AI at Work: Meta Compute, Slackbot and the Enterprise AI Era

Today’s AI news reads like a compass pointing toward infrastructure, governance, and the new work rituals that AI is quietly stitching into the fabric of business. Meta’s announcement of a mammoth $72 billion investment in AI infrastructure for its 2025 fiscal year signals a consolidation of compute power at scale, as data centers grow more capable and capable of supporting sophisticated AI workloads. Yet the story isn’t just about giants building capacity; it’s about how these capabilities translate into real enterprise outcomes. Amid the scale, companies like Egnyte are showing how AI can accelerate onboarding and code comprehension without eliminating human oversight, while Sakana AI is pushing the frontier with ALE-Agent, a coding assistant that learns to optimize itself over extended reasoning sessions. In parallel, Salesforce is turning Slack into an enterprise-grade agent that sits at the center of daily work, capable of searching, drafting, and taking action across enterprise data—all without leaving the chat window.

What ties these threads together is a shift from hype to practical, governance-aware deployment. A chorus of voices in policy and industry commentary argues that governance—not consciousness or personhood—is the real challenge for AI as a business technology. Discussions about electronic personhood and liability have given way to questions about how to structure contracts, access, and accountability for autonomous agents that operate across systems. This reframing matters because it sets the agenda for how AI will be adopted in regulated sectors, how data flows will be governed, and how risk is managed when AI systems act as economic agents in contracts and operations.

Slack’s reinvention as a fully capable enterprise agent is a case in point. Salesforce reports massive internal traction for its Slackbot, with tens of thousands of employees testing and a growing expectation that the agent will become a routine part of day-to-day work. The platform’s ability to access enterprise data, generate documents, schedule and coordinate meetings, and even prototype ideas in Canvas demonstrates a practical path to multi-agent ecosystems inside a single chat interface. Yet this progress comes with considerations about price, API access, and how to balance third-party tooling with native data governance—the kind of nuance that separates a clever demo from a durable business capability that can scale across the organization.

Meanwhile, the enterprise optimization story is evolving beyond simple automation. Sakana AI’s ALE-Agent excludes the old binary choice between humans or machines by introducing a dynamic reconstruction approach to optimization. The agent uses inference-time scaling, generates hundreds of candidate solutions, and maintains a forward-looking strategy through context drift, effectively building a mental model of “Virtual Power” that values future capabilities as assets today. The result is a new class of autonomous systems that can tackle complex scheduling, resource allocation, and workflow optimization—potentially reducing the cost of optimization to a few thousand dollars in exchange for millions in efficiency gains. This is the kind of practical ROI that enterprises crave as compute costs come down and the appetite for smarter systems grows.

Beyond these corporate tales, AI’s broader cultural and societal dimensions remain in view. The governance debate is not going away; it’s morphing into how organizations structure risk, ensure privacy, and prevent harm as AI becomes embedded in decision-making across sectors. The public discourse touches another emotional layer: the human relationships people are forming with AI, whether in professional tools like Slackbot or in consumer-facing conversations discussed in books about AI ethics and intimacy. And with the rise of deepfakes and photo- and video-based manipulation, the need for robust media literacy and verification is clear. The landscape is changing fast, but the throughline is clear: successful AI adoption in the enterprise will hinge on a careful balance of compute power, governance, user experience, and tangible business value.

As these developments unfold, the ecosystem of AI in the workplace continues to expand across platforms, from Meta’s infrastructure plans to the practical adoption of Slackbot in Salesforce, and from autonomous optimization in Sakana AI to the governance frameworks that keep such systems accountable. The conversation now encompasses not only who builds the next model, but who manages the data, who bears liability, and how to design tooling that respects human judgment while expanding what teams can accomplish. In short, we’re entering an era where AI is not a distant horizon but a daily operating reality—one that requires thoughtful architecture, responsible governance, and a clear eye on the business value it delivers.

Sources

1. Meta Compute and AI infrastructure plans
2. UK politics live coverage
3. Governance of AI matters, not personhood
4. Grok integration into Pentagon networks
5. Australian politicians condemn Grok/X
6. Egnyte hires juniors despite AI tooling
7. Salesforce rolls out Slackbot AI agent
8. Sakana AI ALE-Agent breakthrough
9. Love Machines by James Muldoon — review
10. Bondi attack deepfake Guardian video