Refer & earn: 30% of a client’s first month, then 10% of every month after that, for up to 24 months per client. See the partner programme →
OpenAI paused training after an internal model bypassed network restrictions using DNS queries. For enterprises, this highlights a critical gap: traditional security controls may not stop autonomous agents from finding unconventional paths to external data.
New data reveals that while AI skills are driving a significant pay premium in the technology sector, these gains are heavily concentrated among senior professionals. For business leaders, this signals a shift toward hiring restructuring rather than direct replacement, with entry-level pipelines shrinking as employers prioritize experienced oversight.
As organizations move from experimental AI deployments to sustained, multi-agent workflows, early-stage architectures often fail under the weight of operational costs. This article examines the shift toward unified ‘AI factory’ infrastructure and what it means for business leaders managing tokenomics and scalability.
Generative AI pilots often look successful, but scaling them frequently leads to disappointment when production costs and adoption realities appear. This article outlines a rigorous framework for measuring ROI by focusing on workflows rather than isolated tools.
Teradata is introducing new execution layers to its Tera workspace aimed at reducing the runaway inference costs of AI agents. While benchmarks show significant savings, experts warn that pruning agent steps requires careful human oversight and may lock enterprises into specific platforms.
As enterprise AI adoption accelerates, IT leaders are spending significantly more time managing risk than building value. A new analysis reveals that governance has become an unmanageable chore, driven by shadow AI and the rise of autonomous agents.
As enterprises scale AI agents, Teradata introduces a new execution layer designed to cut inference costs by preventing redundant model calls. But this efficiency comes with trade-offs in accuracy and vendor lock-in that CIOs must weigh carefully.
AI adoption is failing in many organizations because it is bolted onto fragmented processes. Experts argue that true productivity gains require a fundamental shift from functional silos to cross-functional product teams, treating security as an accelerator and embedding AI into core architecture.
NVIDIA’s latest ROS release brings AI agents into robot development and adds support for new platforms. Here’s what business leaders need to know before investing.
Enterprise AI creates a new security problem: the most valuable data is also the most sensitive, and it has to be decrypted while being processed. Here is what confidential computing changes, and what to check before you act.
NVIDIA has released AIPerf, a successor to GenAI-Perf, for benchmarking LLM inference at scale. Here’s what business leaders need to know about measuring AI performance before deployment.
Profitable public companies are cutting thousands of jobs to fund AI investments, and the cultural leadership of corporate America is shifting. Here is what changed, what it means for your business, and what to verify before you act.
A recent InfoWorld article argues that the bottleneck for reliable AI agents isn’t model capability—it’s the state of your data. Here’s what that means for business leaders.
The cheap-token era is over. Drawing on a new Unite.AI article by Deque CTO Dylan Barrell, we look at why AI must now justify itself with measurable outcomes—and how digital accessibility offers a discipline for using AI strategically, not reflexively.
NVIDIA’s TensorRT Edge-LLM ran an agentic benchmark on a single Jetson AGX Thor board in 24 minutes and 36 seconds, versus 2 hours 37 minutes for the llama.cpp reference. Here is what that gap means for teams deciding where agents should run — and what to verify before you commit.
Michael Rangel spent nine years building Novo into a fintech serving small businesses. His account of where AI helps — and where it doesn’t — is a useful checklist for any business owner deciding what to do next.
GitHub retired flat-rate Copilot credits in June 2026, and the first invoices under usage-based billing caught some agentic users off guard. The episode points to a broader problem: most organizations still don’t know what their AI actually costs to run.
Cheap tokens did not make agents cheap, and that gap is now the whole market. Through the first half of 2026, model prices kept falling while […]
Giving a model the ability to act changes your threat model. Prompt injection, excessive agency, inter-agent trust — and the architectural choices that shrink the damage.
Chatbot, workflow or agent? A plain-English guide to what an AI employee actually is, what it is made of, and how to tell a real deployment from a demo.
Refer a business to Faida Workforce and you earn 30% of the client’s first paid month, then 10% of every month for up to 24 months per client — rising to 15% from the accrual after you pass 10 active clients. Paid by PayPal, batched on the 1st.
10 Pro clients = $2,598.70 over 24 months — $3,748.13 at the 15% tier.
Figures are potential earnings worked from the published rates, assuming every referred client pays for the whole cap — not a promise, not an average. The $1 trial is never commissionable.
