🦾How You Can Leverage:

Dr. Ben Goertzel just dropped the most uncomfortable truth about AGI. 

The famed computer scientist who LITERALLY coined the term “artificial general intelligence" back in 2002 revealed something on today’s Everyday AI show that should keep every business leader awake at night. 

There's a window between human-level AGI and superintelligence that could only last…… 

One. Month.

That's potentially all the time between "Hey, this must be “AGI and "Welp, game over for human control." Ben calls it the "time to fume."

Nobody knows which timeline we're on.

So on today's show, we chatted with the AI legend on why this timing gap determines who controls the future of intelligence itself. 

Buckle up. It’s about the get weird shorties. 👇

Current LLMs are basically the world's most sophisticated mimics.

They look brilliant because they've consumed the ENTIRE internet for breakfast. Every Wikipedia page. Every research paper. Every Reddit thread about whether cereal is soup. Every brilliant piece of science that no one paid attention to. 

When they seem to "leap" beyond their training, Ben says they're actually taking tiny hops because their dataset is stupidly comprehensive.

Real AGI means something completely different, though.

Learning to drive a car, then figuring out how to operate a forklift without additional training. Current AI needs to binge-watch a million forklift videos first.

Massive difference.

Most companies are betting their AI strategy on linear scaling. "More data equals more intelligence, right?"

Nope.

The gap between pattern matching and genuine reasoning isn't a smooth ramp. It's a cliff. And we're about to discover who built their strategy on solid ground versus who's standing on nothing but wishful thinking.

Try This: 

Map your AI use cases into two distinct categories: pattern matching versus actual reasoning required. Start experimenting with neuro-symbolic AI systems that combine LLMs with symbolic logic engines. 

A 2024 systematic review identified 167 research papers showing these hybrid approaches can achieve genuine reasoning capabilities that pure LLMs can't match. 

Companies building these hybrid architectures now will dominate when true AGI emerges, while everyone else scrambles to catch up.

Ben casually dropped an early contender for the productivity stat of the year.

AI tools make him FIVE TIMES more productive as a researcher. Not someday. Not in some distant future.

Right now.

While everyone debates whether AI will replace jobs, scientists are already using it to accelerate their own AI research by 500%. Every breakthrough happens faster. Which enables even faster breakthroughs.

When scientists and researchers work faster, they create AI systems that will build better AI systems that scientists and researchers will use (in less time, of course) to create….

… 

OK, you get it. 

The world’s smartest AI talent is working at warp speed creating AI models so smart that the models themselves are starting to do the work of humans who create AI models. 

It’s a slippery slope toward superintelligence. 

It's recursive improvement cranked to eleven.

Then zoom out and think of the other implications for a hot minute. The same tools helping discover life extension therapies? They're simultaneously helping intelligence agencies crunch global surveillance data.

Same tech. Completely different outcomes. 

Ben’s team figured out that the biggest research bottleneck wasn't fancy algorithms. It was boring data preprocessing. LLMs turned months of manual dataset normalization into days of automated pipeline work.

Try This: 

Stop treating AI like a fancy search engine and start using it as your research multiplier. 

Set up automated data preprocessing pipelines using Claude or GPT-4 for your industry's research datasets.

Track your time savings weekly - if you're not hitting at least 2x productivity gains, you're leaving competitive advantage on the table.  

Here's the conversation that keeps Silicon Valley execs awake at night.

(Aside from the whole $100 million poaching salaries….) 

The most important battle in AI isn't about who builds the smartest models. It's about who CONTROLS the infrastructure when those models become smarter than every human combined.

Think about that for a second.

Ben has been grinding on this exact problem for years with SingularityNET because he realized something terrifying. If one company (or country!) nails human-level AGI first, they might control the entire pathway to superintelligence.

Remember that "time to fume" window from earlier? If AGI hits superintelligence in months instead of years, whoever builds the first system could be the last group of humans making decisions about intelligence on Earth.

Wild to think about, right? But…. it’s entirely feasible as science fiction-y as it sounds. 

While everyone obsesses over which AI model to use, the real strategists are positioning for infrastructure control. Decentralized protocols spread that power across thousands of developers, server operators, and compute owners instead of letting one tech giant have ownership. 

Think of it like electricity. Would you rather have one person control all the world's power, or distribute that control across a network that no single entity can dominate?

Try This: 

Stop thinking like a consumer and start thinking like an owner. Research decentralized AI platforms beyond just SingularityNET - explore Bittensor for decentralized machine learning, Akash Network for distributed compute, and Ritual for decentralized inference. Start small by contributing compute resources or running validation nodes. 

The relationships you build with these protocols today will determine whether your company influences AGI development or just watches from the sidelines. 

McKinsey data shows that companies positioning for distributed AI infrastructure now could capture massive value as centralized models hit scaling bottlenecks - but only if you start building those network effects before the rush begins.