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The Future of AI is Unfolding: Understanding OpenAI’s and Google’s Latest Announcements and Their Business Implications

Keeping up with the latest advancements in AI is crucial for driving value from your technology investments and staying competitive in the fast-moving marketplace. Last week, OpenAI and Google not only introduced revolutionary advancements in how AI learns but also signaled the beginning of a new pricing strategy competition. This blog explores these announcements, provides a simplified explanation of the technological shifts, highlights specific business examples of what this multimodal capability makes possible and further examples of what are now financially viable use cases. Finally, I offer strategic recommendations for actions needed to take advantage of these developments. Whether you're a business or public sector leader, understanding these changes can help you navigate the evolving AI market, capitalize on cost reductions, and leverage advanced AI capabilities for your organization's benefit.

Dr. Lisa made in partnership with ChatGPT

What Was Announced

OpenAI and Google introduced a groundbreaking shift in AI learning. Traditionally, AI has learned by processing enormous amounts of text, essentially “reading” vast datasets. The new approach, termed “multi-modality,” allows AI to learn more like humans—by hearing sounds, seeing and touching objects, and understanding their relationships in the physical world. This means that AI can now integrate different types of data to form a more comprehensive understanding of its environment, paving the way for more advanced and intuitive applications.

Beyond the multimodal tech advances, the announcements also signaled the start of an AI pricing war. While consumer offerings are low-cost ($20/mo), the prized targeted revenue source for big tech is the enterprise API per-request fees businesses pay to build custom applications atop proprietary large language models like GPT 4o and Gemini - potentially costing millions per month. As Eric Fraser, Dr Lisa AI CTO, pointed out in his brilliant LinkedIn summary post, this is akin to a per text fee versus the all-you-can-use models that we have today.

These big players aim to lock-in businesses as foundational enterprise LLM customers amid rising competition – once an enterprise is established working with one of these players, it will be painful to change. With the new pricing strategies for AI APIs, businesses can expect to move from paying for each individual use or transaction to more inclusive and cost-effective pricing models. This shift will make it easier and more affordable for organizations to scale their AI applications, leading to broader adoption and more innovative uses of AI technology.

What This Means for Businesses and Public Sector


The recent announcements have far-reaching implications across various industries. By introducing multimodal capabilities and signaling a competitive pricing strategy, these developments promise to transform how organizations leverage AI. In this section, I explore practical examples of how these changes can impact outcomes created with AI. First, we will look at how multimodal AI can enhance capabilities across different sectors, followed by examples illustrating the benefits of reduced AI costs. Together, these examples highlight the potential for improved efficiency, innovation, and cost savings that businesses can achieve given these announcements.

Multimodal Capability Examples

The integration of multimodal AI capabilities allows systems to process and understand diverse types of data—text, voice, images, and sensory inputs—simultaneously. This allows for more intuitive and effective applications. Here are practical examples demonstrating how multimodal AI can transform industries, enhancing efficiency and delivering better outcomes.

  1. Enhanced Customer Support: With multimodal AI, customer support systems can now understand and respond to queries through text, voice, and even visual inputs. For example, a customer can send a photo of a broken product, and the AI can analyze the image to provide troubleshooting steps or initiate a return process.

  2. Advanced Healthcare Diagnostics: Multimodal AI can integrate data from medical images, patient history, and real-time monitoring devices. This comprehensive analysis can lead to more accurate diagnoses and personalized treatment plans, improving patient outcomes and operational efficiency in healthcare settings.

  3. Immersive E-commerce Experiences: Retailers can offer more interactive shopping experiences. For instance, customers can upload images of outfits they like, and the AI can suggest similar items from the retailer’s inventory. Voice-enabled searches and virtual try-ons using augmented reality can further enhance the shopping experience.

  4. Smart Manufacturing: In manufacturing, multimodal AI can combine visual data from cameras, sensory data from equipment, and historical performance data to predict maintenance needs, detect quality issues, and optimize production processes. This leads to reduced downtime and improved product quality.

  5. Real-time Financial Analysis: Financial institutions can leverage multimodal AI to analyze text-based financial news, voice communications, and numerical data simultaneously. This holistic approach allows for better risk assessment, fraud detection, and investment decisions, enhancing overall financial management.

  6. Personalized Learning Experiences: Educational platforms can use multimodal AI to create personalized learning experiences. The AI can analyze text input from assignments, voice interactions during virtual classes, and even facial expressions to gauge student engagement and understanding, providing tailored educational content.

  7. Intelligent Home Automation: Smart home systems can become more intuitive with multimodal AI. They can integrate voice commands, visual inputs from security cameras, and sensory data from various home devices to provide a seamless and intelligent living environment, enhancing security and convenience.

  8. Augmented Reality Applications: Multimodal AI can enhance AR applications by combining visual data, spatial audio, and touch inputs. This can be applied in fields like gaming, remote collaboration, and training simulations, providing more immersive and interactive experiences.

  9. Enhanced Transportation Systems: In the transportation sector, multimodal AI can process data from cameras, LIDAR, and other sensors to improve autonomous vehicle navigation. This comprehensive data integration enhances safety, efficiency, and the ability to operate in complex environments.

  10. Comprehensive Market Research: Marketing and advertising agencies can use multimodal AI to analyze consumer behavior through text reviews, social media posts, voice feedback, and visual content. This deep insight enables the creation of highly targeted and effective marketing strategies, driving better business outcomes.

  11. Emergency Response Coordination: By integrating visual data from surveillance cameras, audio inputs from emergency calls, and sensor data from IoT devices, AI can provide a comprehensive situational awareness. This enables faster and more accurate decision-making during disasters or emergencies, improving response times and potentially saving lives.

  12. Smart City Infrastructure Management: Public sector agencies can use multimodal AI to manage city infrastructure more efficiently. By analyzing data from traffic cameras, environmental sensors, and social media feeds, AI can predict and manage traffic congestion, monitor air quality, and detect infrastructure issues in real-time. This holistic approach leads to better urban planning, improved public services, and enhanced quality of life for citizens.

Cost Reduction Examples

The reduction in AI API fees opens up new opportunities for businesses and public sector organizations to enhance their operations without prohibitive costs. From customer service automation to personalized e-commerce experiences, and from healthcare diagnostics to supply chain optimization, lower AI costs enable broader and more sophisticated use of AI technologies. Here are practical examples demonstrating how these cost reductions can lead to significant efficiency gains, improved outcomes, and operational savings across various sectors.

  1. Customer Service Automation: Companies like banks and telecoms, which use AI to automate customer service, currently incur high costs due to per-use API fees. As prices drop, they can expand AI capabilities, reducing wait times and improving customer satisfaction without prohibitive costs.

  2. E-commerce Personalization: Online retailers deploying AI for personalized shopping experiences will benefit significantly. Lower API fees mean more sophisticated recommendation engines can be implemented, increasing sales and customer retention without escalating costs.

  3. Healthcare Diagnostics: Hospitals and clinics using AI for diagnostics can expect to scale their services. Reduced fees will allow for more widespread deployment of AI diagnostics, improving patient outcomes and operational efficiency.

  4. Manufacturing Process Optimization: Manufacturing firms using AI for predictive maintenance and process optimization will see a reduction in operational costs. With lower API fees, they can integrate more AI-driven sensors and systems to monitor equipment health in real-time, predict failures, and schedule maintenance proactively, reducing downtime and increasing production efficiency.

  5. Financial Services Risk Management: Financial institutions employing AI for risk management and fraud detection will benefit significantly from reduced API costs. This will enable them to implement more advanced AI models to analyze vast amounts of transactional data in real-time, enhancing their ability to detect fraudulent activities and manage risks more effectively.

  6. Supply Chain Optimization: Companies managing complex supply chains can leverage AI to predict demand, optimize inventory, and streamline logistics. Lower API costs will allow these businesses to scale their AI usage, enhancing real-time decision-making, reducing waste, and improving overall supply chain efficiency, leading to cost savings and faster delivery times.

  7. Marketing and Advertising: Advertising agencies and marketing departments that use AI for personalized ad targeting and customer segmentation will benefit from reduced API fees. This will enable them to deploy more sophisticated AI models to analyze consumer behavior and preferences, creating highly targeted campaigns that improve conversion rates and ROI without significantly increasing operational costs.

  8. Retail Inventory Management: Retailers can use AI to better manage their inventory, reducing overstock and stockouts. With lower API fees, more advanced AI systems can be implemented to predict trends and optimize inventory levels in real-time, leading to cost savings and increased sales.

  9. Real Estate Market Analysis: Real estate companies using AI for market analysis and property valuation will find it more affordable to integrate comprehensive AI models. This can lead to more accurate property assessments, better investment decisions, and enhanced customer service through personalized property recommendations.

  10. Energy Management: Energy companies can utilize AI to optimize energy distribution and manage grid operations more efficiently. Lower API fees will enable these companies to implement AI-driven solutions on a larger scale, leading to better energy management, reduced operational costs, and improved sustainability efforts.

  11. Public Healthcare Services: Public healthcare systems can benefit from reduced AI API fees by deploying AI-driven diagnostics and administrative automation. Lower costs make it feasible to implement AI solutions widely, improving patient care through faster diagnostics and reducing administrative burdens on healthcare providers, leading to more efficient operations and cost savings.

  12. Education System Management: School districts and public education departments can use AI to personalize learning and manage administrative tasks more efficiently. Reduced AI costs enable broader implementation of AI tools for grading, student progress tracking, and resource allocation. This can lead to more personalized and effective education while reducing administrative overhead and operational costs.

What You Should DO Now

  • Evaluate Current AI Usage: Conduct an internal audit to understand how your business currently uses AI. Identify areas where multimodal AI could enhance your operations, such as customer service, supply chain management, or marketing.

  • Explore Cost-Saving Opportunities: With the expected decrease in API fees, businesses should re-evaluate their AI-related expenditures. Look for opportunities to negotiate better pricing structures with AI service providers or consider switching to providers offering more competitive rates.

  • Scale AI Initiatives: Use the cost savings from reduced API fees to scale up AI initiatives. This could involve expanding AI-driven automation, increasing the complexity of AI models used, or integrating AI into new areas of the business.

  • Invest in Multimodal AI: Begin investing in multimodal AI technologies. This includes training AI models to process and interpret various types of data (visual, auditory, tactile) to enhance their decision-making capabilities and improve the user experience.

  • Stay Updated on Market Trends: Continuously monitor developments in AI technology and pricing models. Staying informed will help your business adapt quickly to new opportunities and maintain a competitive edge.

  • Collaborate with AI Experts: Partner with AI consultants or firms to help navigate the transition to multimodal AI and make the most of the new pricing structures. Expert guidance can ensure that your AI investments deliver maximum value.

  • Focus on Upskilling: Invest in training your staff to work effectively with new AI tools and technologies. This includes upskilling your IT team to manage and optimize AI systems and educating other departments on leveraging AI for better decision-making.

By taking these proactive steps, businesses and public sector organizations can maximize the benefits of the recent advancements in AI and position themselves for future growth and innovation.



The advancements announced by OpenAI and Google mark a pivotal shift in AI technology. The introduction of multimodal capabilities enhances AI's ability to process and understand diverse data types, making it more human-like in interactions. Additionally, the anticipated price war will make these advanced AI technologies more accessible and affordable for businesses.

By leveraging these developments, businesses can drive innovation, improve efficiency, and achieve significant cost savings. The applications of multimodal AI span numerous industries, from customer support and healthcare diagnostics to manufacturing and financial analysis. Competitive pricing strategies further lower the barriers to entry, enabling businesses to scale their AI initiatives.

To stay competitive, businesses should evaluate their current AI usage, explore cost-saving opportunities, and invest in the latest AI technologies. Staying informed and collaborating with AI experts will ensure businesses can capitalize on these advancements and drive future success. Embrace these changes, invest wisely, and leverage AI to transform your operations and achieve new heights of innovation and efficiency.

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