Amazon to Launch New Reasoning Model by June, To Rival OpenAI o1, Claude 3.7 Sonnet - Related to openai, june,, why, sonnet, databases

Amazon to Launch New Reasoning Model by June, To Rival OpenAI o1, Claude 3.7 Sonnet

Amazon is set to release a new reasoning model under its Nova branding by June this year, Business Insider reported. The model will function with a hybrid approach, meaning it can provide quick responses, or use ‘extended thinking’ for more complex queries.

It is also reported that Amazon aims to make the model more cost efficient than OpenAI’s o1, Gemini [website] Flash Thinking, and even Claude [website] Sonnet from Anthropic – the AI startup it has actively invested in.

Furthermore, Amazon aims to rank its upcoming reasoning model among the top five spots in various benchmarks.

This means that Amazon will be the latest organization to join the bandwagon of reasoning models, which was once started by OpenAI with the o1. OpenAI also released the o3 family of reasoning models last year, which claimed the top spot on several benchmarks. A few weeks ago, Chinese AI maker DeepSeek caused quite a storm in both the AI ecosystem, and the US stock market with its high performance, and cost-efficient R1 reasoning model.

not long ago, Elon Musk’s xAI, and Anthropic also released models with reasoning, or ‘thinking’ capabilities.

Last year, Amazon launched its family of Nova AI models on Bedrock, namely Nova Micro, Lite, Pro, and Premier. Each model is optimised for specific tasks, ranging from text summarisation and translation to complex document processing and multimodal interactions.

“They are really cost-effective and about 75% less expensive than the other leading models in Bedrock,” Amazon chief Andy Jassy noted.

lately, Amazon showcased Alexa+, a next-generation personal assistant powered by Anthropic Claude, which will be available for free to Prime members. “Alexa+ is more conversational, smarter, personalised, and helps you get things done,” Panos Panay, senior vice president of devices and services at Amazon, noted.

Amazon has infused Alexa+ with LLMs to improve knowledge retrieval. clients can upload documents, emails, or images for Alexa+ to analyse and summarise.

“For example, people can send a photo of a live music schedule, and Alexa+ will add the details to their calendar,” the corporation expressed.

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Why Businesses Shouldn’t Treat LLMs as Databases

Despite the rise of AI, SaaS companies continue to play a crucial role, as large language models (LLMs) cannot function as databases. Sridhar Vembu, founder of Indian SaaS enterprise Zoho, not long ago explained that neural networks “absorb” data in a way that makes it impossible to enhancement, delete, or retrieve specific information accurately.

, this is not just a technological challenge but a fundamental mathematical and scientific limitation of the current AI approach.

He explained that if a business trains an LLM using its customer data, the model cannot improvement itself when a customer modifies or deletes their data. This is because there is no clear mapping between the original data and the trained parameters. Even if the model is dedicated to a single customer, there is no way to guarantee that their data changes will be reflected accurately.

Vembu compared the process of training LLMs to dissolving trillions of cubes of salt and sugar in a vast lake. “After the dissolution, we cannot know which of the cubes of sugar went where in the lake—every cube of sugar is everywhere!”.

Notably, Klarna CEO Sebastian Siemiatkowski in recent times shared on X that he experimented with replacing SaaS solutions like Salesforce by building in-house products with ChatGPT.

His experience with LLMs was quite similar to Vembu’s. Siemiatkowski mentioned that feeding an LLM the fragmented, dispersed, and unstructured world of corporate data would result in a very confused model.

He noted that to address these challenges, Klarna explored graph databases (Neo4j) and concepts like ontology, vectors, and retrieval-augmented generation (RAG) to improved model and structure knowledge.

Siemiatkowski explained that Klarna’s knowledge base, spanning documents, analytics, customer data, HR records, and supplier management, was fragmented across multiple SaaS tools such as Salesforce, customer relationship management (CRM), enterprise resource planning (ERP), and Kanban boards.

He noted that each of these SaaS solutions operated with its own logic, making it difficult to create a unified, navigable knowledge system. By consolidating its databases, Klarna significantly reduced its reliance on external SaaS providers, eliminating around 1,200 applications.

Microsoft chief Satya Nadella, in a recent podcast, indirectly took a dig at Salesforce by saying that traditional SaaS companies will collapse in the AI agent era.

He noted that most business applications—such as Salesforce, SAP, and traditional ERP/CRM systems—function as structured databases with interfaces for individuals to input, retrieve, and modify data. He likened them to CRUD databases with embedded business logic.

Nadella explained that AI agents will not be tied to a single database or system but will operate across multiple repositories, dynamically pulling and updating information.

“Business logic is all going to these agents, and these agents are going to be multi-repo CRUD. They’re not going to discriminate between what the back end is; they’re going to revision multiple databases, and all the logic will be in the AI tier,” he stated.

Vembu argued that RAGs have their own limitations and cannot fully address the core problem of AI models being inherently static once trained. “In that sense, neural networks (and therefore LLMs) are not a suitable database.”.

“The RAG architecture keeps the business database separate and augments the user prompt with data fetched from the database,” he added.

In high-stakes applications, such as financial transactions, medical records, or regulatory compliance, this lack of adaptability could be a significant roadblock.

“Vembu’s observations about LLMs’ static nature resonate strongly. The ‘frozen knowledge’ problem he describes isn’t just theoretical — it’s a practical challenge we grapple with daily in production environments,” Tagore Reddi, director of digital and data analytics at Hinduja Global Solutions, expressed.

“While RAG architectures offer a workable interim solution, especially for sensitive enterprise data, they introduce their own complexity around data freshness, latency, and system architecture,” he added.

However, many advancements are taking place today in RAG, especially in combination with vector search. Today, many database companies like Pinecone, Redis, and MongoDB offer vector search for RAG.

Pinecone in recent times launched Assistant, an API service that simplifies building RAG-powered applications by handling chunking, embedding, vector search, and more. It allows developers to deploy production-grade AI applications in under 30 minutes.

Similarly, Oracle lately launched HeatWave GenAI, which integrates LLMs and vector processing within the database, allowing individuals to leverage generative AI without requiring AI expertise or data movement.

Meanwhile, Microsoft Azure offers Azure Cosmos DB, a fully managed NoSQL, relational, and vector database that integrates AI capabilities for tasks like RAG. Azure also provides Azure Cognitive Search, which uses AI for advanced search and data analysis.

Data warehousing platform Snowflake not long ago launched Cortex Agents, a fully managed service for integrating, retrieving, and processing structured and unstructured data at scale.

For now, LLMs cannot replace databases because they lack real-time updates and precise data control. Businesses still need reliable database solutions alongside AI.

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Tesla is Hiring a Front-End Software Engineer in India

American EV giant Tesla is hiring a software engineer in Pune, who is focused on front-end development.

The role involves working with a software team focusing on certain internal tools in Tesla that facilitate process management of the design, permitting, and installation of energy products and systems.

“This team is focused on building a stable and scalable platform that can be extended and configured for all Tesla Energy and Vehicle products that are sold and supported today, and any that will be introduced in the future,” read the job description.

The role requires a “solid understanding” of web technologies such as HTTP, REST, AJAX and JSON. Strong proficiency in HTML, CSS and JavaScript/ES6, including DOM manipulation and the JS object model.

Furthermore, the organization is also hiring a PCB design engineer in the country. The role will involve working with the “high speed layout team” at Tesla, which is responsible for the layout of the physical hardware that delivers the autonomous driving capabilities, high performance computing and infotainment experience to their vehicles and AI supercomputers.

in the recent past, Tesla officially began hiring professionals in India. However, the roles posted earlier were focused on operations, customer support, sales, and vehicle service.

The hiring announcements followed after a recent interaction between CEO Elon Musk and Prime Minister Narendra Modi in the US.

“Prime Minister and Mr Musk discussed strengthening collaboration between Indian and US entities in innovation, space exploration, artificial intelligence, and sustainable development. Their discussion also touched on opportunities to deepen cooperation in emerging technologies, entrepreneurship and good governance,” read a statement from India’s external affairs ministry.

This hiring initiative follows previous efforts by Tesla to negotiate lower import taxes as a prerequisite for significant investment in the country.

India in recent times reduced the basic customs duty on high-end vehicles priced above $40,000 from 110% to 70%, which may have influenced Tesla’s decision to explore the market further.

Moreover, it was also reported that Tesla has finalised a location to open its first showroom in India, at the Bandra Kurla complex in Mumbai. The showroom is noted to occupy 4,000 square feet on the ground floor of a commercial complex. Tesla also plans a second showroom in India at Aerocity in Delhi.

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