From “reading” to “seeing” with Google Gemini 3: Why the leap to multimodal AI overshadows everything that has come before.

AI's eyes are opening: How native multimodality is redefining business and society

For a long time, artificial intelligence was blind to the world as we experience it. To understand videos or interpret audio signals, it had to take detours via text – a process that was slow, expensive, and error-prone. But this era is now ending. With the advent of native multimodal systems, spearheaded by innovations like Google's Gemini 3, a technological quantum leap is taking place: The machine is no longer just learning to read; it is learning to see, hear, and grasp complex relationships in real time.

This article explores the profound transformation of business intelligence that goes far beyond mere technical gimmicks. We analyze how the direct processing of image and audio data enables productivity gains of up to 35 percent and why the drastically decreasing costs of this technology represent a democratization of innovation, especially for small and medium-sized enterprises.

But there are two sides to every coin. While industry – from German mechanical engineering to the global creative sector – is on the cusp of a golden age of efficiency, the new capabilities of AI raise pressing questions: What does it mean for privacy in the workplace when software not only records words but also analyzes facial expressions, gestures, and emotional states? How will job profiles change when AI systems can suddenly understand context and make complex judgments?

Dive into a comprehensive analysis that ranges from the macroeconomic impact on global GDP and the disruption of the film industry to the ethical pitfalls of emotion-based surveillance. Learn why the future of work lies not in competing with machines, but in a new form of "superagency"—and why German companies must act now to avoid falling behind.

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The revolution of business intelligence through native multimodal artificial intelligence

The technological landscape of artificial intelligence is currently undergoing a fundamental transformation, the economic implications of which are only beginning to become apparent. With the introduction of Gemini 3 by Google, a paradigm shift is manifesting itself, redefining from the ground up how companies handle information. The central innovation lies not in a gradual improvement of existing systems, but in a conceptual leap: videos, audio files, and images are no longer treated as problematic cases that first need to be converted into text, but are understood as equal data sources that can be analyzed in their original form.

This development marks the end of a decades-long limitation. Until now, organizations had to expend considerable resources converting visual and audio information into text-based formats before it could be systematically analyzed. Transcription services, manual video evaluations, and the fragmentation of multimedia content into isolated components were standard information processing practices. Gemini 3 eliminates these intermediate steps, unlocking efficiency potential that extends far beyond mere time savings.

Native multimodal processing represents a qualitative difference compared to previous approaches. While earlier systems had to first convert different data types into a common format, Gemini 3 understands the inherent context and relationships between visual, auditory, and textual information directly. The system not only analyzes spoken words but also captures facial expressions, body language, tone of voice, and the synchronization of these signals. This ability for holistic interpretation closely corresponds to human perception and opens up new dimensions of data analysis.

The economic dimensions of the multimodal revolution

The economic implications of this technology manifest themselves on several levels. The global market for multimodal artificial intelligence, which was valued at approximately US$1.35 to US$1.73 billion in 2024, is projected to reach US$5.6 to US$10.89 billion by 2030. These forecasts imply annual growth rates of between 32.9 and 36.8 percent, signaling one of the most dynamic developments in the entire technology sector. However, these figures reflect only a fraction of the true economic significance, as the indirect effects of productivity gains and new business models are not fully captured in these estimates.

The productivity gains companies achieve by using Gemini 3 are documented to be between 25 and 35 percent in AI-powered workflows. An Australian retail company reduced the time spent on weekly sales reports from eight hours to one hour by having the system automatically aggregate data from three different systems, identify trends, and generate two-page reports with key insights. A Brazilian marketing agency uses the multimodal capabilities to automatically generate campaign content from product images, sales data, and customer feedback. The time saved allows the team to handle more projects simultaneously without having to hire additional staff.

These economies of scale prove particularly relevant for growing companies that need to expand their capacities but face recruitment costs and a shortage of skilled workers. The ability to handle a higher workload with existing resources is fundamentally changing the economics of corporate growth. Traditionally, every expansion had to be paid for with proportional cost increases. Multimodal AI systems break this cycle, enabling disproportionate productivity gains without corresponding increases in staff.

Macroeconomic projections for the impact of artificial intelligence on gross domestic product (GDP) are substantial. Estimates predict a GDP increase of 1.5 percent by 2035, nearly 3 percent by 2055, and 3.7 percent by 2075. The contribution to the annual productivity growth rate will peak in the early 2030s, reaching 0.2 percentage points in 2032. Goldman Sachs forecasts that generative AI alone could boost global GDP by nearly 7 percent over the next decade, with the United States expected to be the biggest beneficiary. Annual productivity growth could increase by 1.5 percent over a ten-year period.

Approximately 40 percent of current GDP could be substantially impacted by generative AI. Occupations around the 80th percentile of the income distribution have the highest exposure, with roughly half of their work being amenable to AI automation on average. The highest income groups are less exposed, and the lowest least. This differentiated impact has significant implications for income distribution and social inequality.

Sectoral shifts during the AI ​​transition generate lasting structural effects. Sectors with higher AI exposure grow faster than the rest of the economy, and these sectors tend to exhibit faster trend productivity growth. The resulting structural change permanently increases aggregate growth by about 0.04 percentage points, even after the adoption wave is complete. This permanent shift in the level makes the economy permanently larger without further increasing the long-term growth rate after the transition is finished.

The cost revolution in AI-supported data processing

Gemini 3's pricing signals an aggressive market penetration strategy that democratizes access to advanced AI capabilities. The Flash version of Gemini 3 achieves speeds of over 640 tokens per second at drastically reduced costs of $0.15 for inputs and $3.50 for outputs with reasoning mode enabled. In contrast, human transcription costs between $60 and $90 per hour, while AI transcription costs between $9 and $15 per hour. This price difference reflects fundamentally different processes: AI processes audio in real time using computational infrastructure with minimal marginal costs, whereas human transcribers require 4 to 6 hours of labor per hour of audio, in addition to quality assurance.

Google has reduced the prices for Gemini 1.5 Pro by 64 percent for input tokens, 52 percent for output tokens, and 64 percent for incremental contexts. Combined with context caching, this results in continuous cost reductions for developers. Increasing the rate limits for paid Tier users to 2,000 requests per minute for 1.5 Flash and 1,000 for 1.5 Pro significantly facilitates application scaling.

This price development democratizes access to advanced AI capabilities for small and medium-sized enterprises (SMEs) that previously could not afford expensive premium models. The macroeconomic effect of this price reduction is substantial. When AI capabilities that were reserved for large corporations two years ago become available at a fraction of the cost, the barriers to entry for AI-driven innovation fall dramatically.

Return-on-investment calculations for Gemini implementations must consider several factors. Direct token cost savings through lower API prices are the most obvious, but the indirect effects often outweigh them. Productivity gains from faster iteration shorten development cycles and accelerate the time to market for new products. Reduced error correction time due to higher model accuracy lowers quality assurance costs. Competitive advantages from early adoption can secure market share before competitors catch up.

High-volume processing workflows that handle millions of documents or thousands of API requests daily benefit most from the speed improvements. A 2x acceleration means the same infrastructure can handle twice the throughput, or alternatively, infrastructure costs can be halved. For fintech companies that perform real-time credit assessments or e-commerce platforms that personalize product recommendations, these efficiency gains add up to significant competitive advantages.

The fundamental shift from transcription to native understanding

The qualitative difference between transcription and native multimodal understanding manifests itself in the depth of extractable information. Traditional approaches to video analysis followed a multi-stage process: First, the audio file was transcribed, then the visual elements were described separately, and finally, both strands of information were manually correlated. This process was not only time-consuming but inevitably led to information loss. Subtle visual cues, the meaning of nonverbal communication, or the temporal synchronization between spoken words and visual events were lost or inadequately captured.

Gemini 3 captures these contextual levels simultaneously and in an integrated manner. The system not only recognizes that a person is speaking, but also interprets their posture, gestures, and facial expressions in relation to the spoken content. This holistic analysis enables insights that would never be accessible from isolated transcripts. A sales conversation can not only be searched for spoken objections, but the system also identifies moments of hesitation, signs of interest, or skepticism in the conversation partner's body language.

The use cases span numerous industries. In the financial sector, Gemini Enterprise enables the automation of complex analytical processes. Banks can achieve efficiency gains of fifteen percentage points through doubled customer retention rates, a thirty percent increase in lead conversion, fifty percent productivity gains, and the relocation of half their staff to higher-value tasks by automating middle-office activities. AI-powered fraud detection, risk assessment, and compliance monitoring reduce operational risks while simultaneously lowering costs.

In retail, companies analyze customer behavior in stores, monitor shelf space utilization and product placement across multiple locations using video analytics. Visual merchandising compliance is automatically verified by comparing display setups against specifications. In healthcare, emotion recognition and sentiment analysis capabilities enable patient monitoring and therapy analysis. Manufacturing utilizes real-time quality control automation, assembly line monitoring, and safety compliance verification.

The labor market policy implications of multimodal disruption

The integration of multimodal AI into work environments is catalyzing fundamental shifts in the labor market structure. Historically, technological disruptions primarily affected manual or low-skilled jobs. Generative AI and multimodal systems are breaking this pattern by increasingly addressing cognitive and creative tasks that were previously considered the domain of highly skilled professionals. Estimates suggest that by 2030, approximately 30 percent of working hours in the US economy could be automated, requiring 12 million job transitions.

The nature of this disruption differs qualitatively from previous waves of automation. While robotics and traditional AI primarily replaced repetitive, rule-based tasks, multimodal AI addresses activities that require contextual understanding, judgment, and the interpretation of complex, ambiguous information. A marketing manager who previously spent hours manually aggregating campaign performance and writing reports now receives automatically generated, data-driven recommendations within minutes. A product manager can review significantly more customer feedback in less time, as the system automatically analyzes videos of customer interviews and extracts the key insights.

Concerns about job losses due to AI are justified, but historical evidence suggests that new technologies create more jobs than they destroy in the long run. The World Economic Forum estimates that by 2025, AI will displace 75 million jobs globally but create 133 million new ones, resulting in a net gain of 58 million jobs. However, this aggregated view masks significant sectoral and regional disparities. The manufacturing industry is likely to experience substantial job losses, while healthcare and education can expect significant job growth.

The speed at which displaced workers are reintegrated into the labor market will be crucial. Modeling shows that almost all scenarios predict full or near-full employment by 2030, provided displaced workers are quickly rehired. The results illustrate the importance of rapidly reintegrating displaced workers. Higher productivity increases employee income, leading to higher economic growth and increased labor demand. At the same time, AI is accelerating the development of new products and services, which will require more workers.

Organizations are required to implement proactive upskilling and reskilling strategies. Currently, approximately 35 percent of the global workforce—over one billion people—requires further training due to AI adoption. Historically, this figure was only 6 percent. Companies must identify the cross-functional skills necessary for effective AI adoption, help employees develop these skills, and provide targeted training and development opportunities.

The skills that will be in demand in the future are shifting significantly towards those that enable human-AI collaboration. Technical skills in data analysis, machine learning, and programming are gaining importance, but creativity, complex problem-solving, emotional intelligence, and the ability to interpret and strategically apply AI-generated insights are becoming equally critical. The future of work requires not competition with AI, but a partnership in which human workers can focus on creativity and strategy.

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The transformation of creative industries through AI video generation

The creative industry is experiencing one of the most dramatic disruptions in its history through AI-generated video generation. The market for AI-generated art has already reached $2.3 billion, with platforms like ArtStation and DeviantArt seeing a 40 percent annual increase in user-generated content. By 2025, over 1.2 million independent creators had used AI tools to monetize their work through platforms such as Patreon, Substack, and AI-powered marketplaces.

The economic opportunities for new market entrants are considerable. The democratization of video production through AI tools is eliminating traditional barriers to entry that were based on high capital requirements for equipment, studios, and specialized personnel. An independent content creator can now produce visually sophisticated videos with minimal investment, videos that compete with traditionally produced content. This disruption follows the classic pattern of disruptive innovation: the technology initially opens up market segments that were economically unattractive to established providers and then works its way up to higher-value segments.

Established production studios face a complex strategic dilemma. On the one hand, AI tools promise substantial cost reductions and efficiency gains. A science fiction film, whose script was rejected by a major studio as unprofitable, was revised using virtual production techniques and realized with a budget reduction of over 40 percent, yet it grossed more than seven times its original budget. The combination of generative AI with all other technologies could contribute between 0.5 and 3.4 percentage points annually to productivity growth through work automation.

On the other hand, there is a fundamental conflict between the pursuit of efficiency and the preservation of creative authenticity. Creative industries are based on art, and any technology should support this art, not attempt to replace the creative process. Generative AI can increase efficiency, but it cannot directly replace human writers, directors, actors, or designers. Attempting to use generative AI to generate script drafts, which are then edited by creatives, meets with active resistance from the artists who infuse these processes with emotion and innovation. The risk of alienating the very people on whom the business is built is considerable.

The optimal strategy for production studios lies in focusing on efficiency gains in production and post-production, while keeping the creative process at the forefront. Virtual production techniques, AI-powered visual effects, and automated post-production can cut production times by months and reduce budgets by 20 percent or more. The key is to produce more usable minutes per shooting day and complete half of the visual effects in pre-production without compromising the creative vision.

The long-term implications for the structure of the creative industries are profound. The traditional production process, which required high fixed costs and specialized expertise, created natural oligopolies and barriers to market entry. Democratization through AI tools is fragmenting this structure. The number of independent creative professionals capable of producing high-quality content is increasing exponentially. This intensifies competitive pressure on established studios but also creates new opportunities for innovative business models that combine AI-powered production with curated distribution and marketing capabilities.

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Data protection and ethical challenges of multimodal surveillance

The ability of multimodal AI systems to simultaneously process and interpret visual, auditory, and textual information opens up application possibilities that raise significant data privacy and ethical concerns. Real-time analysis of body language, facial expressions, and tone of voice allows for inferences about emotional states, sincerity, and intentions that go far beyond what can be derived from spoken words alone. These capabilities are already being used in job interviews, employee monitoring, and customer behavior analysis.

Over fifty percent of large employers in the United States use emotion-tracking AI to monitor employees' internal states, a practice that has increased significantly during the COVID-19 pandemic. Companies like Unilever employ AI-powered video interviews where algorithms analyze facial expressions to assess honesty and emotion. The software, provided by companies like HireVue, identifies supposedly the best candidates and provides human recruiters with notes on what the AI ​​observed in each candidate.

The potential benefits of these approaches include convenience for both sides, as candidates can complete interviews at any time and recruiters can review them according to their own schedule. Unilever claims that this new approach has contributed to ethnic diversity, with a significant increase in hiring non-white candidates. The elimination of unconscious bias in human recruiters through properly trained AI could theoretically lead to fairer hiring processes.

However, the risks and ethical issues are considerable. AI-powered monitoring often operates in the background, leaving many employees unaware that they are being tracked. These systems often lack transparency and explainability, and employees are profoundly affected by their outputs. Furthermore, employers may misuse AI, for example, to exploit bargaining power, manipulate productivity figures, or restructure employment relationships.

Biometric data in AI applications presents significant ethical dilemmas. Facial recognition technology can improve security measures, but often operates without the explicit consent of individuals and leads to unwanted surveillance. If this data is hacked or misused, for example through unauthorized access to personal accounts or the creation of deepfakes, the consequences can be severe. The use of such technologies by law enforcement agencies can lead to significant human rights issues.

Multimodal AI models significantly expand the attack surface for abuse. A report by Enkrypt AI shows that certain models are sixty times more likely to produce texts related to child sexual exploitation material than comparable models such as GPT-4o and Claude 3.7 Sonnet. These models are eighteen to forty times more likely to generate dangerous chemical, biological, radiological, and nuclear information when exposed to adversarial inputs. These risks are not caused by overtly malicious text inputs, but by prompt injections hidden in image files, a technique that effectively bypasses traditional security filters.

The risk mitigation recommendations include integrating red teaming datasets into security alignment processes, continuous automated stress testing, the use of context-aware, multimodal guardrails, and the establishment of real-time monitoring and incident response systems. Furthermore, model risk cards should be created for transparent communication of vulnerabilities.

Regulatory frameworks are lagging significantly behind technological developments. The Dutch data protection authority halted a pilot program by a company that required employees to wear Fitbits for data processing purposes. Similar interventions will increase as the gap between technological capabilities and legal safeguards becomes more apparent. Companies implementing multimodal AI monitoring must develop proactive data protection frameworks that go far beyond minimum compliance requirements.

The challenge lies in harnessing the potential of multimodal AI to improve safety, efficiency, and decision quality without compromising fundamental data privacy rights or creating a climate of constant surveillance that erodes employee trust and autonomy. Successfully navigating this tension requires not only technical solutions but also fundamental organizational discussions about values, transparency, and the limits of acceptable surveillance.

The strategic implications for German industrial companies

The manufacturing intensity of the German economy offers significant potential for AI-supported optimization. Baden-Württemberg combines cutting-edge research with practical applications and demonstrates how the use of AI creates measurable benefits across traditional sectors. Integrating AI into production processes enables German SMEs to maintain their competitiveness against global competition through increased efficiency and quality.

The preference of German companies for on-premises solutions clashes with cloud-based AI services. Gemini via Vertex AI requires cloud adoption, which poses challenges for data-sensitive industries such as pharmaceuticals and automotive. Hybrid architectures that process critical data locally and send only aggregated or anonymized data to the cloud are becoming compromise solutions.

In mechanical engineering and the automotive industry, video analytics powered by multimodal AI enables quality control automation, assembly line monitoring for workflow optimization, and real-time safety compliance verification. Companies can detect product defects and irregularities during the manufacturing process in real time. Tracking worker movements and machine operations identifies bottlenecks and optimizes processes. Verification that workers are following safety protocols and wearing appropriate protective equipment is automated.

The application of gesture recognition in manufacturing is transforming human-machine interaction. Workers can control machines with hand movements, improving efficiency and safety. Audi's Brussels plant is experimenting with gesture-controlled robots that can remotely direct workers. This touchless control eliminates physical switches and buttons, reduces the risk of accidents, and increases operational speed.

The strategic challenge for German companies lies in combining their historical strengths in engineering excellence and manufacturing quality with the capabilities of data-driven AI systems. The typical sequential optimization of production processes, based on experience and incremental improvement, is increasingly being supplemented or replaced by AI systems that learn from continuous data flows and suggest optimizations in real time.

Cultural adaptation to this new reality may pose a greater challenge than technical implementation. German industrial companies are characterized by deep specialist expertise, pronounced hierarchies, and established processes. Integrating AI systems that potentially suggest or make decisions that deviate from traditional expertise requires a cultural shift. Successful adoption will be achieved by those companies that position AI not as a replacement, but as an extension of human expertise.

The future of work in the AI-native economy

The transformation to an AI-native economy does not represent a singular disruption, but rather a continuous process of realignment in which human and machine intelligence are increasingly merging. The speed of this transformation far surpasses that of historical technological upheavals. While electrification took decades to permeate the production landscape, and digitalization spanned two to three decades, AI integration is taking place within just a few years.

The nature of the work is shifting fundamentally from executing clearly defined tasks to orchestrating and supervising AI-supported processes. A marketing manager spends less time manually creating reports and more time interpreting AI-generated insights and making strategic decisions about which recommendations to implement. A product manager focuses less on transcribing and coding customer interviews and more on synthesizing AI-extracted patterns into coherent product strategies.

This shift necessitates new forms of collaboration between humans and AI. The metaphor of AI as a tool, which dominated early discourse, is proving increasingly inadequate. AI systems do not function as passive instruments activated as needed, but rather as continuous collaborators that filter information, suggest options, and make routine decisions. The ability to interact effectively with these systems is becoming a core competency across virtually all professions.

The economic logic of the superagency, where individuals dramatically increase their productivity through AI augmentation, is beginning to manifest. A sole proprietor can now, with the support of AI systems, deliver services that previously required small teams. A consultant can conduct more extensive analyses, produce multilingual content, and handle more complex projects. These productivity gains do not automatically lead to job losses at an aggregate level, but they dramatically shift the demand for different skill sets.

The polarization of the labor market, which has been observed for decades, is likely to intensify. Highly skilled workers who can effectively utilize AI achieve significantly increased productivity and correspondingly higher incomes. Workers in mid-skilled categories, whose tasks are becoming increasingly automatable, are under considerable pressure. Polarization along the dimension of AI complementarity, not just skill level, will become the defining characteristic of the labor market.

The implications for education systems are profound. The traditional focus on factual knowledge and standardized processes loses relevance when AI systems have access to virtually unlimited information and perform routine tasks more efficiently than humans. Education must reorient itself toward developing skills that represent genuine human strengths: complex problem-solving in novel situations, creative synthesis of disparate information, ethical judgment, emotional intelligence, and the ability to collaborate effectively with AI.

The role of politics is to shape this transformation in such a way that its benefits are widely shared and its risks are minimized. This requires massive investments in lifelong learning and retraining, the creation of social safety nets for workers during transitional periods, the promotion of AI access for small and medium-sized enterprises, and regulatory frameworks that enable innovation while protecting fundamental rights.

The overall economic impact of the multimodal AI revolution is positive, albeit with significant distributional effects. Productivity gains are real and substantial. The ability to extract previously inaccessible insights from unstructured multimedia data creates genuine new value. Democratizing access to advanced analytical capabilities lowers market entry barriers and fosters innovation.

At the same time, the speed of this transformation demands proactive planning to prevent short-term disruptions from undermining long-term potential. The history of technological revolutions teaches us that while their net effects are positive, the transition phases can bring significant social upheaval. Societies' ability to manage these transitions will determine whether the multimodal AI revolution leads to widely shared prosperity or exacerbated inequality.

The future of work is neither a dystopia of mass unemployment nor a utopia of effortless prosperity. It is a reality in which the boundaries between human and machine intelligence are increasingly blurred, in which success depends on the ability to understand, manage, and augment AI systems, and in which continuous learning and adaptation are becoming permanent necessities. The organizations and societies that successfully navigate this transformation will be those that not only adopt the technology but also create the fundamental processes, cultures, and institutions that enable people to thrive in this new reality.

Industry focus: B2B, digitalization (from AI to XR), mechanical engineering, logistics, renewable energies and industry

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