1. Why Multimodal AI Is Different From Past Technology Transitions

Whereas past automation technologies were only repetitive tasks—multimodal AI can consolidate multiple skills at one time. In short, one AI application can:

• Read a research paper, abstract it, and create an infographic.
• Write a news story, read an audio report, and produce related visuals.
• Help a teacher develop lesson plans, as well as adjust content to meet the individual student’s learning style.

This ability to bridge disciplines is the key to multimodal AI being the industry-disruptor that it is, especially for those who wear “many hats” on the job.

2. Education: Lecturers to Learning Designers

Teachers are not just knowledges-educators-teasers, motivators, and planners of curriculum. Multimodal AI can help by:

• Having quizzes, slides, or interactive simulations create automatically.
• Creating personalized learning paths for students.
• Transferring lessons to other media (text, video, audio) as learning demands differ.

But the human face of learning—motivation, empathy, emotional connection—is something that is still uniquely human. Educators will transition from hours of prep time to more time working directly with students.

3. Design: From Technical Execution to Creative Direction

Graphic designers, product designers, and architects will likely contend with technical proficiency (computer skills) and creativity. Multimodal AI is already capable of developing drafts, prototypes, and design alternatives in seconds. This means:

• Designers might likely spend fewer hours on technical realization and more hours on curation, refining, and setting direction.
• The job can become more of a creative director role, where the directing of the AI and the creation of its output is the focus.

Or, freshman design work on iterative production declines.

4. Journalism: From Reporting to Storytelling

Journalism involves research, writing, interviewing, and storytelling in a variety of forms. Multimodal AI can:

• Analyze large data sets for patterns.
• Write articles or even create multimedia packages.
• Develop personalized news experiences (text + podcast + short video clip).

The caveat: Trust, journalistic judgment, and the power to hold powers that be accountable are as important in journalism as AI can rapidly analyze. Journalists will need to think more as investigation, ethics, and contextual reporting—area where human judgment can’t be duplicated.

5. The Bigger Picture: Redefinition, Not Replacement

Rather than displacing all such positions, multimodal AI will likely redefine them within the context of higher-order human abilities:

• Empathy and people-skilling for teachers.
• Vision and taste for artists.
• Ethics and fact-finding for journalists.

But that first-in-line photograph can change overnight. Work that at one time instructed beginners—like trimming articles to size, creating first draft pages, or building lesson plans—will be computer-assigned. This raises the risk of an empty middle, where low-level jobs shrink, and it is harder for people to upgrade to higher-level work.

6. Preparing for the Change

Experts in these fields may have to:

• Learn to collaborate with AI, but not battle with it.
• Highlight distinctly human skills—empathy, ethics, imagination, and people skills.
• Reengineer functions so AI handles volume and velocity, but humans add depth and context.

Final Thought

Multimodal AI will not displace work like teaching, design, or journalism, but it will change their nature. Instead of spending time on tedious work, the experts may be nearer to the heart of their work: inspiring, designing, and informing in human abundance. The transformation can be painful, but if done with care, it can create space for humans to do more of what they cannot be replaced by.