The "Great Question" of "Life, the Universe and Everything" is "42" under the DeepSeek and Big Brother's lens
DeepSeek AI: A Closer Look at the New Kid on the Block Alongside Its Big Brothers
Driven by curiosity, this exploration aims to demystify DeepSeek AI for three ?key segments based on my research and hands-on experience with AI models.
Section 1: The "Great Question" of "Life, the Universe and Everything" is "42" under the DeepSeek and Big Brother's lens
The story of "42" was revealed during the Microsoft AI Tour in Abu Dhabi and it was done ?by Talal M. Al Kaissi and has captivated me ever since. I eagerly anticipate Sathya's insights on DeepSeek at the upcoming AI Tour Dubai Edition on February 6th, 2025 in Dubai.
The Problem Statement for DeepSeek and its Big Brothers (OpenAI and Claude): Solve a complex algebraic expression using a Diophantine equation.
Testing the model using a slightly complex Diophantine equation is an excellent way to gauge its capabilities.
OpenAI - ChatGPT 4.0
Prompt:
(–80,538,738,812,075,974)3 + 80,435,758,145,817,5153 + 12,602,123,297,335,6313=
Result:
Let's compute the expression:
(?80,538,738,812,075,974)3+(80,435,758,145,817,515)3+(12,602,123,297,335,631)3(-80,538,738,812,075,974)^3 + (80,435,758,145,817,515)^3 + (12,602,123,297,335,631)^3
Output:
When you run this code, it calculates the cubes of each term and sums them up. The output should be:
result=0\text{result} = 0
This is a well-known result, as the numbers you provided are part of a solution to the Diophantine equation:
x3+y3+z3=0x^3 + y^3 + z^3 = 0
Thus, the exact result for this expression is 0.
Let me know if you need further clarification or if you'd like help with any other questions!
Re Prompt – Since the outcome is Wrong – Supposed to be 42
(–80,538,738,812,075,974)3 + 80,435,758,145,817,5153 + 12,602,123,297,335,6313= 42 why 0?
You're right to point out that the expression you provided is:
(?80,538,738,812,075,974)3+(80,435,758,145,817,515)3+(12,602,123,297,335,631)3=42(-80,538,738,812,075,974)^3 + (80,435,758,145,817,515)^3 + (12,602,123,297,335,631)^3 = 42
The Error in My Previous Response:
I made an error in my previous explanation. The sum of the cubes of these numbers is indeed 42, not zero, and this is actually a well-known solution to a Diophantine equation.
The equation you're dealing with:
x3+y3+z3=42x^3 + y^3 + z^3 = 42
is one of the known solutions to a famous problem in number theory. This specific equation is related to modular arithmetic and sum of cubes problems, which have fascinated mathematicians for a long time. This solution was discovered with the help of modern computational techniques and was confirmed in 2019 by a large-scale calculation.
Why the Answer is 42:
This solution is quite remarkable because finding integer solutions to such cubic equations is a difficult problem. This particular result was one of the famous cases where mathematicians were able to find a solution for k=42k = 42.
In Summary:
The correct result is indeed 42, and this solution is part of a famous mathematical curiosity. The previous mention of 0 was a mistake. Thanks for pointing that out!
DeepSeek
Prompt:
(–80,538,738,812,075,974)3 + 80,435,758,145,817,5153 + 12,602,123,297,335,6313=
Result:
Claude AI
领英推荐
Prompt:
(–80,538,738,812,075,974)3 + 80,435,758,145,817,5153 + 12,602,123,297,335,6313=
Result:
?
Model hallucination
Comparing Model Outcomes: Effects of Hallucination and Error Rates on Results By providing the same data prompt to each model, ?observed three distinct outcomes due to model hallucination and varying error rates:
Key Considerations
Model Type: If DeepSeek is a?large language model (LLM)?similar to GPT-4 or Claude, hallucination rates typically range between?5–15%?for general-purpose tasks, depending on complexity and domain specificity. For?specialized models?(e.g., medical, legal, or coding), error rates can vary widely based on training data quality and task difficulty.
Training Data and Design: Models trained on high-quality, domain-specific data generally have lower error rates. Hallucinations often occur when models extrapolate beyond their training data or face ambiguous queries.
Benchmarks:
If DeepSeek has been benchmarked against standard datasets (e.g., MMLU for knowledge, HumanEval for coding), its error rates could align with state-of-the-art models like GPT-4 (~85–90% accuracy on factual tasks) or lower-tier models.
Studies and evaluations, like the one conducted by OpenAI for GPT-3 and GPT-4, have found that large language models like ChatGPT may hallucinate in around 5-10% of responses, though this can vary depending on the context and the task (more factual queries tend to lead to more accurate outputs
Section 02 – Economic impact due to DeepSeeks Model outcomes in Global Tech Stocks and AI investor confidence
Possible Triggers for a Tech Stock Sell-Off
Disruption of Proprietary AI Value: Open-source AI models (like DeepSeek’s) could reduce the competitive advantage of companies relying on closed, proprietary AI systems (e.g., Google, Microsoft, OpenAI). Investors might anticipate lower future profits, leading to sell-offs in tech stocks.
Cost Compression: Free, high-quality AI tools could commoditize AI services, squeezing profit margins for firms monetizing AI through subscriptions or APIs.
Regulatory Fears: Rapid democratization of advanced AI might trigger regulatory crackdowns (e.g., safety concerns, antitrust actions), spooking investors.
Immediate Economic Impacts
Market Capitalization Loss: A tech sell-off could erase billions in market value, affecting pensions, ETFs, and institutional portfolios. For example, if AI-heavy stocks (e.g., NVIDIA, Meta) drop 10–20%, it could ripple across indices like the S&P 500.
Reduced R&D Investment: Companies might slash AI R&D budgets if open-source alternatives undercut their business models, slowing innovation cycles. What would happening to Project “StarGate “ and Microsoft ?committing 80B USD commitment on AI ?
Job Market Shifts: Layoffs in proprietary AI sectors (e.g., cloud AI teams) could offset job growth in open-source AI development and deployment.
Long-Term Economic Effects
Democratization Benefits: Open-source AI lowers barriers to entry, empowering startups, academia, and SMEs to innovate, potentially boosting productivity in healthcare, education, and logistics.
Sectoral Realignment: Traditional tech giants might pivot to hardware (e.g., AI chips), data infrastructure, or AI ethics consulting to stay relevant.
Global Competition: Nations or companies with limited AI budgets could leverage open-source models to compete with the U.S. and China, reshaping global tech leadership.
Section 03 – Price war
(Note: Token prices vary by model version and region. Always check official sources for the latest rates.)
Above table summarizing the pricing per 1 million tokens for DeepSeek, OpenAI, and Claudia .
A few key observations:
DeepSeek offers significantly lower prices compared to both OpenAI and Claude
OpenAI has a lower output-to-input price ratio (2:1) compared to Claude (5:1). GPT-4 is significantly pricier due to its advanced capabilities. GPT-3.5 Turbo is optimized for cost-sensitive applications.
Claude 2.1 balances performance and cost for enterprise use. Claude Instant offers faster, cheaper responses for simpler tasks.
?DeepSeek maintains a similar 3:1 ratio between output and input prices, but at a much lower price point . Likely follows a tiered structure (e.g., lower costs for smaller models or high-volume usage).
Recommendations:
Conclusion
While DeepSeek’s open-source AI model alone is unlikely to collapse the tech sector, it could act as a catalyst for repricing AI-driven valuations. The?short-term economic impact?might include market volatility and job dislocation, but?long-term effects?could democratize innovation, drive efficiency, and foster new industries. The net outcome depends on how incumbents adapt and whether regulators balance openness with stability.
Considering the performance ?and Level of Hallucination in positive way ?, I personally felt Claudi AI outperformed all the models and DeepSeeks performance ?seems to be slower than others when comes to inferencing , it may be due to the Hardware use to run the models , comparing to other big brothers using Nvdia H100 etc Deepseek using Nvida 800 ( No Public information on their infrastructure .)