Post-ChatGPT Dawn: The Modern Apollo Program

GenAI’s journey over the past two years mirrors the trajectory of other groundbreaking technologies: initial hype tempered by practical realities, leading to sustained innovation. While the transition from fascination to adoption was slower than anticipated, the foundations are now firmly in place. With continued investment, rising developer engagement, and growing confidence in ROI, GenAI is poised to become a cornerstone of modern economies
When OpenAI’s ChatGPT debuted in late 2022, it ignited a technological fervour reminiscent of the dot-com boom. Consumers marvelled at its conversational abilities, sparking imaginations about its potential to reshape industries. Daily usage soared, and Google search interest in generative AI doubled within months. Yet, the initial enthusiasm quickly encountered a sobering reality: enterprise adoption of generative AI (GenAI) was cautious and deliberate.
Morgan Stanley’s recent analysis highlights this dichotomy. While consumer adoption of tools like ChatGPT and image generators flourished, enterprises grappled with questions about data security, regulatory compliance, and measurable returns on investment (ROI). A proprietary AlphaWise survey of 400 companies underscores these concerns – over 60% cited data security as a primary hurdle, and nearly 25% feared reputational damage from hasty implementation.
The divergence between consumer and enterprise use cases is striking. On the consumer side, tools like GitHub Copilot and Grammarly revolutionised productivity for developers and writers, respectively. These platforms allowed individuals to learn new skills, automate routine tasks, and achieve more with fewer resources.
Enterprise adoption, by contrast, has focused on cost savings and operational efficiency. Larger companies, with annual revenues exceeding $15 billion, were 20% more likely than smaller firms to report cost reductions from GenAI projects. Customer service emerged as a leading application across industries, with GenAI tools reducing call centre costs and enhancing resolution rates. Despite the slower pace, enterprises have begun moving from proofs of concept to scalable solutions, with notable successes detailed below.
Walmart, for example, tackled the monumental challenge of managing its vast product catalogue comprising 850 million data points spread across 140,000 SKUs. Traditionally, such updates demanded immense human effort, but with the power of GenAI, the process accelerated by a staggering 100 times. Leveraging advanced language models, Walmart not only optimised its inventory management but also introduced customer-facing tools like GenAI-powered search engines, turning a logistical hurdle into a competitive edge.
Similarly, L’Oréal transformed how customers interact with its extensive range of cosmetics through Skin Genius, an AI-driven recommendation engine. The firm developed tech aimed at analysing customers’ skin and tailoring personalised product suggestions – a tool that pushed in-store conversion rates from a modest 10% to an impressive 70%, showcasing how GenAI bridges the gap between consumer needs and product offerings.
In the realm of public safety, Axon addressed a critical pain point for law enforcement – the 25% of time officers spend on writing reports. Using the Draft One tool powered by large language models, Axon automated the creation of incident reports from bodycam footage, freeing up officers for frontline duties. This innovation not only improved efficiency but also generated $100 million in pipeline revenue, solidifying its place as one of Axon’s most successful innovations.
In 2025, global cloud capital expenditures (capex) are projected to rival the real-dollar cost (inflation-adjusted) of the Apollo space program. This staggering investment underscores the belief that GenAI’s potential is transformative. As infrastructure scales, enterprises can harness more robust computing power to refine and deploy AI models, addressing bottlenecks like latency and scalability.
Developers remain the life-blood of the operation, with GitHub metrics revealing unprecedented engagement in GenAI projects, with OpenAI alone accounting for 50% of AI-related contributions. This surge in activity indicates a thriving ecosystem where developers continuously push the boundaries of what GenAI can achieve. From creating domain-specific copilots to enabling multimodal functionalities, this community is a cornerstone of GenAI’s evolution.
While initial hurdles dampened expectations, the AlphaWise survey shows promising outcomes for companies that embraced GenAI early. Over 40% of surveyed firms reported exceeding ROI expectations, particularly in sectors like technology and industrials, suggesting as enterprises grow more comfortable with the technology, adoption rates will accelerate.
Challenges and Ethical Implications
The road ahead is not without obstacles. Enterprise caution stems from legitimate concerns about data privacy, regulatory frameworks, and intellectual property rights. Moreover, the rise of GenAI raises ethical questions about workforce displacement and algorithmic bias. As automation permeates industries, organisations must balance productivity gains with inclusivity and accountability.
For instance, while Axon’s Draft One tool exemplifies efficiency, it also highlights the need for rigorous safeguards against AI errors and biases, particularly in sensitive applications like law enforcement. Similarly, enterprises deploying GenAI must prioritise transparency to build trust among stakeholders.
As GenAI matures, its applications are likely to expand beyond productivity-enhancing tools into transformative domains. Consider the demographic challenges facing a few major economies. By 2030, countries like Japan, China and much of Europe will face a net loss of 75 million workers just from the aging population. In this context, GenAI’s ability to augment human capabilities – or even fully automate repetitive tasks – could address labour shortages and boost economic productivity. Moreover, the next generation of GenAI tools, exemplified by multimodal models, promises even greater versatility. Imagine a sales representative using a single platform to analyse emails, inventory records and customer preferences simultaneously, offering hyper-personalised solutions in real-time. Such innovations could redefine how businesses operate, from retail to healthcare and beyond.