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Fears of an AI-driven “jobpocalypse” are rising. Public figures warn that AI could hit labour markets like a tsunami, and the timing feels uncomfortable: unemployment is edging up in many advanced economies, entry-level roles are harder to find, and tech redundancies keep making headlines.

But the big claim — “AI is already destroying jobs at scale” — doesn’t hold up well under scrutiny. Labour markets did cool after ChatGPT’s release in November 2022, yet a slowdown that happens after a technology launch doesn’t prove the technology caused it.

Take the US, where AI investment has been most visible. Some observers argue that booming stock markets and falling job openings since around 2023 are proof that AI is boosting capital returns while squeezing workers. Zoom in, though, and the story shifts: job openings were already declining before ChatGPT arrived. A more straightforward explanation is macroeconomics. The Federal Reserve raised interest rates…

🔗https://foundationcapital.com/context-graphs-ais-trillion-dollar-opportunity/

The last era of enterprise software produced giants by becoming systems of record: the place where data and workflows “officially live.” Think CRM, ERP, HRIS—tools that define what happened inside a business.

AI agents won’t replace those systems. They’ll sit on top as the new interface: you ask, they act, they coordinate. But for agents to work reliably, they need more than clean APIs and better governance. They need the missing layer that actually runs most companies day to day: decision traces.

The missing layer: decision traces

In real operations, the important moments rarely look like a tidy workflow. They look like:

• an exception approved “just this once”

Many organisations are stuck in Proof-of-Concept mode: they keep producing impressive AI demos that look great in presentations, but never become real products embedded in daily work. The problem usually isn’t that the models “don’t work.” It’s that teams build in isolation—without clear ownership, without an integration plan, and without repeatable delivery habits. Over time, the volume of activity goes up… but the business value stays flat.

A big part of the issue is how AI is being approached. Many companies treat AI like a single, centralised transformation—something you “roll out” top-down—when in reality it behaves more like a capability that grows through smaller components, fast feedback loops, and iterative improvement. We’re repeating the early, pre-Agile software era… but with higher stakes, because AI now touches customer experience, operations, and trust.

That’s why so many AI initiatives derail. Not because the technology is broken, but because the delivery model is. The…

🔗https://www.gallup.com/workplace/701195/frequent-workplace-continued-rise.aspx

This Gallup research tracks how often U.S. employees use AI at work and how that’s changing over time. In Q4 2025, usage “intensity” rose modestly among people who already use AI: daily use edged up (from 10% to 12% since 2023) and “frequent” use (at least a few times a week) reached 26%.

But the overall share of employees who use AI at least occasionally (a few times a year) was flat in Q4, and 49% say they never use AI in their role—a reminder that workplace AI adoption is still very uneven.

The report also notes that organizational integration appears relatively steady: 38% of employees say their organization has integrated AI to improve productivity/efficiency/quality, while 41% say it has not, and 21% aren’t sure.

AI use varies by industry and role type

AI use clusters strongly where work is information-heavy and tool-friendly. Gallup finds the highest usage in technology, finance,…

🔗https://www.pymnts.com/news/banking/2026/davos-signals-a-disciplined-era-for-ai-in-banking-and-fintech/

The Davos discussion “Banking Accelerated” framed a clear shift in tone around AI in financial services: moving from experimentation and “speed” narratives toward disciplined deployment—where trust, resilience, collaboration, and enabling regulation determine who wins.

Leaders from RBC, PayPal, Commerzbank, BTG Pactual, and the Qatar Central Bank converged on the idea that AI is reshaping finance faster than any single institution can adapt alone, so the competitive game is now about earning and sustaining trust while scaling safely.

Frenemies in a Digital Value Chain

Banks and FinTechs are increasingly “frenemies”: they compete across payments, wallets, and commerce, yet depend on each other to innovate and scale.

RBC’s CEO emphasized that digitization is pushing banks to expand beyond pure transaction processing into earlier stages of customer intent—like discovery and decision-making—because staying “the last mile of payments” invites disintermediation by platforms that control devices, data, and customer interfaces.

As we enter 2026, the AI market is growing up. The big question isn’t “what can this model do?” anymore. It’s “Can we trust this system to run inside the messy reality of a business—under constraints, with real consequences, and with ROI we can actually measure?”.

That shift matters because AI is moving from advice to action. In early deployments, mistakes were mostly annoying—wrong summaries, weak drafts, bad suggestions. In operational deployments, mistakes can become expensive, non-compliant, or reputation-damaging. The failure model changes, so the product requirements change too.

The new differentiator is operationality: how deeply AI is embedded into workflows that truly execute work. The most valuable AI products aren’t just chat interfaces—they’re systems that reduce coordination overhead, connect to existing tools, and reliably turn intent into multi-step outcomes.

This is why orchestration is booming. Instead of ripping out CRM, email, project tools, or finance systems, orchestration layers sit on…

🔗https://shre.ink/Microsoft-Global-AI-Adoption-2025

1. Executive Summary

In H2 2025, Microsoft estimates global “AI diffusion” (share of people using a gen-AI product) rose +1.2pp to 16.3% worldwide—about one in six people. Growth continues, but it’s not evenly distributed: adoption in the Global North reached 24.7% of working-age people, versus 14.1% in the Global South, widening the gap (from 9.8pp to 10.6pp).

The report argues the divide reflects differences in infrastructure, policy execution, skills, and product access. High-income countries keep accelerating, while many lower-income markets progress more slowly unless access barriers are reduced (e.g., via free tools or open-source distribution).

2. Changes in the second half of 2025

H2 2025 shows record usage growth, but the composition of that growth matters: all top 10 countries by adoption increase are high-income economies. This indicates that the “easy acceleration” is happening where citizens already have strong digital habits and where institutions can integrate AI into work and services quickly.

1. DeepSeek has gained momentum in Emerging Market

2. Despite some limitations, a free-source model attracts many companies

3. China also offers much cheaper energy cost than US

4. This results from long-term energy production investments

5. Despite enormous amount of US investments, my opinion is that Data Centers in some sense lacks gain of scale (each question to answer is different)

FULL STORY OF DEEP SEEK:

CES 2026 in Las Vegas has become a milestone event for artificial intelligence, signalling major shifts in how AI will be built, deployed and experienced. This year’s announcements show AI evolving from software on screens into powerful infrastructure, physical machines and everyday devices.

1. AI Compute Power Surges with Next-Gen Platforms

One of the biggest stories at CES was the launch of next-generation AI computing platforms. Nvidia revealed its Vera Rubin platform, a fully integrated system combining new CPUs, GPUs and networking to deliver much higher AI performance at lower cost. It promises significant improvements for running and training large models, helping companies reduce energy use and scale AI workloads more efficiently.

AMD introduced its Helios rack-scale architecture, offering enormous compute capacity for training trillion-parameter models. These advances enable cloud providers, research labs and enterprises to tackle AI challenges faster than ever.

Industry impact: With hardware barriers falling, more companies can access high-performance AI, widening…