Cognition, an AI coding startup, has acquired Poke, an AI assistant known for its friendly, conversational texting style, in a deal valuing Poke in the low nine figures. This acquisition integrates Poke’s unique interaction approach into Cognition’s coding assistant, Devin. The move highlights a shift in AI development where the personality and manner of interaction are becoming just as crucial as the underlying technology itself.
By Sumeet Vaidya
Frontier labs and major AI providers promise revolutionary innovation, and many deliver. But recent incidents like the Anthropic policy reversal and security lapses at Hugging Face and OpenAI reveal instability in these platforms. This unpredictability poses a serious risk to organizations relying on these AI models for critical operations.
Open-source initiatives such as OpenClaw and DeepSeek offer cost-effective models with comparable quality, closing previous gaps in usability, safety, and accessibility for enterprises. This evolution forces CTOs, CIOs, and engineering leaders to ask how to maintain reliability while controlling costs amid fluctuating hyperscaler pricing.
The solution is not simple but highlights the need for systems enabling rapid swapping of AI models and adaptive collaboration between AI agents and humans using real-world data and tools. Building a robust foundation ensures organizations can stay agile without merely chasing trends.
Engineering leadership has moved away from costly token-spending strategies toward sustainable practices that prioritize team morale and infrastructure modernization. Future enterprise AI must empower human-agent cooperation with equal access to real environments and strict governance over credentials and actions.
Adopting flexible architectures that accommodate model changes, integrate emerging tools, and prevent vendor lock-in builds resilience. This approach allows organizations to evolve with AI advancements steadily and securely, embodying a flexible, adaptive culture for long-term success.
Sumeet Vaidya, CEO and co-founder of Crafting, previously shaped AI engineering at Meta, Uber, and Discord.
Alphabet, Google's parent company, posted impressive second-quarter earnings with $120 billion in revenue, marking its 12th consecutive quarter of strong financial performance. These results surpassed expectations despite delays in launching the advanced AI model Gemini Pro. Investors are closely watching how Alphabet’s significant AI investments impact its financials as the company increases its annual capital expenditure forecast to $200 billion, with quarterly spending doubling year-over-year.
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.
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?
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.
