I'm realizing that builders in crypto don't pay a lot of attention to the broader AI / macro tech innovations, so I'm planning to share a weekly update along with my personal takes. Let me know if this is helpful!

1️⃣ Salesforce Pauses Hiring Engineers in 2025

- Salesforce announced it will not hire new engineers in 2025, citing a 30% productivity boost from AI tools like Agentforce.

- Instead, the company plans to hire 1,000–2,000 sales roles to focus on helping clients adopt AI-powered solutions.

My take: 

- I personally think a lot of the junior- and mid-level software engineering tasks will be replaced by AI agents in the next few years.

2️⃣ Nvidia’s Vision for AI Agents 

- Nvidia CEO Jensen Huang introduced “Agentic AI” at CES 2025 last week, emphasizing its potential to create multi-trillion-dollar industries.

- The company launched a new foundational model "Cosmos", delivering 2x the performance of its predecessor, powered by the advanced Hopper architecture.

- “Agentic AI” focuses on autonomous decision-making, aiming to revolutionize workflows in industries like software, marketing, and customer service.

My take:

- While the vision remains compelling, remains to be seen if AI can replace raw engineering horse-power, not withstanding hallucinations and other problems.

3️⃣ DeepSeek-V3 Challenges GPT-4 

- Chinese AI startup DeepSeek unveiled DeepSeek-V3, a 671-billion-parameter model that outperformed GPT-4 in reasoning tasks by 15%. 

- The big news is that the training costs are less than $6m, which according to their benchmarks is the state of the art for open source.

My take:

- Pretty cool is see open source models like DeepSeek and LLaMa give closed sourced models a run for their money. More options for builders is always great!

4️⃣ Microsoft Launches Phi-4 Model, a small language model

- Microsoft launched Phi-4, a 14-billion-parameter model that is 25% more efficient than its predecessor.  

- This is an open-source small language model that's designed to be run on edge devices with limited computational resources. This model beats other larger models in challenging mathematical benchmarks.

- The most interesting thing about this model is that it was trained on synthetic data, rather than organic data. Their technical report has a lot of details on how they generated the synthetic data. It's open-source and available on Azure AI Foundry and Hugging Face today.

My take:

- Just like my previous post on test-time compute innovations, cool to see newer, powerful models using synthetic data for training which is novel and challenges prior assumptions.