AI coding token costs are on track to rival human payroll

AI agents are moving beyond individual productivity and into government operations.  According to Gartner, at least 80% of governments will deploy AI agents to automate routine decision-making, enhancing efficiency and service delivery by 2028. Because of this, government leaders are challenged to determine where they can deliver value with AI agents while maintaining public trust. Their [...] The post AI agents are reshaping government. What should leaders know? appeared first on SAS Blogs.

Tech industry experts are urging IT decision-makers to be wary of AI vendor gimmicks such as free tokens, and to adopt a multi-vendor and multi-model strategy to avoid vendor lock-in. “Don’t be afraid to adopt a multi-vendor approach to get value from different AI tools rather than risk lock-in with a single one,” said Max Goss, senior director analyst at Gartner. It is unlikely one AI vendor or model will meet an organization’s requirements, Goss said. The advice comes as more AI vendors are offering cheap tokens subsidized by venture capital in a land grab for customers. The companies are also hiring forward-deployed engineers (FDEs) to push their models to enterprises. Once companies start developing business processes around specific AI models, they get locked into their ecosystem. “People are adopting hybrid strategies…to cut token costs, and adopting more token-efficient models,” said Jack Gold, principal analyst at J. Gold Associates. Free and low-cost tokens from AI vendors cou

The next wave of software will be AI-native, industry-specific platforms, writes guest author Richard de Silva who believes the biggest winners will be vertical AI companies with deep domain expertise, proprietary data and strong customer relationships, as these advantages create durable competitive moats that generic horizontal SaaS products cannot match.

Gartner 565 TWh forecast and a FERC grid order put GE Vernova and Vertiv at the center of AI data‑center constraints. Compare roles, catalysts, and risks.

It is tempting to date cloud computing from the launch of Amazon S3 in 2006 and the rise of infrastructure as a service (IaaS) that followed. That was certainly the moment the market changed in a visible, irreversible way. But the truth is that cloud began earlier, in the 1990s, when software as a service (SaaS), application hosting, managed services providers, and various forms of remote subscription computing started to reshape how enterprises thought about owning and operating technology. Even then, the core value proposition was familiar: Let someone else run the infrastructure, abstract the complexity, deliver capability as a service, and allow the business to consume only what it needs. What AWS changed was the scale, accessibility, and precision of the execution. Amazon turned infrastructure into a programmable utility. It made compute and storage available in ways that were elastic, self-service, API-driven, and globally reachable. That was the breakthrough. Enterprises had out

With its latest AI tools and products, the vendor showed it is listening to its customer base even while it fails to offer novelty in the market.

Because AI and LLMs are reshaping the traditional SaaS model, founders are forced to focus less on software alone and more on delivering measurable business outcomes, defensible workflow ownership, strong retention, and efficient growth. Crunchbase guest author, Ivan Nikkhoo argues that rather than chasing trends like adding services, founders should build deep moats, understand customer workflows, adapt pricing toward usage- or outcome-based models, and prove that AI creates lasting value.

Tive's innovative single-use 2G tracker slashes costs, fueling growth to a $545 million valuation. The post Krenar Komoni: Tive reaches $100 million annual run rate, the power of a SaaS plus model for data moats, and the impact of real-time tracking on logistics | SaaS Interviews appeared first on Crypto Briefing.