage here, forcing LLM providers to introduce token-based pricing specifically for analytical tasks. However, such a shift would complicate its pricing model.
  3. Will Desktops Replace Mainframes?
    Eventually, analytical intelligence like R1 Zero might become cheaper to build and deploy on low-end devices like desktops, robots, or phones. They may not even need to connect to multi-billion-dollar AI clusters. This is reminiscent of desktops taking over mainframes, as explained by Clayton Christensen in his book The Innovator’s Dilemma, where disruption happens when a cheaper product—like analytical intelligence—becomes increasingly capable, pushing super intelligence into niche applications and eventually making it less relevant.
In essence, this is a vertical SaaS moment for AI. We are witnessing the birth of intelligence-specific verticalization:
  1. Analytical Intelligence: Deterministic reasoning for math, logic, and coding. (e.g., R1 Zero)
  2. Creative Intelligence: Generating ideas, designs, and narratives. (e.g., ChatGPT-4)
  3. Artistic Intelligence: Producing visuals, music, and other forms of artistic expression. (e.g., DALL-E)
This shift mirrors the rise of vertical SaaS in software, where tools went from Shopify (eCommerce) to Toast (Restaurants) to Slice (Pizzerias) and became narrowly focused on one vertical. Similary, AI will become specialized intelligences, with analytical intelligence leading the way.

Summary

So far, humans have economically valued analytical skills more than artistic skills, leading to a world where logical reasoning and structured problem-solving dominate white-collar jobs. But what happens when analytical AI becomes not only smarter but also far cheaper than artistic AI?
Consider this: If someone uses AI to send you a poetic compliment, you might ignore it or even view it as disingenuous. This suggests that creative jobs that require human touch may still hold unique value. However, would you feel the same if an AI provided superior stock analysis? Unlike creative tasks, analytical jobs face less resistance to automation since accuracy and efficiency matter more than human authenticity. If AI takes over analytical tasks and humans merely oversee its output, will we still regard these roles as highly when the “intelligence” behind them is automated?

This isn’t just a shift in how we think about AI—it’s a shift in how we value human skills. Analytical intelligence is coming, and it will reshape not just industries but our economy and our sense of purpose in a world where reasoning machines take over tasks we once prized most.