The Business Impact of DeepSeek: Chips, Data Centers, and AI Use Cases

The DeepSeek Breakthrough and Why It Matters

I had been using DeepSeek since mid-December and talked about it in one of my earlier post - at that time DeepSeek had not open sourced their model. I did find myself using DeepSeek a lot more since it was probably closest to ChatGPT O1 and my use of Claude (it still does not have a internet search button to get the latest info) and Google Gemini (not insightful enough). At that time, I did not read their paper though I knew they had managed to develop their model at a much lower cost - interestingly enough, I had also talked about how several of the constraints China has could actually lead to more innovation from that country in another blog I had written also about a month ago. Of course, during last week DeepSeek took the world by storm when they made their model open source and several folks jumped to read their paper (and others including my fellow IITians posted great explanations that made it easier for me to understand these innovations) - unlike traditional AI models that rely on brute-force computation, DeepSeek leverages a Mixture-of-Experts (MoE) architecture, Multi-Token Prediction (MTP), 8-bit precision training, and Multi-Headed Latent Attention (MLA) to optimize computational efficiency while maintaining high performance. This shift has profound implications for chipmakers, cloud providers, data centers, energy consumption, and enterprise AI applications (including CoPilots and Agentic solutions).

In this blog, I will explore the business impact of technical innovations that make DeepSeek unique across chips, data centers (impact on energy consumption is obvious hence, I skipped that), LLM Providers, and enterprise AI use cases, including CoPilots and agentic solutions.

DeepSeek’s Key Innovations Explained in Simple Terms

For business leaders to understand the significance of DeepSeek’s impact, it's essential to break down the key technical innovations in simple terms. These breakthroughs fundamentally change how AI models are trained, optimized, and deployed at scale.

1?? Mixture-of-Experts (MoE) – A Smarter Way to Use AI Model Parameters

Traditional AI models use all their parameters for every single request, leading to enormous computational costs. DeepSeek, however, uses a Mixture-of-Experts (MoE) approach, where only a subset of the model (the most relevant “experts”) is activated for each request. Think of it as calling in specialists rather than making everyone work on every problem.

Business Impact:

• Significant cost reduction in AI training and inference since only a fraction of the model runs at a time.
• Lower energy consumption, making AI more scalable and environmentally friendly.
• New chip architectures will be needed to support dynamic expert routing, benefiting GPU/AI accelerator makers that optimize for MoE workloads.

2?? Multi-Token Prediction (MTP) – Thinking Ahead Instead of Step by Step

Most AI models generate responses one token (word, number, or symbol) at a time, predicting the next step sequentially. DeepSeek introduces Multi-Token Prediction (MTP), allowing the AI to predict multiple future tokens simultaneously—like a chess player thinking several moves ahead instead of just one.

Business Impact:

• Dramatically speeds up AI model training and reduces latency in real-time applications.
• Cheaper cloud AI deployments since businesses can accomplish the same AI-powered tasks with fewer compute resources.
• Real-time AI interactions become much smoother, improving enterprise AI assistants and AI-driven analytics tools.

3?? 8-bit Precision Training – Doing More with Less Memory

Most AI models train using 16-bit or 32-bit precision, which requires high memory usage. DeepSeek instead uses 8-bit floating point precision (FP8), reducing memory requirements while maintaining model accuracy.

Business Impact:

• Lower cost per AI training run, reducing expenses for companies developing proprietary AI models.
• FP8-optimized chips will become critical, favoring NVIDIA’s upcoming Blackwell GPUs and similar hardware.
• Energy-efficient AI adoption in enterprises since FP8 precision reduces data center power consumption.

4?? Multi-Headed Latent Attention (MLA) – Smarter Memory Use

DeepSeek's MLA seems to make AI models "smarter" with memory by having specialized "focus points" (heads) analyze hidden patterns instead of raw data. Instead of memorizing every word or pixel, MLA compresses information into simplified themes (like "emotion" or "cause-and-effect") and assigns each head to track one theme. This reduces clutter—like organizing a messy room into labeled boxes—so the model uses memory efficiently, handles longer texts, and understands deeper context without getting overwhelmed

Business Impact:

• Faster AI inference speeds make AI assistants, chatbots, and CoPilots more responsive.
• Reduces costs for companies running AI at scale, improving ROI for cloud-based AI solutions.
• Allows companies to process larger datasets more efficiently, making AI-powered analytics and automation more accessible.

5?? Reinforcement Learning Without Supervision – A Self-Improving AI

Unlike traditional AI training, which relies on labeled human feedback, DeepSeek employs reinforcement learning without supervision (RFL), allowing AI to learn by evaluating its own outputs and improving itself over time.

Business Impact:

• Reduces reliance on expensive, manually labeled datasets, making AI training more scalable.
• Leads to models that are better at long-term reasoning, benefiting industries like finance, legal, and scientific research.
• Could challenge OpenAI’s RLHF (Reinforcement Learning with Human Feedback) approach, disrupting how AI models are fine-tuned for user interactions.

Key Takeaways: Why DeepSeek’s Innovations Matter for Business

Each of these innovations addresses a major pain point in AI adoption: cost, speed, scalability, and efficiency. These breakthroughs will shape the next phase of AI-driven business solutions by making AI:

• More affordable → lowering costs for training and running AI models.
• More efficient → reducing GPU and cloud compute requirements.
• More accessible → expanding AI adoption across industries, even in companies that previously couldn’t afford it.

Now that we’ve outlined DeepSeek’s technical breakthroughs, let’s explore their impact across different parts of the AI ecosystem, including chipmakers, cloud providers, data centers, energy consumption, and enterprise AI applications.

1?? Chipmakers (NVIDIA, AMD, Hyperscaler ASICs)

My hypothesis is that DeepSeek has now opened the floodgates on Alrorithmic Innovation by open sourcing its model and also publishing its clever algorithmic variations. This further exacerbates the mismatch between algorithmi churn (every few months?) and hardware lifecycles (2-3 years)

Impact:

• NVIDIA Must Shift Toward Sparse Compute & MoE Optimization - good news here is that NVIDIA GPUs are already built with software configurable components (its CUDA ecosystem allows developers to optimize new algorithms like sparse attention and its Hopper architecture already supports FP8)
• FP8 Acceleration Becomes the Standard
• Hyperscaler ASICs Will Reduce Cloud Dependence on NVIDIA (although there doesn’t seem to be a single vendor-neutral framework that matches CUDA in both maturity and performance for deep learning, although there are multiple ongoing cross-platform initiatives like OpenCL, SYCL, ROCm/HIP, oneAPI)

Note some of this is already happening:

? AMD (MI300 Series):

• AMD’s MI300X AI accelerator supports sparsity-aware computing, meaning it can skip unnecessary calculations, improving efficiency for AI models like MoE.
• AMD is actively optimizing memory bandwidth and interconnect speeds to handle MoE-style AI workloads better.

? Google (TPU v5 & v6):

• Google’s Tensor Processing Units (TPUs) are designed for sparse compute, especially for large-scale AI workloads inside Google Cloud.
• TPU v5 and TPU v6 will likely focus on MoE-style model acceleration, especially for Google DeepMind’s AI research.

? AWS (Trainium & Inferentia Chips):

• AWS’s Trainium chips are built to support MoE models natively, making AI training on AWS cheaper.
• AWS Inferentia chips also focus on efficient inference for MoE models, reducing cloud AI costs.

Business Outcomes:

? NVIDIA still dominates training but faces inference pricing pressure.

? Hyperscalers accelerate in-house AI chip development to reduce costs.

? AMD gains market share IF it optimizes FP8 + MoE support.

2?? AI Cloud & Data Center Providers (AWS, Azure, GCP, Equinix)

Impact:

• Lower Per-Model Compute Costs = More AI Workloads
• Data Center Density Increases Due to MoE Optimization
• Edge AI Becomes More Viable

Business Outcomes:

? Cloud AI providers will reduce inference pricing but make up revenue in volume.

? Edge AI expansion benefits modular data center providers (Equinix, Digital Realty).

3?? AI Model Builders (OpenAI, Anthropic, Meta, SLM Startups)

Impact:

• MoE Adoption is Now Mandatory
• Inference Pricing War Will Accelerate
• Domain-Specific SLMs Will Gain Market Share

Below I have compiled LLM API Pricing from the US providers and the ones from China.? Note that DeepSeek is by far the cheapest (~1/10th the price of GPT-4 Turbo).? This will no doubt creating inference pricing pressure for Open AI and Claude.

LLM API Pricing Comparison (Input/Output per 1M Tokens)

Key Observations

1. Cost Leaders: DeepSeek v3 now appears to be significantly more cost-effective, with pricing that is much lower than competitors for both input and output.Qwen-1.8B and Gemini Pro remain competitive for budget workflows.
2. Context Window: Claude 3 and Kimi Chat continue to lead with a 200K context window, offering deep context understanding.DeepSeek v3 and GPT-4 Turbo provide a substantial 128K context at competitive rates.
3. Regional Specialization: Qwen models are tailored for Chinese NLP tasks, but pricing is adjusted for international comparison.InternVL caters to vision-language tasks, potentially appealing to sectors like healthcare.
4. Enterprise vs. Open Access: InternVL and Grok-1 might require specific negotiations or have limited access.Kimi and Qwen might offer free tiers, though this wasn't confirmed in recent data.

Business Outcomes:

? Open-source AI (Meta, Mistral) will thrive using DeepSeek-like methods. There is a chance that Open AI will make its models open source to counter the adoption and ecosystem that DeepSeek is building.

? OpenAI/Anthropic will still lead due to proprietary datasets & brand trust but will face increasing inference pricing pressure.

? Specialized AI startups (e.g., healthcare, legal AI) will outperform general LLMs in cost-sensitive sectors.

1?? Can DeepSeek Succeed Beyond Math, Coding, and Reasoning?

While DeepSeek is optimized for structured, logic-driven tasks, it does not inherently exclude other applications. The question is whether its MoE-based efficiency and reinforcement learning approach can provide a meaningful advantage in areas where OpenAI and Anthropic have dominance, such as creative writing, marketing, and multimodal AI.

Challenges for DeepSeek in Non-Logical Use Cases:

• Creativity is not purely logical: Unlike math and coding, art, music, and storytelling require high-dimensional abstraction and latent space manipulation, which OpenAI's models excel at through RLHF (Reinforcement Learning with Human Feedback) training.
• Multimodal AI is lacking: OpenAI’s GPT-4 Turbo, DALL·E, and Sora (video AI) leverage vast multimodal datasets to create cross-domain generative outputs. DeepSeek lacks this training infrastructure.
• Marketing and Engagement AI requires fluency and adaptability: OpenAI’s models are fine-tuned for brand voice, engagement metrics, and emotional resonance, areas where DeepSeek’s structured logic-based approach may fall short.

2?? Are Generic, All-Purpose LLMs Still Necessary for AI Distillation?

Does Distillation Still Require a Large, Generic LLM?

? Arguments Supporting the Need for General-Purpose LLMs in Distillation:

1. A Generalist Model Acts as a "Universal Teacher"
2. Multi-Task Learning Requires a Base Model with General Knowledge

? Why Distillation May No Longer Require Large, Generic LLMs:

1. MoE and Expert Specialization Reduce the Need for a Monolithic Base Model
2. Small-Scale, Expert-Specific Training Can Replace Generalist Models

? Perhaps a Hybrid Approach:

• Hybrid Approach: The future might not be about choosing between large, generic LLMs or small, specialized models but rather integrating both. A hybrid approach where:
• Domain-Specific Pre-training: Instead of always starting from a generalist model, pre-training on domain-specific data using smaller models or MoE could become more prevalent, reducing the need for distillation from a large generic model.
• Continuous Learning and Updating: With AI systems that can continuously learn and adapt, the rigid idea of a one-time distillation from a large model to a smaller one becomes less relevant. Models can evolve through interaction with new data or by integrating new "expert" modules.

In conclusion, while large, generic LLMs have been pivotal in the past for distillation, the evolution towards more modular, specialized, and adaptable AI systems suggests that their necessity might be diminishing or at least transforming into a different role within AI development.

Now I will focus on the what does this mean for Enterprise Gen AI Business Use Cases including CoPilots and Agents.

Comparing DeepSeek vs. OpenAI for Custom Agentic Solutions in Key Enterprise Use Cases

Building custom agentic AI solutions requires models that can autonomously execute workflows, reason through complex problems, and interact with systems dynamically. Below, we evaluate which AI model (DeepSeek or OpenAI) is better suited for Customer Service, IT Help Desk, HR Support, Financial Valuation, Fraud Detection, Anomaly Detection, and Application RCA based on their strengths.

1?? Customer Service & Support

Best Choice: OpenAI ?

Why?

? Conversational AI is OpenAI’s strength – GPT-4 is trained on vast human interaction data, making it better at understanding, responding, and handling user intent fluidly.

? Creative and empathetic responses – OpenAI models are better at handling nuanced human interactions, sentiment analysis, and tone adjustments for customer satisfaction.

? Multi-modal Capabilities – OpenAI is evolving voice, vision, and text-based multimodal AI (e.g., Sora for video, Whisper for speech-to-text), making it ideal for voice-based customer service agents.

? Marketing & Upselling Capabilities – OpenAI’s LLMs are better at engaging customers, identifying upselling opportunities, and generating personalized recommendations.

Where DeepSeek Might Work?

? If customer service is highly structured with complex reasoning tasks (e.g., technical troubleshooting), DeepSeek could be useful for self-service knowledge retrieval.

? For industries where logical reasoning is more important than conversational fluency (e.g., tax support, legal Q&A, financial compliance queries), DeepSeek could be useful.

Final Verdict: OpenAI dominates customer service due to its conversational strengths, while DeepSeek could work for structured, logic-heavy queries.

2?? IT Help Desk Support

Best Choice: OpenAI for General IT Support, DeepSeek for Advanced Troubleshooting ???? (Hybrid Approach Works Best)

Why?

? OpenAI excels in conversational, common IT troubleshooting tasks – resetting passwords, resolving common software issues, guiding users through workflows.

? DeepSeek is better at deep technical issue resolution – diagnosing complex IT system errors, debugging scripts, and explaining structured troubleshooting workflows.

Where DeepSeek Outperforms OpenAI?

? Root Cause Analysis (RCA) of complex IT failures – DeepSeek’s logical reasoning and structured decision-making make it ideal for handling multi-step troubleshooting and technical debugging.

? Code-based troubleshooting – If IT help desk requires AI that can analyze logs, debug code, or generate optimized scripts, DeepSeek would be more useful.

Final Verdict:

• Basic IT Help Desk (Reset Passwords, Software Issues) → OpenAI ?
• Complex RCA, System-Level Debugging → DeepSeek ?
• Best Strategy? Use OpenAI for front-line support, escalate to DeepSeek for technical issue resolution.

3?? HR Support (Employee Queries, Payroll, Benefits, Policy Guidance)

Best Choice: OpenAI ?

Why?

? HR queries require natural conversation and emotional intelligence – OpenAI’s alignment training makes it better at handling sensitive topics (e.g., payroll issues, workplace policies, grievances).

? Policy lookup and personalized guidance – OpenAI can synthesize HR policies into easy-to-understand responses.

? Better Sentiment Understanding – OpenAI is trained on large-scale human interactions, making it better at addressing workplace concerns with empathy.

Where DeepSeek Might Work?

? If HR support requires highly structured, rules-based compliance (e.g., tax filings, labor law compliance), DeepSeek’s structured reasoning could help automate compliance checks.

Final Verdict: OpenAI wins HR support due to superior conversational fluency and sentiment analysis, but DeepSeek could be useful for compliance-heavy HR functions.

4?? Equity & Bond Valuation

Best Choice: DeepSeek ?

Why?

? Valuation is a structured mathematical problem – DeepSeek’s expert routing architecture allows it to perform multi-step financial calculations more efficiently than OpenAI.

? Superior logical reasoning for risk assessment – Financial modeling requires Monte Carlo simulations, portfolio optimization, and discounted cash flow (DCF) analysis, all of which benefit from DeepSeek’s structured problem-solving ability.

? MoE’s efficiency allows faster, lower-cost AI-powered financial analysis – DeepSeek’s inference efficiency reduces the cost of running complex financial models in real-time.

Where OpenAI Might Work?

? If equity analysis requires market sentiment analysis, qualitative risk assessment, or NLP-based news interpretation, OpenAI could help.

Final Verdict: DeepSeek dominates structured valuation and financial modeling tasks, while OpenAI is useful for qualitative analysis (e.g., earnings sentiment analysis).

5?? Financial Fraud Capture

Best Choice: DeepSeek ?

Why?

? Fraud detection relies on anomaly detection and structured reasoning – DeepSeek is built to analyze patterns, flag outliers, and perform forensic accounting-style investigations. ? MoE is well-suited for multi-dimensional data analysis – Analyzing transactions across multiple layers (geographic, temporal, behavioral) to detect suspicious activity.

? Lower cost per inference means real-time fraud detection is scalable – Financial institutions can deploy DeepSeek AI models at a lower operational cost than OpenAI.

Where OpenAI Might Work?

? OpenAI could help detect fraudulent text-based scams, phishing attempts, or deepfake-generated fraud.

Final Verdict: DeepSeek dominates structured fraud analysis in finance, while OpenAI could be useful for social engineering fraud detection.

6?? Anomaly Detection (Enterprise Security, IT Monitoring, Threat Analysis)

Best Choice: DeepSeek ?

Why?

? DeepSeek excels at recognizing deviations from normal patterns, making it perfect for cybersecurity anomaly detection, IT performance monitoring, and network threat detection. ? AI-powered SIEM (Security Information & Event Management) tools benefit from DeepSeek’s structured reasoning to predict, categorize, and respond to security threats.

Where OpenAI Might Work?

? OpenAI can assist in interpreting cybersecurity reports, making them more understandable for business leaders.

Final Verdict: DeepSeek is better at real-time anomaly detection, cybersecurity risk scoring, and automated response systems.

7?? Application Performance Root Cause Analysis (RCA)

Best Choice: DeepSeek ?

Why?

? DeepSeek is designed for structured debugging, root cause analysis, and performance optimization – It can diagnose application performance bottlenecks by analyzing logs, identifying inefficiencies, and suggesting fixes.

? AI-assisted IT Ops (AIOps) and self-healing applications – DeepSeek can automate performance tuning and anomaly detection across distributed cloud environments.

? Faster inference cost makes it viable for always-on RCA monitoring.

Where OpenAI Might Work?

? OpenAI is useful for translating complex technical reports into easy-to-understand explanations for non-technical stakeholders.

Final Verdict: DeepSeek dominates RCA for large-scale enterprise applications due to its ability to process structured logs and performance metrics with precision.

Additional High-Impact Enterprise Use Cases & Which AI Model (DeepSeek or OpenAI) is Better

Beyond Customer Support, IT Help Desk, HR, Finance, Fraud Detection, Anomaly Detection, and RCA, there are other key areas where AI is transforming industries. Below are additional high-value enterprise AI use cases, along with an analysis of whether DeepSeek or OpenAI is better suited and why.

1?? Enterprise Knowledge Management (Internal Search & Documentation Retrieval)

Best Choice: DeepSeek ?

Why?

? DeepSeek’s Mixture-of-Experts (MoE) model excels at structured document retrieval and reasoning over large text corpora.

? Better at multi-step reasoning across technical documentation, making it ideal for enterprises with complex internal knowledge bases.

? Lower-cost inference means always-on enterprise search is more affordable compared to using large, monolithic LLMs.

Where OpenAI Might Work?

? OpenAI’s natural conversation ability makes it better for human-like responses in cases where employees need summarized, conversational explanations.

Final Verdict:

• For structured enterprise knowledge retrieval (e.g., legal, engineering docs) → DeepSeek ?
• For human-like summarization of knowledge base articles → OpenAI ?

2?? Medical Diagnostics & Radiology Image Analysis

Best Choice: DeepSeek ?

Why?

? DeepSeek’s multi-token prediction (MTP) and structured reasoning are well-suited for analyzing patient medical history and lab results.

? MoE allows specialization for different diseases (e.g., oncology, cardiology) within the same AI system.

? Lower inference costs allow real-time AI diagnostics in hospitals at scale.

Where OpenAI Might Work?

? OpenAI can help with patient-friendly explanations of complex medical conditions, making AI-powered doctor-patient communication more effective.

Final Verdict:

• For AI-powered diagnosis and structured medical decision-making → DeepSeek ?
• For doctor-patient communication, simplifying medical reports → OpenAI ?

3?? AI-Powered Legal Research & Contract Analysis

Best Choice: DeepSeek ?

Why?

? DeepSeek excels at scanning large volumes of case law, contracts, and regulations for compliance violations.

? Perfect for legal firms that need AI to generate structured legal arguments, identify risk clauses, and summarize regulations.

? Structured, deterministic decision-making aligns with legal reasoning.

Where OpenAI Might Work?

? OpenAI is better for conversational legal Q&A, client communication, and summarizing complex legal language into layman’s terms.

Final Verdict:

• For automated contract review and risk assessment → DeepSeek ?
• For legal chatbot support and client-facing legal AI → OpenAI ?

4?? Supply Chain Optimization & Demand Forecasting

Best Choice: DeepSeek ?

Why?

? DeepSeek’s structured prediction models are ideal for inventory optimization, demand forecasting, and logistics route optimization.

? MoE’s efficiency allows dynamic real-time supply chain adjustments at lower inference costs.

? Superior for multi-objective optimization (e.g., balancing cost, speed, and carbon footprint in logistics).

Where OpenAI Might Work?

? OpenAI’s conversational AI can assist in explaining supply chain disruptions, providing real-time summaries of shipment issues, and supporting executive decision-making.

Final Verdict:

• For AI-driven logistics optimization and real-time demand forecasting → DeepSeek ?
• For executive reporting and supply chain Q&A → OpenAI ?

5?? Autonomous Business Intelligence (BI) & Real-Time Data Analytics

Best Choice: DeepSeek ?

Why?

? DeepSeek can analyze and generate insights from large enterprise datasets, detecting patterns and anomalies in real-time.

? Works well for predictive analytics, sales forecasting, and business trend detection.

? Can power AI-driven dashboards that offer real-time data insights without human intervention.

Where OpenAI Might Work?

? OpenAI could be used to convert raw data into narrative reports that non-technical stakeholders can understand.

Final Verdict:

• For AI-driven real-time data analysis and forecasting → DeepSeek ?
• For BI report generation and natural language dashboards → OpenAI ?

6?? AI-Powered Code Refactoring & Legacy System Modernization

Best Choice: DeepSeek ?

Why?

? DeepSeek’s superior logic-based reasoning makes it better at analyzing, restructuring, and optimizing legacy codebases.

? Perfect for large enterprises looking to migrate from COBOL, Java, or other legacy systems to modern cloud-native architectures.

? Ideal for automating cloud infrastructure transitions (e.g., moving from on-premise to AWS/GCP/Azure).

Where OpenAI Might Work?

? OpenAI’s Codex is good for generating simple code snippets or assisting junior developers in coding tasks.

Final Verdict:

• For large-scale legacy system transformation → DeepSeek ?
• For day-to-day developer coding assistance → OpenAI ?

7?? AI-Powered Personal Finance & Wealth Management

Best Choice: DeepSeek ?

Why?

? DeepSeek’s structured financial modeling can power AI-driven personal finance assistants that optimize budgets, investments, and tax strategies. ? Superior at Monte Carlo simulations, risk modeling, and retirement planning calculations.

Where OpenAI Might Work?

? OpenAI is better for engaging, human-like financial advisory conversations, answering user questions in a friendly, conversational tone.

Final Verdict:

• For AI-powered investment modeling and wealth optimization → DeepSeek ?
• For conversational AI in personal finance apps → OpenAI ?

8?? AI-Driven Cybersecurity Threat Intelligence

Best Choice: DeepSeek ?

Why?

? DeepSeek’s anomaly detection excels in detecting cyber threats, malware patterns, and network vulnerabilities. ? Can be embedded into SIEM (Security Information & Event Management) platforms for real-time security analysis. ? Lower-cost inference allows organizations to monitor cybersecurity at scale.

Where OpenAI Might Work?

? OpenAI can be useful for translating cybersecurity threats into understandable business risk reports for executives.

Final Verdict:

• For real-time cyber threat detection and AI-powered SIEM → DeepSeek ?
• For executive security briefings and cybersecurity Q&A → OpenAI ?

9?? AI-Powered Scientific Research & Drug Discovery

Best Choice: DeepSeek ?

Why?

? DeepSeek’s structured reasoning is ideal for bioinformatics, protein folding analysis, and AI-driven drug design.

? MoE-based models allow specialization in different biochemical pathways, accelerating research.

? Cost-efficient inference makes large-scale AI simulations viable for pharmaceutical companies.

Where OpenAI Might Work?

? OpenAI can help with natural language research summarization, making complex scientific papers easier to understand.

Final Verdict:

• For AI-driven scientific simulations and research automation → DeepSeek ?
• For scientific literature review and research assistance → OpenAI ?

Summary

DeepSeek with its innovations and making its model open source is now truly "democratizing" Generative AI - overall, great news for Consumers like me (who like ChatBOTs that think out aloud allowing me a window into their reasoning) and Developers (like my son who can download & study the code and play with it in Amazon Bedrock...where it was just made available....great job AWS!!!). Clearly, chip makers need to incorporate these algorithmic innovations in their design and possibly anticipate even more algorithmic twists along the way. I do think that due to "made in China" effect, DeepSeek's adoption in the Enterprise will face challenges though there are several use cases where DeepSeek's focus on math, logic, and coding makes it a better choice especially given its dirt cheap API call price. Overall, this is a great development that was much needed to give US based LLM providers, Hyperscalers, and Chip Makers a bit of a jolt!

Disclaimer: Views expressed here are solely of the author and do not represent those of his current or past employers.