The Emergence of AI-First Era – Part II

The Emergence of AI-First Era – Part II

For the first time, AI can create –and exciting tools are being launched based on this creative capability. But numerous challenges still need to be addressed

AI First Applications

The next generation of software applications will emerge with AI as a first-class citizen. While data-driven applications will enable a more personalized and improved customer experience, like successful traditional software, AI-first applications must solve an acute need and have attributes that suggest long-term durability (e.g., being embedded within a workflow, access to proprietary data, network effects, etc.).

The rise of foundational models, specifically LLMs exposes many unserved and underserved gaps in the current infrastructure stack. New tools will need to emerge for developers, data scientists, and non-technical users to leverage LLMs within an enterprise. These will improve the accessibility and usability of large language models.

There are numerous challenges for new infrastructure technologies to address, including the raw, unstructured quality of image and video data, the static nature of existing LLM interfaces, the fragmentation of different models, which will only continue as models become increasingly industry-specific, or the need for effective governance to ensure unbiased, responsible results.

Model lifecycle management solutions will take on even greater importance as LLM creation, deployment, and collaboration become more complex. Emerging solutions in the management and safety of datasets will construct important guardrails and processes against bias in line with new regulations and expectations in the ethical uses of AI.

The industry is excited about tooling platforms that make the lives of the “prompt engineer” or LLM-focused data scientist easier. They will form the backbone of next-gen model creation, deployment, and orchestration.  The MLOps toolchain will continue to thrive as LLMs continue to multiply. For instance, Weights & Biases is a significant beneficiary as the hyperparameter tuning and version control solution for most LLM builders.

Horizontal applications

Every function in an organization with repetitive and/or skill-based work will be reshaped by foundation models, whether it is the democratization of coding, generating sales, supporting customers with virtual agents, or creating content for design and marketing. That said, some of these functions will be reshaped more effectively by agile and innovative incumbents. Identifying where large language models are better suited to enable a better feature instead of a standalone platform will be critical when investing in this space. In addition to this, it will be important to find problems that require solutions deeply embedded into business workflows and access to unique data sets.

Developer Productivity & Security

Two areas where new standalone platforms will arise are developer productivity and security.

  • Developer productivity: We see significant opportunities in the creation of coding automation tools that accelerate the delivery, development, and testing of software code. Developers spend hours per day on debugging and test/build cycles which computer-generated code and code-review tools can significantly reduce. An estimated $61B can be saved per year from these tools in reduced inefficiencies and costs for software development. Moreover, democratized access to product and tech development for non-coders holds the potential to open the doors of entrepreneurship and software development for non-developers, sparking innovation and value creation in the economy.
  • Security: One of the most exciting applications of generative AI to security comes in auto-remediations where computers can predict and effectively address security vulnerabilities. Moreover, AI/ML models already power fraud detection for major software and fintech companies – however, the lack of quality data is a major issue. With generative AI, businesses can create more complex and widespread fraud simulations using synthetic data (i.e., fake photographs, fake PII) that solve for the lack of real-world data and enable more accurate detection rates.

Saves time & money but disrupts jobs

Fundamentally, generative AI reduces the money and time needed for content creation– across text, code, audio, images, video and combinations thereof. On the flip side, the rise of generative AI could affect jobs as machines begin to supplant human workers. AI could lead to further job displacement for those engaged in the processes impacted, and in some cases companies and business models may become obsolete.


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