AI-enabled marketing today accounts for nearly 45% of all advertising globally. By 2032, AI will influence 90% of all ad revenue. Agencies are facing pressure to adapt or risk irrelevance. This is leading a shift towards value-based pricing models, where costs are determined by the value of services provided to the advertiser.

71% of customers are confident in using AI to virtually try on products before buying. This indicates the potential for AI to streamline decision-making processes in online retail. The top generative AI use cases consumers look forward to are automating product filters based on customer needs, creating customized items, and summarizing product reviews.

This article discusses the transformative potential and current limitations of generative AI like ChatGPT, noting that while it excels in tasks like coding and generating drafts, it struggles with complex tasks that require specific programming. It highlights the need for a vision that matches AI solutions with practical applications, emphasizing that identifying and integrating these into daily workflows remains a significant challenge.

Software is an apprenticeship industry - you can only learn by doing. The industry changes fast, so developers have to keep learning. It takes many years to forge a competent software engineer. Senior software engineers don't only write code - they have to be able to understand, maintain, explain, and manage a large body of software in production over time and translate business needs into technical implementation. Writing code is the easiest part of software engineering, and it's getting easier every day. AI can help generate lots of code really fast, but it can't aid in the work of managing, understanding, or operating that code. Generated code can't be trusted - it needs to be reviewed before it can be committed and shipped to production.

Goldman Sachs released a critical 31-page report titled "Gen AI: Too Much Spend, Too Little Benefit?", arguing that generative AI's productivity benefits and returns are significantly limited and that its power demands will drastically increase utility spending. The report highlights doubts about AI's ability to transform industries, pointing out high costs, power grid challenges, and lack of clear productivity gains or significant revenue generation. It suggests a potentially bleak future for the technology without major breakthroughs.

AI companies are increasingly using the sparkles emoji to signify generative AI features. Google, OpenAI, Microsoft, and Adobe all employ variations of this icon in their products. However, some users resist this trend, viewing the emoji as a misleading representation of AI's capabilities. Critics argue it distracts from significant ethical and labor issues within the AI industry.

Roblox is introducing a generative AI tool allowing creators to generate 3D scenes using language prompts. It will enable users to quickly create and modify environments, significantly speeding up the design process and enabling those with limited 3D art skills to produce more detailed worlds. The system uses a combination of 3D and 2D AI models to ensure logical consistency and reduce errors.

In a recent analysis, Edward Zitron delves into the troubling dynamics of the Software as a Service (SaaS) industry and its relationship with the burgeoning field of generative AI. He highlights a concerning incident where Microsoft considered reallocating resources to prioritize AI capabilities, reflecting a broader trend of Big Tech's aggressive push into AI. Zitron expresses skepticism about the effectiveness of generative AI products from major tech companies, noting that many offerings are underwhelming and often serve as mere enhancements to existing services rather than groundbreaking innovations. Zitron explains that the SaaS model, which charges businesses on a subscription basis for software they do not own, has become a dominant force in the tech industry. While this model can provide cost savings and flexibility for companies, it also creates a dependency that can lead to inefficiencies and frustration. As organizations grow, managing multiple SaaS applications becomes increasingly complex, often resulting in a situation where businesses are locked into ecosystems that are difficult to escape. The author argues that the SaaS market is experiencing a decline in growth, with many companies struggling to maintain their revenue streams. This stagnation is compounded by rising customer acquisition costs and a decrease in customer retention rates. Zitron points out that many SaaS companies are now resorting to price increases and aggressive upselling tactics to sustain their business models, which may not be sustainable in the long run. Zitron connects these trends to the current AI boom, suggesting that the desperation for growth in the SaaS sector is driving companies to adopt AI technologies, even when the practical benefits remain unclear. He critiques the way AI is being marketed, often as a superficial enhancement rather than a genuine solution to business challenges. The author warns that the high costs associated with generative AI could further strain the profitability of SaaS companies, leading to a potential crisis in the industry. Ultimately, Zitron paints a bleak picture of the future for SaaS and AI, suggesting that many companies may be overextending themselves in a bid for growth, risking their financial stability in the process. He calls attention to the need for a reevaluation of business strategies in light of these challenges, emphasizing that the current trajectory may not be sustainable for the tech industry as a whole.