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The rise of AI in enterprise software

Also: The Race to Capture Value: Cloud Lessons for the AI Era


Today’s AI landscape continues to be shaped by concerns over data privacy, the significant role of AI in transforming legacy enterprise systems, and its potential impact on the global financial market. As we dive deeper into the realm of artificial intelligence, it’s becoming clear that with innovation comes a mix of opportunities and challenges. In today’s newsletter, we cover the rise of AI in enterprise software, the impending race to capture value in the AI era, recent findings on ChatGPT’s accuracy, and a cautionary note from the SEC chair about AI’s potential role in future financial crises. We’ll also give you updates on the latest AI tools and venture capital news.


  • 🦾 The rise of AI in enterprise software

  • 🏎️ The Race to Capture Value: Cloud Lessons for the AI Era

  • 🤔 An In-Depth Analysis of ChatGPT revealed that 52% of ChatGPT's answers contain inaccuracies and 77% are verbose

  • 💰 AI will be at the center of the next financial crisis, SEC chair warns

The integration of large language models like ChatGPT into enterprise software is raising significant concerns among CIOs and IT heads about data privacy and compliance with regulations like GDPR. These AI systems require extensive training data, often sourced from the public cloud, leading to apprehensions about security and data confidentiality. According to a BlackBerry study, 66% of 2,000 IT decision-makers surveyed are either banning or contemplating a ban on ChatGPT. While larger businesses might have the resources and data diversity to develop their own AI models, smaller companies face challenges in obtaining wide-ranging datasets. As enterprise software evolves, the decision of whether to build or buy AI capabilities becomes a pressing debate, exemplified by recent shifts at SAP.

In the shift from cloud and SaaS computing to the AI era, the primary determinant of long-term value will be defensibility. Historically, software transformations, such as the move from on-prem computing to the cloud, result in new market winners. With AI promising a significant transformation, companies are rushing to capture this evolving market. While earlier defensibility sources like unique technology, network effects, and customer feedback-driven expansion remain vital, the AI landscape introduces fresh competitive advantages. For instance, proprietary data has become a critical edge for many AI startups. However, with the rapid pace of AI adoption, the landscape’s dynamics are changing faster than previous shifts. Major incumbents in the SaaS era, such as Salesforce, have quickly introduced AI-based products.

Over the past decade, programmers have heavily relied on Q&A platforms, but ChatGPT’s emergence is changing this norm. A study comparing ChatGPT’s answers to 517 Stack Overflow questions found that while 52% of ChatGPT’s responses had inaccuracies and 77% were wordy, users preferred them 39.34% of the time for their detailed and clear language. This highlights the importance of improving ChatGPT’s accuracy and reminds users to approach its answers with caution.

The SEC chair, Gary Gensler, has expressed concerns over the potential risks posed by the increasing integration of Artificial Intelligence (AI) in the financial sector. He warns that AI might be at the epicenter of future financial crises. Central to these concerns are AI-powered trading algorithms which, due to their opaque nature and similarity, might simultaneously execute similar actions leading to a market crash. This is compounded by a lack of varied expertise in AI model building and potential regulatory measures that could inadvertently promote model homogeneity. The complexity and opacity of deep learning algorithms render them difficult for regulators to oversee. Beyond trading, AI models are used in areas like creditworthiness assessments, where their evolving nature might introduce unpredictable biases.

🛠️ AI tools updates

Generative AI is revolutionizing photography, allowing for the transformation of standard photos into AI-generated art. Kyle Goodrich, a SnapChat developer, has unveiled the DreamGenerator camera, a device capable of instantly converting captured images into one of 30 themed artworks using in-built generative AI. The camera, powered by a Raspberry Pi and software like Stable Diffusion and ControlNet, can perform these transformations without relying on powerful GPUs or cloud offloading, traditionally required for such AI processes. While still in prototype and offering a niche appeal compared to smartphone apps offering similar features, this stylish camera has garnered attention.

💵 Venture Capital updates

SK Telecom, Korea’s leading wireless carrier, will invest $100 million in U.S. AI firm Anthropic, aiming to enhance its presence in the AI industry. Anthropic, established in 2021 by ex-OpenAI members and known for its AI assistant Claude, will collaborate with SK Telecom to create an AI platform and a multilingual large language model (LLM). Jared Kaplan, Anthropic’s chief scientist, will spearhead the new LLM’s development. This partnership aligns with SK Telecom’s recent initiative with global telecommunication companies like Deutsche Telekom, e& and Singtel to expedite AI-driven transformation and spawn new AI business opportunities. CEO Ryu Young-sang expressed aspirations for SK Telecom and Anthropic to lead in cultivating a global AI ecosystem.

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⭐️ Generative AI image of the day