By NewsBlogger Investing in AI technologies, particularly Large Language Models (LLMs) and machine learning (ML), has become a trend among venture capitalists eager to capitalize on potential breakthroughs, but experts warn that billions of dollars could be wasted on AI investments in the next decade as the true impact of these technologies remains uncertain.
At a glance
- Investing in AI technologies, particularly Large Language Models (LLMs) and machine learning (ML), has become a trend among venture capitalists (VCs).
- Experts predict that billions of dollars could be wasted on AI investments in the next decade as the true impact of these technologies remains uncertain.
- LLMs are seen as a revolutionary technology with rapid user adoption, but companies must consider the long-term costs of maintaining AI projects.
- Following certain guidelines can help companies ensure that their AI investments are successful and not squandered.
- Foundational, a startup specializing in modern data infrastructure, secured $8 million in seed funding and aims to streamline data preparation tasks through its innovative code analysis techniques.
The details
Investing in AI technologies, particularly Large Language Models (LLMs) and machine learning (ML), has become a trend among venture capitalists (VCs) who are eager to capitalize on potential breakthroughs.
Despite the excitement surrounding AI, experts predict that billions of dollars could be wasted on AI investments in the next decade as the true impact of these technologies remains uncertain.
Many companies are jumping on the AI bandwagon, with some experiencing success while others face failures.
LLMs, in particular, are seen as a revolutionary technology with rapid user adoption, but companies must carefully consider the long-term costs of maintaining AI projects.
The increasing marginal costs associated with AI initiatives may not always align with the expected value, and there is a risk of commoditization in the tech industry, especially with AI projects.
Focusing on AI initiatives that enhance existing products is viewed as a safer bet, even if it means disrupting current business models to stay competitive.
Following certain guidelines can help companies ensure that their AI investments are successful and not squandered.
In a recent development, Foundational, a startup specializing in modern data infrastructure, secured $8 million in seed funding led by Viola Ventures and Gradient Ventures.
The company’s platform automatically analyzes and maps data teams’ code to identify issues, propose solutions, and prepare data for AI applications.
After operating in stealth mode for the past year and a half, Foundational is now unveiling its technology to the public.
Notable companies like Ramp and Lemonade are already leveraging Foundational’s services.
CEO and cofounder Alon Nafta, with a background in cybersecurity and data infrastructure, aims to tackle the challenges organizations face as they scale up their data capabilities.
The platform seamlessly integrates with tools like GitHub to offer actionable suggestions and fixes directly within developers’ workflows.
Industry analysts stress the importance of tools that ensure data quality and consistency as companies embrace AI, as data scientists can spend a significant amount of time on tasks like cleaning and structuring datasets.
Foundational’s approach aims to streamline data preparation tasks through its innovative code analysis techniques.
With the fresh injection of funding, Foundational plans to expand its engineering team and enhance its go-to-market strategies.
Nafta believes that the ability to automatically interpret data pipelines and uphold quality standards will be crucial as data volumes increase and AI becomes more mainstream.
Foundational aspires to serve as the foundational layer for a new era of data-driven innovation.
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| venturebeat.com |
|---|
| – VCs are eager to invest in AI technologies to avoid missing out on potential big hits – Large language models (LLMs) and machine learning (ML) are considered AI – Billions of dollars are expected to be wasted on AI investments over the next decade – Excitement around new technologies like AI is common before their true impact is known – Many companies are investing in AI projects, leading to some successes and many failures – LLMs are considered a game-changing technology with rapid user adoption – Companies should consider the cost of sustaining AI projects over time – AI projects can have increasing marginal costs that may not align with value – Commoditization is a risk in the tech industry, especially with AI initiatives – Focusing on AI initiatives that improve existing products is a simpler bet – Cannibalizing current business with new technology may be necessary to stay competitive – Following certain tips can help ensure AI investments are successful and not wasted |
| venturebeat.com |
|---|
| – Foundational, a startup focused on modern data infrastructure, raised $8 million in seed funding led by Viola Ventures and Gradient Ventures – The company’s platform automatically maps and analyzes data teams’ code to identify issues, suggest fixes, and prepare data for AI applications – Foundational has been in stealth mode for the past year and a half and is now making its technology available to the public – Public companies like Ramp and Lemonade are already customers of Foundational – CEO and cofounder Alon Nafta previously worked in cybersecurity and data infrastructure roles – Foundational aims to address the challenges organizations face as they scale up their data capabilities – The company’s platform analyzes data teams’ source code to map data lineage and identify potential issues before deployment – Foundational integrates with tools like GitHub to provide actionable suggestions and fixes within developers’ workflows – |
| The platform combines static code analysis, dynamic runtime analysis, and AI-powered techniques to build a comprehensive map of an organization’s data pipelines – Industry analysts say tools for maintaining data quality and consistency are critical as companies adopt AI – Data scientists can spend up to 80% of their time on data preparation tasks like cleaning and structuring datasets – Foundational aims to streamline data preparation tasks through its code analysis approach – The company plans to expand its engineering team and go-to-market efforts with the new funding – The seed round was led by Viola Ventures and Gradient Ventures, with participation from other investors – Nafta believes that the ability to automatically make sense of data pipelines and enforce quality will become essential as data volumes grow and AI becomes mainstream – Foundational aims to become the foundational layer for a new era of data-driven innovation. |
