Inside the brutal truth about AI adoption

Today, we’re looking at Baidu’s boldest move yet to scale AI: doubling down on cloud services and robotaxis. Today, we’re also looking at Runway expanding its world simulation models from film and media into robotics and self-driving.

In today’s post:

• Most used AI tools right now

• Baidu’s AI bets are starting to pay off

• Runway’s next act: teaching robots with AI worlds

• Study: Why 95% of AI projects fail

• Best AI tools to explore right now

What’s Trending Today

AI chatbots

The 10 Most-Used AI Chatbots in 2025

AI chatbots have become the everyday interface for work and life. From drafting emails to research to customer service, they’re everywhere. But the usage data shows a market that’s anything but balanced.

• ChatGPT leads with 46.6 billion visits nearly half of all chatbot traffic.

• Its dominance is staggering: four times the combined usage of every other chatbot on the list.

• DeepSeek, the runner-up, logged 2.74B visits less than 4% of the total market.

• Gemini and Claude hold third and fourth place, each crossing the billion-visit mark with steady growth.

• Perplexity and Grok are climbing fast, though still far from the leaders.

• Poe, once an early contender, saw traffic fall 46% a cautionary sign in a crowded space.

• Meta AI and Mistral barely crack 1% market share, showing how tough it is to scale. Microsoft Copilot also got less users but its up there with Grok and Gemini.

Here’s what I think:

The chatbot market isn’t just competitive it’s lopsided. While new entrants grow quickly, ChatGPT’s lead is widening, not shrinking. The lesson is simple: in AI, scale matters, and the gap between the top and the rest may be harder to close than anyone expects.

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LAUNCH

Cloud growth and robotaxis fuel a Buy rating for BIDU

Yahoo Finance reports that Baidu is earning fresh investor confidence, with Tiger Securities reiterating a Buy rating and a $100 price target on the stock.

Here’s everything you need to know:

• Baidu’s AI Cloud revenue rose 27% year over year to RMB 6.5B.

• Subscription services and heavy GPU utilization (90%+) are driving the surge.

• Management expects slower growth ahead due to swings in project-based revenue.

• Apollo Go, Baidu’s autonomous driving service, delivered 2.2M fully driverless rides in Q2.

• That’s a 148% jump year-over-year with unit economics improving in China.

• Baidu is expanding Apollo Go globally, partnering with Uber in Asia/Middle East and Lyft in Europe.

• The company continues to diversify beyond search with AI initiatives like Ernie, its conversational model.

Here’s what I have in mind:

Baidu is showing how China’s tech giants are leveraging AI to scale beyond their legacy businesses. Cloud and robotaxis may not make headlines like search disruption, but they represent durable bets on infrastructure and mobility. The question is whether Baidu can keep global momentum while managing slowing growth at home.

UPGRADE

From movie sets to machine labs, Runway bets on robotics

Runway, the New York startup known for its generative video models, is now moving into robotics and self-driving industries looking to its AI “world models” as training grounds.

Here’s everything you need to know:

• Runway built its reputation on tools like Gen-4 for video generation and Aleph for editing.

• As its simulations became more realistic, robotics and autonomous vehicle companies started knocking.

• Training robots in the real world is slow, expensive, and risky; simulations offer scale and precision.

• Runway’s tech lets teams test variables like a single turn or maneuver while keeping everything else constant.

• Unlike Nvidia, which released dedicated robot training systems, Runway plans to adapt its existing models instead of creating a separate line.

• The company is hiring a robotics team and fine-tuning its models for industrial use.

• Investors like Nvidia, Google, and General Atlantic already in for $500M back the expansion, betting that “simulation” is a principle that can cross industries.

• The broader vision: world models won’t stop at film sets, they’ll become the backbone for robots, cars, and possibly any system that learns by interacting with the environment.

Runway’s shift says more about AI’s trajectory than the company itself. The best models aren’t built for one vertical, they’re built to mirror reality. Once you can simulate the world convincingly, training both actors and robots becomes the same problem. The question isn’t whether these simulations will spread beyond entertainment, but how fast industries will reorganize around them.

Generative AI

AI isn’t broken but how we’re using it is.

The MIT Media Lab recently found that 95% of generative AI investments show no measurable ROI.

Cue the panic. But the problem isn’t AI it’s how companies are experimenting with it.

• Most organizations are repeating old mistakes from the digital transformation era: too many scattered pilots, no clear path to scale.

• The data backs this up: AI efforts tend to focus on flashy marketing experiments, not core operations where real value lives.

• Leaders are letting AI experimentation drift without anchoring it to business outcomes a “10,000 flowers bloom” approach that rarely works.

• The better approach? Start with how AI can directly improve customer value then run lean, focused experiments tied to real problems.

• Use frameworks like IFD (Intensity, Frequency, Density) to prioritize experiments that matter and skip the cosmetic use cases.

• Once something works, scaling it requires a “ninja team”: lean, cross-functional, and backed by leadership to move fast and cut red tape.

Here’s what I think:

AI isn’t in a trough of disillusionment it’s in a phase of misapplication. The real winners will be the ones who focus less on hype, and more on using AI to solve actual problems for real customers.

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