GoKawiil
Tech News
A new report finds that just 7% of companies are extracting measurable business value from their AI investments, despite widespread adoption efforts across industries. The research identifies specific practices that separate these high-performing organizations from the majority still struggling to translate AI spending into results.
While many tech firms cite AI code-generation as reason to cut entry-level hiring, one company says it is increasing its junior engineer headcount instead. The company argues that AI tools handle routine coding tasks but cannot substitute for the on-the-job learning that turns junior developers into engineers with real judgment and intuition.
Grindr's CEO George Arison has outlined plans to rebuild the dating app around artificial intelligence, aiming to make it 'AI native' throughout. The vision includes AI-driven matching to suggest potential partners and AI tools that help prioritize which incoming messages users should see first.
Arm has launched the Mali G2-Ultra NX, described as its first AI-native Mali GPU, which embeds dedicated neural accelerators directly into shader cores alongside a new execution engine and third-generation ray tracing hardware. The design lets neural graphics workloads run in parallel with traditional graphics and compute tasks, sharing memory and cache resources to cut data movement and power use while aiming for desktop-quality visuals on phones.
Forward-deployed engineering, where vendor engineers embed with customers to wire AI products into real operating environments, has become a core sales and delivery model in enterprise AI. The piece argues the real test of this approach is whether repeated engagements shrink into pure services work or actually feed back into a more capable, reusable product over time.
Anthropic released a framework restructuring the software development lifecycle around AI coding agents, arguing that since agents made writing code fast, the bottleneck has shifted to planning, review, and verification. The playbook defines six stages—planning, design, build, deploy, and self-checking—each producing a document like intent.md, spec.md, or plan.md that feeds the next stage, with hooks preventing agents from gaming tests and CI evals treating agent configuration itself as software.
An Entrepreneur contributor argues most companies claiming to be 'AI companies' have simply bought licenses and run pilots, not restructured how they operate. The piece defines true AI-native organizations as ones built on the assumption that intelligence is cheap and abundant, where workflows, staffing and measurement would collapse without the models—unlike adopters, who could remove AI tools and keep functioning, just slower.