As AI accelerates the automation of routine digital work, a fast‑circulating Samaa TV piece argues that five formerly reliable technical skills — data entry, basic graphic design, basic web development, social media management, and simple video editing — no longer deliver the career returns they once did and should be deprioritized in 2025.
The Samaa TV list is shorthand for a broader labour‑market signal: tasks that are predictable, repeatable, and rules‑based are the most vulnerable to automation, while skills that require judgment, domain knowledge, or human‑facing stewardship retain value. The original write‑up frames the change as a pivot point — not a moral condemnation of people who built careers on these tasks, but a practical nudge to reallocate learning time toward higher‑leverage technical and human‑AI collaboration skills.
That pattern is backed by major industry and economic studies. McKinsey’s modeling of automation shows that while relatively few entire occupations are fully automatable, a large share of the activities within occupations—especially data collection and repetitive processing—are susceptible to automation, and millions of workers will likely need to shift the mix of tasks they perform or re‑skill by 2030. This is not an immediate death sentence for whole professions, but it is a durable signal about which activities are getting cheaper and faster when delegated to software.
McKinsey’s workforce research complements this vendor view by showing that data collection and processing activities are among the most automatable categories across sectors — which helps explain the declining market value of pure data‑entry skills.
Automation is redistributing the value of work, not eliminating the need for skilled people. Those who adapt — building AI literacy, governance chops, domain expertise, and collaborative workflows — will capture the premium in a world where machines handle the predictable and humans handle the consequential.
Key references in the reporting above include the original Samaa TV piece and contemporary vendor and industry reporting that document both the capabilities of automation tools (UiPath, Canva, Adobe, Webflow, Hootsuite) and the macro evidence that many routine activities are now highly automatable.
Conclusion: stop treating rote execution as the endgame; start designing, supervising, and governing the automated systems that will do the execution.
Source: samaa tv 5 technical skills you should stop learning in 2025
Background
The Samaa TV list is shorthand for a broader labour‑market signal: tasks that are predictable, repeatable, and rules‑based are the most vulnerable to automation, while skills that require judgment, domain knowledge, or human‑facing stewardship retain value. The original write‑up frames the change as a pivot point — not a moral condemnation of people who built careers on these tasks, but a practical nudge to reallocate learning time toward higher‑leverage technical and human‑AI collaboration skills.That pattern is backed by major industry and economic studies. McKinsey’s modeling of automation shows that while relatively few entire occupations are fully automatable, a large share of the activities within occupations—especially data collection and repetitive processing—are susceptible to automation, and millions of workers will likely need to shift the mix of tasks they perform or re‑skill by 2030. This is not an immediate death sentence for whole professions, but it is a durable signal about which activities are getting cheaper and faster when delegated to software.
Quick summary of the five skills Samaa TV flags
- Data entry: routine transcription and manual keystrokes are increasingly replaced by OCR, document‑understanding models, and RPA platforms.
- Basic graphic design: template engines and generative image tools now produce logos, social posts, and thumbnails quickly.
- Basic web development (HTML/CSS-only): no‑code and low‑code builders let non‑developers assemble production sites.
- Social media management (captioning/scheduling): AI copilots can create, optimize, and schedule posts, reducing the value of manual posting.
- Simple video editing (trim/color/caption): AI editing and text‑to‑video tools automate many routine editing workflows.
1. Data entry — why the task is fungible, and what really matters now
Why data entry is being automated
Modern enterprise platforms combine Optical Character Recognition (OCR), document understanding, and workflow automation to ingest and route data without manual keying. UiPath — a market leader in RPA and Intelligent Document Processing — explicitly positions “Document Understanding” as the replacement for manual document work, offering OCR engines, pre‑trained extraction models, human‑in‑the‑loop validation steps, and end‑to‑end automation for invoices, forms, and unstructured content. UiPath’s product literature and case studies document large reductions in manual processing time and human headcount needed for high‑volume document tasks.McKinsey’s workforce research complements this vendor view by showing that data collection and processing activities are among the most automatable categories across sectors — which helps explain the declining market value of pure data‑entry skills.
What to learn instead
- AI data management (annotation strategy, taxonomy design, quality control).
- Workflow automation design (RPA/IDP orchestration, connectors, exception handling).
- Data governance and lineage (privacy, retention, compliance oversight).
Caveats and risks
- In some regulated environments (medical records, certain government processes) human verification and domain‑specific handling remain essential for legal and compliance reasons. The job market may still pay a premium for people who combine domain knowledge with manual handling skills.
- Automation projects fail when governance, change management, and exception handling are neglected. Knowing how to mitigate those failure modes is valuable.
2. Basic graphic design — the mechanical work is fading, but creative judgment remains scarce
The automation reality
Generative design tools and template platforms now produce brand assets, posts, and thumbnails in minutes. Canva’s Magic Studio and its acquisitions/expansions into generative image/video are explicitly aimed at making design accessible to non‑designers, and Adobe’s Firefly ecosystem has moved full throttle into generative image and video features across mobile and desktop apps. News coverage and vendor releases over the last 18 months show these platforms improving quality, expanding styles, and bundling brand‑consistent templates and “brand kits.” The net effect: routine layout, background removal, and quick social creatives are far cheaper to produce.Where human designers keep an edge
- Creative direction and visual strategy (brand voice, narrative, cross‑channel consistency).
- UX/UI design and interaction design (where user testing, accessibility, and interaction patterns matter).
- Motion design and interactive prototyping (higher technical barrier, more bespoke craft).
Practical pivots
- Learn design systems and governance: how to define brand tokens, tone, and accessibility rules so generative outputs stay on‑brand.
- Upskill in UI/UX research and prototypes — these translate to higher job resilience.
Unverifiable / cautionary note
- Claims that “Photoshop is dead” are hyperbole; advanced compositing, color grading for high‑stakes campaigns, and designer‑to‑developer handoff remain human‑heavy tasks. Treat blanket obituaries for tools as rhetorical, not literal.
3. Basic web development — no‑code is real, but so is complex engineering
What no‑code/low‑code platforms can do
Platforms such as Webflow, Bubble, and Framer empower non‑developers to create production websites, landing pages, and even e‑commerce experiences with visual editors, CMS collections, and hosted infrastructure. Webflow’s positioning — pixel‑perfect visual design that outputs clean HTML/CSS and built‑in CMS — exemplifies how the lowest‑hanging front‑end work is democratized. Reviews and platform docs show that many small business and agency use cases no longer demand hand‑coded HTML/CSS skill as an entry barrier.What still requires developers
- Backend integrations, API orchestration, authentication flows, complex business logic, performance optimization, and site reliability engineering aren’t solved by templates.
- Enterprise‑grade systems need CI/CD, observability, scaling, and security — all engineering domains.
What learners should prioritize
- Move from “how to hand‑code a static page” to platform integration, API orchestration, and DevOps for web apps.
- Learn how to design extensible architectures that combine no‑code front ends with robust back‑end services.
Balanced takeaway
Basic HTML/CSS remains useful as literacy, but the marketplace now pays for individuals who can connect systems, optimize performance, and secure scale — not for people who can only build static landing pages.4. Social media management — automation for execution, humans for strategy
How automation changes the role
Social tools have layered AI into caption generation, trend detection, scheduling, and analytics. Hootsuite’s OwlyWriter (now marketed as an AI content assistant) and similar features in major scheduling tools can generate caption variants, suggest hashtags, and recommend posting times. These systems reduce the manual time required to produce and schedule posts while increasing throughput for small teams.Where strategy still wins
- Community management and moderation (nuanced interpersonal interactions, conflict resolution).
- Campaign strategy and growth experiments (A/B testing frameworks, funnel design, cohort analysis).
- Brand safety and compliance (legal/regulatory sensitivity in certain industries).
How social media specialists should retool
- Learn basic analytics and experimentation design: translating engagement signals into product or content decisions.
- Become adept at AI‑assisted content strategy — not just generating captions but orchestrating sequences, briefs, and measurement frameworks.
5. Simple video editing — rapid automation of routine cuts, but storytelling is still human
Tooling and capabilities
Generative and AI‑assisted video tools (Runway, Descript, Pika Labs, and others) now offer automatic trimming, transcription‑driven editing, background replacement, inpainting, and even text‑to‑video generation. Vendor pages and creator roundups list these features as mainstream for short‑form content production and rapid repurposing of long‑form footage into bite‑sized clips.Why the “edit by AI” story is partial
- Automated cuts, captions, and stabilisation compress production time dramatically, but narrative structure, pacing, archival research, and emotional arc are still artisanal skills. High‑value editing for brand films, documentaries, or commercials retains human editors.
- Quality control — spotting hallucinated frames, audio artifacts, or content policy issues — requires human oversight.
Where to invest learning hours
- Advanced editorial craft (storyboarding, rhythm, sound design).
- Toolchain orchestration (agent workflows, version control for assets, supervising generative frames).
- AI‑assisted post‑production (supervising generative frames, quality assurance, visual effects pipelines).
What to learn instead (a practical, prioritized roadmap)
If the goal is to maximize employability in 2025 and beyond, the following cluster of skills gives the best ROI and resilience:- AI literacy and prompt design — understand model capabilities, failure modes, and safety/verification steps.
- Human‑in‑the‑loop systems design — build processes where AI produces suggestions and humans validate outcomes.
- Automation orchestration & MLOps — pipelines, CI/CD for models, and operational monitoring of model performance.
- Cybersecurity & data governance — as automation expands, protecting the data ingest and inference pipelines is critical.
- Data storytelling & visualization — turning automated outputs into decisions through dashboards and narratives.
- Domain expertise + AI — industry nuance (healthcare, finance, legal) that prevents naïve automation mistakes.
- Build basic AI literacy (2–6 weeks): practice prompts, evaluate outputs.
- Add a technical compliment (3–6 months): an MLOps or automation course, plus a demonstrable project.
- Grow domain depth (6–12 months): apply tools to a vertical and create a portfolio piece.
- Master human‑AI orchestration (ongoing): build governance artefacts, monitoring, and audit trails.
Critical analysis — strengths of the “stop learning” argument
- Clarity and time economics: The advice forces learners to ask what training will pay off, and discourages sunk‑cost learning in tasks that can be scheduled away by affordable tooling. The Samaa list is a useful heuristic for reallocating scarce learning hours.
- Market alignment: Vendors and platform roadmaps show enterprise adoption of copilots, IDP, and no‑code builders; employers increasingly seek people who can operate, govern, and augment these platforms.
- Practical pivots: The list doesn’t just say “stop”; it suggests alternative, higher‑value skills (AI literacy, cybersecurity, storytelling) that are defensible in a more automated workplace.
Risks, blind spots, and why nuance matters
- Overgeneralization: Not every instance of “basic web dev” or “data entry” is identical. Niche, regulated, or legacy systems still demand manual expertise. A blanket “stop learning” can mislead people who work in industries where manual control is mandatory.
- Access & equity: Pivoting to AI‑adjacent skills assumes access to training, time, and networks. Without employer support or public programs, many workers may be unable to reskill. Public policy and corporate L&D funding matter.
- Quality erosion from automation: High throughput isn’t the same as quality. Generative tools create volume, but without human editorial standards, brands and products can suffer from “workslop” — low‑quality, high‑volume outputs that erode trust. Recent platform rollouts also show quality and governance problems when automation is overapplied.
- Vendor lock‑in and governance risk: Leaning on a single vendor’s ecosystem can create operational lock‑in and compliance surprises; multi‑vendor competence and governance literacy are essential mitigations.
Concrete next steps for technologists, students, and L&D teams
- For individual learners:
- Build one demonstrable artifact: an “AI + workflow” proof‑of‑concept that shows how automation reduces error rates or decision latency.
- Learn one orchestration platform (e.g., UiPath for IDP, Webflow for no‑code flows, or a basic MLOps pipeline) and document it in a short case study.
- For educators and employers:
- Fund micro‑learning tied to real work deliverables; require governance artefacts (prompt libraries, verification checklists) as part of assessments.
- Reframe role progression: reward people who can operationalize AI safely, not only who can execute manual work faster.
- For hiring managers:
- Rework job descriptions away from “must be able to do manual task X” toward “must be able to design/verify/monitor automation for X.”
- Test for judgement, experiment design, and governance awareness, not only tool proficiency.
Final verdict
The Samaa TV list is a sharp, serviceable heuristic: in 2025, spending months mastering narrow, repetitive digital tasks will often yield diminishing returns. But the correct takeaway is not to abandon technical learning; it is to upgrade the kind of technical learning you pursue. Move from being a “doer” of mechanized tasks to being an orchestrator of systems, a steward of quality and ethics, and a partner to AI.Automation is redistributing the value of work, not eliminating the need for skilled people. Those who adapt — building AI literacy, governance chops, domain expertise, and collaborative workflows — will capture the premium in a world where machines handle the predictable and humans handle the consequential.
Key references in the reporting above include the original Samaa TV piece and contemporary vendor and industry reporting that document both the capabilities of automation tools (UiPath, Canva, Adobe, Webflow, Hootsuite) and the macro evidence that many routine activities are now highly automatable.
Conclusion: stop treating rote execution as the endgame; start designing, supervising, and governing the automated systems that will do the execution.
Source: samaa tv 5 technical skills you should stop learning in 2025
