Tesla's shares dropped more than 3% in after-hours trading following the release of its second-quarter earnings, which revealed profits well below Wall Street forecasts. The company's stock, already down around 14% this year, fell further after the report. Meanwhile, Elon Musk's attention seems divided as SpaceX, his rocket and AI venture, caught the spotlight with the largest stock market debut ever last month. This milestone made Musk the first trillionaire, though his net worth has slipped since then.
Software engineering, once among the highest-paying US jobs in 2022, faces disruption from AI, causing layoffs and underemployment. Matt, a software engineer, uses his train commute to develop a browser-based video game, coding entirely by himself to maintain his skills. Though his work increasingly involves reviewing AI-generated code, he avoids relying on AI to preserve his craftsmanship. His experience highlights a broader trend of engineers reacquainting themselves with core programming skills while pushing for collaborative efforts in adapting to AI-driven changes.
The European Commission has fined Google €890 million (£760 million) for violating competition laws within the Digital Markets Act. The firm is accused of prioritizing its own services, like shopping and hotel deals, over competitors' offerings in search results. Google must now treat third-party services appearing in its search results fairly and without discrimination.
When we imagine robots, we often picture something humanoid, but the real revolution in 2025 is happening behind the scenes—silent, relentless, and indispensable. Over a century after the term 'robot' was coined by Josef & Karel Čapek to mean artificial labor, LLM agents are now redefining business operations. These agents aren’t just interactive chatbots; they handle critical back-office tasks—processing documents, verifying data, updating systems, and flagging issues—without pause or error.
Unlike traditional automation, which relies on fragile scripts prone to failure with user interface changes, LLM agents are adaptable, self-aware systems that understand goals and figure out the methods autonomously. This shift is driving the intelligent automation market toward a $51 billion valuation by 2026, with LLMs leading the way. At Born Digital, we’re replacing bulky, old automation with compact, efficient agents that reduce overhead and increase productivity.
We’ve successfully deployed our first agent managing server support tasks—running diagnostics, restarting services, and escalating problems—saving significant time. Scaling becomes easy; instead of hiring more people, we deploy more agents that consistently follow policies and never lose focus.
This is the future Born Digital is building: intelligent, scalable infrastructure that operates like code. Čapek’s robots are here, working quietly in the background, handling tasks from reading logs to solving problems proactively. The opportunity is now—if your data is ready, your LLM agent can be operational in weeks. The real question is how many opportunities will you miss before then?
At the 2025 Consumer Electronics Show, Nvidia CEO Jensen Huang predicted a rapid surge in humanoid robots similar to the rise of generative AI. However, our research indicates that humanoid robot adoption will be fragmented and uneven, driven by specialized roles, geographical factors, and human reactions.
Unlike generative AI, humanoid robots are not one-size-fits-all. Each robot is tailored for distinct tasks—whether caregiving requiring emotional sensitivity or warehouse work needing heavy lifting—resulting in varied designs and technology setups. This specialization means companies focus on different market niches rather than producing universal robots.
Geographically, adoption depends heavily on factors like labor costs, regulations, and demographics. Countries with aging populations and labor shortages, such as Japan and South Korea, are hotspots for investing in humanoid technologies. China leads in manufacturing volume due to its comprehensive supply chain and cost advantages.
Additionally, human responses to humanoids vary widely, affecting their acceptance and success. While some studies report positive emotional connections, others highlight discomfort, influenced by personality types and cultural factors. Building trust through empathetic design is crucial.
For organizations, success requires a clear, role-specific approach, targeting suitable geographies and paying close attention to human factors. Leaders should engage in partnerships across industries and prepare employees and customers for this new interaction to harness the full potential of humanoid robots.
In a survey of 157 enterprises, many are giving AI agents increased autonomy without fully trusting the tests designed to ensure their reliability. Half of these companies have deployed AI features that passed internal evaluations but later failed in real-world customer interactions. Despite this, two-thirds allow AI agents to be deployed to production automatically, without human oversight. The biggest issue is the "evaluation gap"—a disconnect between the autonomy granted to AI agents and the trust in the evaluations meant to oversee them. Only 5% of organizations fully trust automated evaluations, citing poor alignment with real-world outcomes as the primary concern. Moreover, monitoring in production often checks only if the AI is functioning rather than verifying the correctness of its outputs. The market for evaluation tools remains fragmented, with many companies relying on provider-native tools or none at all, though investment in human review and observability is growing. While enterprises are moving towards greater automation, they continue to invest in oversight, highlighting a cautious approach amidst burgeoning autonomy. This points to a critical need for evaluations that better reflect actual performance to ensure AI agents can be trusted as their independence grows.
Across 101 enterprises, the challenge isn’t accessing AI context—it’s trusting it. Retrieval-augmented generation (RAG) dominates as the leading method to feed AI agents relevant business information, yet many organizations experience their AI confidently providing incorrect answers due to inconsistent or missing context. Despite the rise of provider-native retrieval tools like OpenAI’s file search and Google’s Vertex AI Search overtaking traditional vector databases, a significant trust gap remains. Most enterprises are actively building or piloting a governed semantic layer to address this gap, aiming for hybrid retrieval models that combine accuracy with governance. However, widespread adoption is still in progress, with many caught between the convenience of bundled provider tools and a desire to maintain best-of-breed solutions. This tension highlights that the core issue is not retrieval volume, but the quality, governance, and consistency of AI context—elements critical to AI’s reliable performance in business environments.
A study of 107 enterprises reveals significant gaps in AI agent security as organizations grant these agents system access without adequate controls. Over half (54%) have experienced security incidents or close calls involving AI agents. Despite this, only about a third of enterprises assign each agent a unique scoped identity, while most still share credentials, increasing risk. Additionally, just 30% isolate their highest-risk agents in sandboxes to limit damage if compromised. Security measures primarily rely on native provider tools from OpenAI, Google, and Microsoft rather than specialized agent security vendors. Satisfaction with these controls remains high, though spending on AI agent security is generally minimal, accounting for less than 10% of overall security budgets. Enterprises are divided on whether their AI defenses surpass attackers, with only a third confident they are ahead. A majority plan to update their security tools soon, indicating recognized vulnerabilities amid growing agent use.
A study across 107 enterprises reveals that AI infrastructure spending is growing rapidly, yet most organizations struggle to understand the actual costs involved. Many companies rely on familiar hyperscalers and model-provider APIs, but their next investments are focused on specialized AI compute resources rarely used today. Over half of these enterprises plan to switch or add providers within the next year, placing value on integration and total cost of ownership rather than on token price. However, GPU utilization remains low, with 83% running at 50% capacity or less, and only 44% of companies rigorously track their AI compute expenses. This mismatch between fast spending and poor cost visibility creates a "compute gap"— heavy investment without sufficient control. The report also notes high churn intentions among vendors and an emerging but under-recognized shift from GPU compute to memory bandwidth as inference demands grow. The findings suggest enterprises are eager to expand AI capabilities but need better tools to measure and manage their investments effectively.
Cisco's AI security lead, Amy Chang, revealed at VB Transform 2026 that multi-turn attacks breached AI models 88.3% of the time in a study involving nearly 7,000 multi-turn attacks on 15 leading proprietary AI models. This highlights the insufficiency of single-turn testing, which fails to capture risks posed in extended conversational interactions. Cisco’s findings were supported by data showing over half of surveyed enterprises have faced AI agent security incidents or near-misses, underscoring the urgency for improved security frameworks. Industry giants like Palo Alto Networks and CrowdStrike are responding by investing heavily in identity and isolation security layers designed to safeguard AI agents. Chang and other experts emphasized the importance of multi-layered security involving scoped permissions, sandboxing, and continuous monitoring to mitigate risks. They also stressed the need to rethink security strategies from a fundamental level, moving beyond static code reviews to agentic security lifecycles where AI agents actively participate in enforcing protections. The discussion addressed challenges like intent detection versus probability assessment and the dynamic nature of AI agent vulnerabilities, urging enterprises to adopt continuous, multi-turn testing approaches to stay ahead of increasingly adaptive adversaries.
