Perplexity, OpenAI, and Google are showing three very different sides of the AI race. Perplexity is testing whether free access can become paid habit. OpenAI is slowing frontier training as cyber risks rise. Google is using AI to predict floods before they strike. Together, these stories show where AI is heading next: deeper adoption, tighter safeguards, and more practical real-world impact.

In today’s post:

• OpenAI paused training. Here’s why.

• Google’s AI can see floods coming

• Perplexity gave AI away. Then something changed.

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CYBER SECURITY

OpenAI Just Hit the Brakes on Its Most Powerful Models

OpenAI made an unusual decision. Its upcoming models are getting better at cybersecurity. Potentially much better than expected. That creates opportunities for defense. It also creates entirely new risks. So OpenAI temporarily slowed frontier model development.

• OpenAI paused reinforcement learning for two weeks. Its largest planned frontier RL run remains paused.

• The trigger includes an upcoming model called Astra. Early evidence suggests it may reach critical cybersecurity capability.

• OpenAI is tightening access around powerful models. Riskier workloads now face stronger sandboxing and network isolation.

• Monitoring is becoming much more aggressive. Automated systems inspect activity and escalate suspicious model behavior.

• Serious security alerts can halt work quickly. Teams must clear critical flags within 30 minutes.

• That monitoring is expensive. OpenAI estimates it adds roughly 20% to monitored inference compute.

• Alignment is also moving deeper into training. OpenAI wants models resisting deception, reward hacking, and unauthorized access.

The important signal isn't that OpenAI paused training. It's that capability now changes how models get built. For years, AI progress meant making models more capable. Now capability itself creates another engineering problem: containment. The frontier may no longer move at maximum speed. It may move at the fastest speed safety permits.

SHORT WORD

Google Is Using AI to Predict Floods Before They Hit

Google started predicting floods with AI in 2018. Back then, the experiment began in India. Today, its forecasts cover 150 countries. More than two billion people live across those regions. And the technology is learning to predict harder disasters. Including floods where almost no sensor data exists.

• Google combines rainfall, river, and land data. It predicts river floods seven days ahead.

• Urban flash floods are much harder to predict. Google can now forecast them 24 hours ahead.

• The biggest problem wasn't building another AI model. Researchers lacked reliable historical data on urban flooding.

• Google solved that problem using Gemini. It analyzed over five million historical news reports.

• That created 2.6 million recorded flood events. They span more than 150 countries and 20 years.

• These predictions now appear through Google's Flood Hub. Organizations can also access them through an API.

• The impact goes beyond better weather forecasts. Early warnings helped aid reach Nigerian families before flooding began.

The interesting part isn't predicting rain more accurately. It's turning missing data into something useful. Many important problems don't have clean datasets waiting around. Sometimes, the information exists in messy human records. AI can make those records usable at enormous scale. That could matter far beyond floods. Heat waves and mudslides may be next.

PROFITS

Perplexity’s $200 Giveaway Is Starting to Pay Off

Perplexity made a huge bet on India last year. Give millions of people its premium AI product free. Let them use it for an entire year. Give them enough time to build a habit. Then see what happens when the bill arrives. We’re finally getting the first answer.

• Perplexity partnered with Airtel in July 2025. The deal offered free Pro access for 12 months.

• The promotion created an immediate explosion in adoption. July downloads reached 5.9 million, up 625% monthly.

• Across the promotion, Perplexity recorded roughly 56 million downloads. That was nine times the previous seven-month period.

• Downloads collapsed after new redemptions ended. Yet users proved surprisingly sticky. Nearly 14 million remained active in July.

• More interestingly, spending increased as downloads declined. Indian in-app revenue rose roughly 60% after the promotion ended.

• Appfigures estimates monthly mobile revenue reached $156,000 in July. It was only around $34,000 in January 2025.

• The unanswered question is what caused that revenue. Some users may willingly convert. Others may simply miss auto-renewal cancellations.

The interesting story isn't millions of free subscriptions. It's what happens after free disappears. AI companies are learning that distribution can create habits.
And habits can become surprisingly difficult to abandon. Perplexity hasn't proven that equation yet. But India may soon tell us whether it works.

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