### Summary
British Prime Minister Rishi Sunak is allocating $130 million to purchase computer chips to power artificial intelligence and build an "AI Research Resource" in the United Kingdom.
### Facts
- 🧪 The United Kingdom plans to establish an "AI Research Resource" by mid-2024 to become an AI tech hub.
- 💻 The government is sourcing chips from NVIDIA, Intel, and AMD and has ordered 5,000 NVIDIA graphic processing units (GPUs).
- 💰 The allocated $130 million may not be sufficient to match the ambition of the AI hub, leading to a potential request for more funding.
- 🌍 A recent report highlighted that many companies face challenges deploying AI due to limited resources and technical obstacles.
- 👥 In a survey conducted by S&P Global, firms reported insufficient computing power as a major obstacle to supporting AI projects.
- 🤖 The ability to support AI workloads will play a crucial role in determining who leads in the AI space.
AI chip maker d-Matrix has unveiled the next-generation of its Jayhawk platform, Jayhawk II, which features enhanced digital in-memory computing to improve performance and efficiency for AI deployment. The chiplet architecture allows for modular and scalable compute platforms for different needs, enabling the efficient processing of AI models during inference.
Main topic: The demand for computer chips to train AI models and its impact on startups.
Key points:
1. The surge in demand for AI training has created a need for access to GPUs, leading to a shortage and high costs.
2. Startups prefer using cloud providers for access to GPUs due to the high costs of building their own infrastructure.
3. The reliance on Nvidia as the main provider of AI training hardware has contributed to the scarcity and expense of GPUs, causing startups to explore alternative options.
Hugging Face, an AI startup, has raised $235 million in a Series D funding round, with participation from tech giants such as Google, Amazon, Nvidia, and IBM, and now has a valuation of $4.5 billion, signaling the growing demand for AI platforms and tools.
Nvidia's CEO, Jensen Huang, predicts that upgrading data centers for AI, which includes the cost of expensive GPUs, will amount to $1 trillion over the next 4 years, with cloud providers like Amazon, Google, Microsoft, and Meta expected to shoulder a significant portion of this bill.
ControlRooms.ai, an AI-powered analytics startup, has raised $10 million in a Series A round to automate the industrial troubleshooting process and minimize downtime for heavy industries like chemical and energy plants. The platform predicts manufacturing plant behavior and detects potential problems before they are noticed by engineers or operators.
Nvidia predicts a $600 billion AI market opportunity driven by accelerated computing, with $300 billion in chips and systems, $150 billion in generative AI software, and $150 billion in omniverse enterprise software.
Nvidia's head of enterprise computing, Manuvir Das, believes that the artificial intelligence (AI) market presents a $600 billion opportunity for the company, as demand for AI technology continues to fuel its growth, leading analysts to overlook its undervalued shares and potential for exceptional growth in the years to come.
AI startup Darrow has raised $35 million in funding for its AI-powered data engine that searches for class action litigation potential, with active cases resulting from its insights currently totaling around $10 billion in claims, and plans to use the funding to expand its team, add new legal domains to its tools, and invest in technology assets.
AMD plans to acquire AI startup Nod.ai to strengthen its software capabilities and compete with rival chipmaker Nvidia in the AI chip market.
Tech giants like Microsoft and Google are facing challenges in profiting from AI, as customers are not currently paying enough for the expensive hardware, software development, and maintenance costs associated with AI services. To address this, companies are considering raising prices, implementing multiple pricing tiers, and restricting AI access levels. Additionally, they are exploring the use of cheaper and less powerful AI tools and developing more efficient processors for AI workloads. However, investors are becoming more cautious about AI investments due to concerns over development and running costs, risks, and regulations.
