Every Inc.

While everyone's obsessing over AGI, smart founders are quietly building fortunes in a different direction. According to Phin Barnes: 1. AGI will be too expensive for most practical applications 2. Specialized AI models will capture most of the economic value 3. "Good enough" targeted solutions beat perfect general ones The real opportunity isn't in building the next AGI breakthrough. It's in creating focused, efficient solutions that solve specific problems well. Start expensive, iterate towards efficiency, and stay model-independent. That's where the billions will be made. Read the full piece: https://bit.ly/4g0CX2u

I just read Katie Parrott's fascinating article, "The Once and Future History of Knowledge Work", in Every Inc. and it’s packed with intriguing insights on how AI is reshaping our professional lives. Here are three thought-provoking scenarios the article outlines: 1?? Optimistic: AI democratizes access to knowledge work, while other undervalued professions like caregiving rise in prominence. 2?? Dystopian: AI creates a hyper-specialized elite, leaving the majority in precarious, low-paying roles. 3?? Utopian: AI takes over mundane tasks, freeing us to pursue creativity, passions, and purpose. Katie's exploration of how AI could blur the lines between "skilled" and "unskilled" work really resonated with me—could we finally see a more equitable valuation of all forms of labor? What I found most compelling is her reminder that we have agency in shaping this future. The article challenges us to think critically about the frameworks, policies, and cultural norms that will guide AI’s impact on work. ?? As AI becomes more integrated into our lives, how are you preparing for these shifts? Are you optimistic, cautious, or somewhere in between? Read Katie Parrott's full article for a deep dive: https://lnkd.in/dGmBHAdB Katie Parrott - thanks for this thought-provoking piece! ?? #FutureOfWork #AI #KnowledgeWork #CareerDevelopment #AIRevolution #WorkplaceInnovation #CareerTransformation #FutureWorkforce #DigitalTransformation #AIAndHumanity #TechAndWork #FutureReady #SkillsOfTheFuture #WorkReimagined #KnowledgeEconomy

Here's what landed in your inbox today (if you're an Every subscriber): - Sari Azout challenges our obsession with doing more in "The End of Productivity" - The hidden opportunity in AI disruption: Katie Parrott on the future of knowledge work - How RAG is making AI more honest and creative with Mike Taylor - Master unbiased AI marketing with @Chris Silvestri's latest framework - Building a 500K-user AI business: Dan Shipper discusses the future of human-AI collaboration with Vicente Silveira Read more: https://lnkd.in/emYTQzad

In 1959, Peter Drucker made a bold prediction: Knowledge workers would be "the most valuable asset of a 21st-century institution." But with AI, that future is uncertain… The term "knowledge work" shaped our entire economy for decades. Each tech wave increased knowledge work: Industrial Revolution → Management needs Computers → IT professionals Internet → Digital marketers Mobile → App developers But AI might break this pattern. For the first time, AI might reduce the "skill premium"—the wage gap between "skilled" and "unskilled" labor. The future could go 3 ways. Today on Every, Katie Parrott breaks them down: https://bit.ly/40TLVKF

not your grandfather's team page

Traditional productivity tools are failing us in the age of creativity. While great at tracking tasks, these tools can't help us figure out what to create or support the messy, non-linear work of innovation. Sari Azout, founder of Sublime, proposes a new vision for creative tools that encourage connections, facilitate serendipity, and support deeper thinking rather than just faster output. Sari explores the limitations of current productivity tools and how we can design better ones for the creative age. (link in bio)

Artificial intelligence is redefining what it means to be productive. It’s also changing how important it is to be productive. In my latest for Every Inc. I wrote about why creativity, not productivity, will be the new currency of success - and how we need to rethink our tools to support deeper, more intentional creative work. https://lnkd.in/ehgkFrTb

“Everyone told Vicente Silveira that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. Vicente is the cofounder and CEO of AI PDF, a tool to help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload documents to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Even when some of his competitors closed up shop, Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. Today, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it with a five-person team and a friends-and-family funding round." Dan Shipper sat down with Vicente to understand the success of AI PDF. Read the full interview about how small teams can win in the age of AI (link in bio).

Two years after ChatGPT's launch, we're learning that AI adoption isn't just about better technology—it's about psychology. OpenAI's executives were initially puzzled by ChatGPT's massive success, since the underlying technology wasn't new. What made it revolutionary was how it presented AI through a familiar chat interface, making complex technology feel accessible. The story mirrors the original Macintosh, which sparked the personal computing revolution not through superior specs, but by making computers feel friendly and approachable. Research shows four key psychological factors drive tech adoption: expected performance, ease of use, social influence, and support systems. Rhea Purohit covers the psychology behind viral technologies and what it means for AI's future (link in bio)

Today, we go inside RAG, the technology helping AI tell the truth: Think of RAG (retrieval augmented generation) as giving AI an open-book test instead of asking it to memorize everything. RAG: - Reduces AI hallucinations by providing real-time context - Makes answers more accurate through vector search technology - Enables AI to find needle-in-a-haystack information with 90-100% accuracy - Powers smarter company knowledge bases and documentation In Mike Taylor's latest piece for Every, he dives into how RAG is making AI more trustworthy: https://bit.ly/410RsyS