MARKETS

Why the AI race just shifted back toward Microsoft

The AI market is starting to value efficiency over power. 

On Thursday, Microsoft saw its shares skyrocket more than 16% after reporting fourth-quarter revenue results that beat analysts' expectations, including a 43% jump in its Azure cloud business and more than 30 million paid seats and 40 million agents built for Microsoft 365 Copilot. In one day, the company added a record-breaking $450 billion to its market capitalization, its largest jump since 2008. 

The company's overall revenue rose around 18% for both the quarter and the full fiscal year, hitting $90 billion for the quarter and $332 billion for the year. Notably, the revenue from its Anthropic investment saw $3.2 billion in gains for the quarter, while its OpenAI investment lost $600 million in the same timeframe.

Microsoft's win stands in stark contrast to the responses to Meta's quarterly results, which missed investor expectations on both earnings and revenue guidance after reporting a 91% drop in year-on-year free cash flow to just $784 million as it continues its massive AI spending spree in pursuit of competing with the frontier labs on AI. 

• Meta reported that it expects capital expenditures for the year to sit anywhere from between $130 billion and $145 billion, dedicated mostly to AI infrastructure. 

• Conversely, Microsoft's capital expenditures forecast remained unchanged at $175 billion, the company's spending spree is starting to see significant returns. 

What sets Microsoft apart in this case is that its strategy is largely focused on efficiency and winning over enterprise customers. Its first reasoning model, MAI-Thinking-1, launched in early June, is a clear example of this, sporting only 35 billion parameters and a 128K context window. It was built to lower token costs. Its latest security products have a similar pitch: its recently announced MAI-Cyber-1-Flash model can handle 90% of the security tasks involved in identifying and remediating vulnerabilities at half the cost of leading models. 

That bet on efficiency and affordability is starting to pay off. In the company's earnings report, CEO Satya Nadella said, "We are advancing the frontier on the cost-to-outcome curve, ensuring every customer can turn tokens into business results."

And other companies are clearly starting to see the value in efficiency, too. OpenAI, for instance, cut the prices of GPT-5.6 Luna, its lighter-weight model, by 80% and Terra, its midweight model, by 20% after finding ways to improve the efficiency of the models. And Thinking Machines debuted Inkling-Small on Thursday, a lightweight model that it says achieves performance comparable to its recently announced 975-billion-parameter model at a third of the size, sitting at 276B parameters with 12 billion active

Efficiency and cost savings aren't the only reason Microsoft is winning over the AI market. If there is one clear advantage that Microsoft has in AI, it's legacy. Microsoft has built itself into the foundation of millions of enterprises, with its Outlook and Teams suite being practically synonymous with workplace technology. Even though its homegrown models aren't the most powerful on the market, the fact that they are built into Microsoft's existing stack makes them a more seamless addition for most enterprise workers. As a result, Microsoft Copilot is viewed as the safe, enterprise-ready, easy-to-deploy option. Meta, meanwhile, doesn't have that legacy to back it up, and is instead spending billions to try and keep up with Claude and ChatGPT in the race to build frontier AI models at a time when the market is seemingly shifting its sights. With the release of Kimi K3, a Chinese open-source model that rivals the best AI models from OpenAI and Anthropic, there's growing concern about model commoditization. In other words, the value in the AI ecosystem could be accruing more to the companies helping deploy it safely and consistently rather than to the AI labs pushing the frontier.

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HARDWARE

Google's robotic models could shift both AI and robots

The hardware behind humanoid robots has been available for years, but getting those machines to learn and interact with the world in a truly human-like way has remained the central challenge. Google says it's one step closer.

On Thursday, Google launched Gemini Robotics 2, its most advanced vision-language action (VLA) model yet, which converts the robot's visual and language inputs into motor control for the full humanoid. This means the robot can reason and take action with every part of its body. 

Google also released two other models: Gemini Robotics ER 2 and On-Device 2, which together bring robots a step closer to acting like humans.

• Gemini ER 2's function is to interpret a human's command and determine the steps to complete the task, with an understanding of the physical world. It is also responsible for allowing robots to work as a team. 

• Meanwhile, On-Device 2, Google's more efficient VLA model, is optimized to run locally on robotic devices, allowing it to adapt to a new robotic body in just hours. 

Ultimately, the models should enable robots to take better real-world action with whole-body control and dexterity, and even interact with others, allowing them to perform a wider range of tasks, rather than the one fixed, repetitive task that humanoid robots have typically been limited to. This points to a broader industry trend toward generalist models that allow robots to handle a wide range of tasks without significant training. 

For instance, UMA, a physical AI startup founded last year by a former staff scientist at Tesla, unveiled a Real-Time Learning architecture in which robots can learn through demonstration rather than manual programming, enabling them to better mimic human interactions in the physical world. Meanwhile, robotics firm Generalist is on a mission to build a universal AI brain that allows robots to learn and perform more tasks, and AI video firm Runway has started an open physical AI initiative dedicated to the generalization in robotics. 

All of this is happening against the backdrop of the FCC's decision this week to ban foreign-made advanced robotic devices that weigh over 4.4 pounds, which includes humanoid robots, robot vacuums and even robot lawn mowers. China dominates the robotics sector, especially in humanoid robots, so the ban could lead to a setback for the US robotics sector.

While AI is being rapidly adopted in software by working professionals and enterprises, robotics looms as a long-term opportunity to drive even bigger changes in work and life. And let's not forget that AI and robotics have a symbiotic relationship. AI models are often used to create synthetic data that mimics real-world situations where collecting actual data is difficult, and that data is used to train robots to handle a wider range of tasks, creating a feedback loop that generates more data to improve the models. Additionally, AI can help robots reason in real time, decide which steps to take next, and tap into more of their physical capabilities, enabling them to be much more useful, especially in situations they may not have been initially trained for. It's through this symbiotic relationship that AI and robotics could reach mass adoption at a much quicker pace.

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GOVERNANCE

IBM study: AI-enabled cyber attacks cost 20% extra

AI isn't just making security breaches more common. It's making them more expensive. 

A report from IBM published last week found that the average cost of an AI-enabled malicious security breach for enterprises is roughly $6 million, around $1 million more than the global average for all security breaches. Around one in four security breaches in the last year were aided by AI, a 56% increase from the previous year. 

"What's changing is the economics of cyberattacks. AI is making attacks faster and cheaper, while breaches keep getting more expensive," Suja Viswesan, VP of IBM Security Software, said in a statement. 

A large majority of the attacks targeted critical infrastructure sectors, with energy and financial services seeing the highest concentration of AI-driven breaches. IBM noted that the density of attacks in these sectors presents the potential for cascading risks that interrupt supply chains and essential services. 

According to IBM, these attacks were mostly facilitated by deepfake impersonation and AI-powered malware, and are becoming cheaper and easier for attackers to launch. However, AI may also present a solution: 

• Companies that reported using AI in their security operations managed to cut the cost of breaches by an average of $2 million. Around 75% of the organizations surveyed have adopted AI into their security operations thus far, and three-fourths say that frontier AI is causing them to rethink agent deployment in security. 

• Still, adoption within security is uneven. While more than 50% said they use AI for threat detection and containment, only 18% apply agents to vulnerability management, leaving potential attack windows open as AI makes it easier than ever to find and exploit vulnerabilities. 

IBM's report is just the latest sign that frontier AI is causing a cybersecurity shake-up across industries, with OpenAI's breach of Hugging Face demonstrating the sheer power that these models possess, serving as a warning for what may come as bad actors get their hands on increasingly capable AI. Even some of the biggest companies in tech are strengthening their cybersecurity posture: Google now is patching Chrome twice a week, aided by rapid AI-assisted bug discovery.

One of the most dangerous vulnerabilities that an enterprise can have is thinking that it's not vulnerable. Many organizations assume that a breach simply won't happen to them and don't invest in their security posture. But the speed and simplicity that AI has afforded attackers means that anyone can be a target, and any organization can suffer millions in losses as a result. Still, investing in AI-enabled cybersecurity doesn't have to be expensive. Both Microsoft and Cisco, for instance, are investing in efficient and affordable AI systems that are purpose-built to bolster enterprise defenses without breaking budgets. However, what's equally important to having the right tools for defense is having the right culture around security within your organization. Consistent education on AI risks and threats can go a long way in keeping an enterprise from falling victim to deepfakes or malware.

LINKS

• ChatGPT, Roblox to face tighter EU rules as "very large online platforms"

• US FCC ban covers software-controlled wireless robots over 4.4 pounds

• OpenAI CFO Sarah Friar tells staffers July ARR exceeds Q2 total

• Airlines reportedly adopt AI to adjust seat prices in real time

• DeepSeek to build 1-gigawatt data center in inner Mongolia

• AI pendant maker Friend debuts device that can talk back

• Gemini Spark: Google's personal agent now integrates with Chrome. 

• Inkling-Small: Thinking Machines' latest model, offering comparable performance to predecessors at a quarter of the size. 

• Gemini Omni: Users can create up to ten videos at no cost until August 4th.  

• Numbat: Perplexity is open-sourcing its agent-detection and response layer.

• Nixon Peabody LLP: AI Advisor

• Gamma: AI engineer

• University of Washington: AI Agent Builder

• General Motors: AI Agent Engineer

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