Welcome back, AI’ers!

Here’s What’s On The Menu For Today

• The 95% failure rate of enterprise AI

• Meta's AI pivot from building to buying

• DeepSeek's massive open-source breakthrough

• The $25B bet on AI's power infrastructure

AI ON MY MIND

A MESSAGE FROM OUR PARTNER

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TODAY’S TOP STORIES

AI's Reality Check: 95% of Enterprise Projects Failing

The Recap:

Enterprises have poured over $30 billion into generative AI, but a recent MIT report reveals a stark reality: 95% of these projects are failing to produce any return on investment. This disconnect between hype and tangible value is sending ripples through the tech industry and Wall Street.

Unpacked:

• The primary reason for failure is that most current AI systems don't learn from feedback or adapt to specific business workflows, making them brittle and disconnected from daily operations.

• Despite the low success rate, companies have invested over $30 billion into generative AI, with only a tiny 5% of integrated pilots managing to extract millions in value.

• The report's findings echo Sam Altman’s recent warning that investors are getting “overexcited” about AI, drawing parallels to the dotcom bubble.

Bottom line:

The challenge isn't the potential of AI, but its practical implementation beyond simple productivity boosts. The next wave of value will be unlocked by systems that can deeply integrate and learn within specific company contexts.

Meta's AI Strategy Shift

The Recap: Meta plans to downsize its Superintelligence Labs while exploring partnerships to license third-party AI models, marking a strategic pivot from building to buying AI capabilities.

Unpacked:

• The tech giant's shift suggests that even companies with massive resources are finding it challenging to independently develop frontier AI systems

• This pivot echoes Meta's previous all-in approach to the metaverse, but now with AI as the primary focus for future development

• The company appears to be embracing a more practical strategy of leveraging external partnerships rather than relying solely on in-house development

Bottom line: Meta's strategic reorganization reflects the evolving landscape of AI development, where building everything in-house may not be the most efficient path forward. This move could encourage other companies to consider similar hybrid approaches, combining internal capabilities with external partnerships.

MY FAVORITE AI TOOLS

Relevance is my favorite AI tool right now (even more so than N8N).

Here’s why:

It’s an AI agent building tool that doesn’t require you to build out the logic using nodes. Instead, you use natural language to build out these agents. But the best thing imo is that you can build AI Agent teams.

Like agents talking to each other completing a goal until its done. It’s nuts. No other tool on the market is doing this. You have to check it out.

(Try it out →Link)

👾 AI Tools We Use Everyday

➡️ N8N for AI Agents - Literally use this everyday to write this newsletter (link)

➡️ Axiom AI - AI Browser automation to scrape anything on the web (link)

➡️ GummySearch - AI Reddit research (link)

Tools are sourced from our database and are not sponsored or affiliated.

MORE STORIES

 DeepSeek Releases a Massive Open Source AI

The Recap: Chinese AI firm DeepSeek just unveiled DeepSeek V3.1, a colossal 685B-parameter open-source model designed to challenge the industry's top proprietary systems.

Unpacked:

• With 685 billion parameters, this model enters the top tier of AI, rivaling the scale of some of the most powerful closed-source systems available.

• It features a generous 128K context window, allowing it to process and analyze extensive documents or complex codebases in a single prompt.

• By open-sourcing a model of this magnitude, DeepSeek provides researchers and developers with access to top-tier AI capabilities without the high costs of proprietary APIs.

Bottom line: This release significantly boosts the open-source AI ecosystem, providing a powerful alternative to expensive, closed-off models. It also signals that high-stakes AI development is increasingly a global competition.

The $25B Bet on AI's Power-Hungry Future

The Recap: Vantage Data Centers is building 'Frontier,' a massive $25 billion campus in Texas with 1.4 gigawatts of power dedicated entirely to AI compute.

Unpacked:

• This move mirrors comments from OpenAI's Sam Altman, who predicted his company alone would spend trillions of dollars on data center construction in the near future.

• The 1.4-gigawatt facility is a direct response to the unprecedented AI demand for the immense computational power needed to train and run large-scale models.

• The $25 billion investment signals a long-term, high-stakes bet on AI's future value, even as the market shows jitters about a potential bubble.

Bottom line: The race for AI dominance is increasingly becoming a battle over physical infrastructure and raw power. Access to this level of dedicated compute will be a key differentiator for companies aiming to lead the field.

OTHER NEWS YOU MAY LIKE

Adobe launched Acrobat Studio, a new premium subscription allowing users to analyze various file types and manage documents in an AI-powered workspace using plain English.

Databricks eyes a $100 billion valuation in a new fundraising round, signaling massive investor confidence in the AI data platform space.

Google updated its Translate app with two new Gemini modes and Duolingo-style practice games, integrating advanced AI and gamification into its language tool.

Slingshot AI launched “Ash,” an a16z-backed chatbot marketed as the first public AI-powered therapy service, pushing further into the AI for mental health space.

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