AI Evolution - Blog

AI - Beyond the Hype’ blog explores a wide range of topics tailored for operational leaders and innovators, including:

AI Market Analysis: Periodic reviews of market trends and the evolving AI landscape.

AI Product Innovation and Strategy: Relevant topics around product development, economics, and go-to-market (GTM) strategies.

AI Research Insights: Relevant topics around models, LLMs, GenAI, agents, algorithms, deep learning, neural networks, and much more.

AI Software Engineering Lifecycle: Best practices for developing, testing, and managing AI-enabled and AI-native products.

Adopting AI Across Enterprise: Strategies to boost operational efficiency through AI adoption.

And much more, offering relevant insights to keep you ahead in the AI-driven world.

AI Evolution blog explores a wide range of topics tailored for operational leaders and innovators, including:

AI Market Analysis: Periodic reviews of market trends and the evolving AI landscape.
AI Research Insights: Relevant topics around traditional machine learning, models, LLMs, GenAI, agents, algorithms, deep learning, neural networks, agentic software and much more.
AI Product Innovation and Strategy: Relevant topics around product development, economics, and go-to-market (GTM) strategies.
AI Software Engineering Lifecycle: Best practices for developing, testing, and managing AI-enabled and AI-native products.
Adopting AI Across Enterprise: Strategies to boost operational efficiency through AI adoption.
And much more, offering relevant insights to keep you ahead in the AI-driven world.

Internet and AI Evolution - A High level Comparison

AI Evolution: Lessons from the Dotcom Era

As we witness the rapid evolution of AI, it’s helpful to draw parallels with past technological revolutions. For those who have experienced similar shifts, this perspective provides valuable context for understanding the present landscape. One such comparable transformation is the rise of the internet, which had a similarly profound impact. In this post, we’ll explore some high-level concepts from the Dotcom era, along with key developments like Cloud, Search, Mobile, and SaaS, to highlight notable similarities.

A Look Back: The Dotcom Boom of 2000

The technology industry often follows repeating patterns. If you find yourself navigating the many layers of the AI stack today, it's no surprise that various players and ecosystems are thriving.

To simplify and draw comparisons, let’s break down AI innovation into six key layers and see how they parallel the internet evolution of the early 2000s.

The AI Stack and Its Evolution

Hardware & Ecosystem – Companies like Nvidia, AMD and those building AI optimized chips(for both training and inference with promise of quantum chips), large-scale data centers and infrastructure are thriving, similar to how specialized chip and hardware(for scaling networks) manufacturers flourished during the internet boom.
Data Platforms & Ecosystem – Data-centric companies like Databricks, Snowflake, and major cloud providers (AWS, Microsoft Azure, Google Cloud) are rapidly expanding, just as internet backbone infrastructure did two decades ago.
Foundation Models & Ecosystem – Organizations like OpenAI, Anthropic, Meta AI, and cloud partners (AWS, Microsoft Azure, Google) are leading AI model development, much like early internet research and software companies.
Application Development Toolsets – Tools like LangChain,LLamaIndex and various existing open-source ML libraries (PyTorch, TensorFlow) are making AI application development more efficient—akin to how software development tools and networking protocols evolved in the dotcom era.
Enterprise & SaaS ML Applications – AI capabilities are now embedded into enterprise and customer facing SaaS products, mirroring how software companies adopted cloud and mobile.
Agents & Agentic AI – Companies are innovating in agentic AI to automate workflows, similar to how SaaS, Mobile and Other application workflows flourished last decade.

Parallels with the Internet Boom

If we look back at the dotcom era, we can see a similar structure in technological evolution:

Hardware & Ecosystem – Companies like Intel, AMD, Ciena, and Cisco built networking-friendly chips, hardware just as today’s AI hardware leaders are doing.
Internet Backbone & Infrastructure – Firms like Cisco, Juniper, 3Com, and Lucent thrived by building the internet’s infrastructure, akin to today’s data platform providers.
Application Development Toolsets – Wireless and wireline technology research accelerated, led by companies like Qualcomm—similar to the AI development tools we see today.
Software Development & Protocols – Open-source software and networking protocols emerged, just as AI tools and libraries are doing now.
Enterprise & Consumer Applications – The dotcom boom gave birth to tech giants like Google and Amazon, just as today’s AI transformation is fostering new enterprise solutions.
SaaS & Cloud Services – The rise of SaaS companies in the late 2000s and last decade mirrors the rapid expansion of AI-driven enterprise software today.

The Key Takeaway

History shows that despite the dotcom crash, real innovation persisted—B2B and B2C software thrived, and the internet shaped the future of business and technology. The same applies to AI today. While some companies will inevitably rise and fall amid the hype, AI’s long-term impact will be undeniable. As we learnt from dotcom era, as AI is more adopted in application layer, it helps clearly understand supply and demand equations around collective computing power (Remember, application developers emerged as winners in dotcom burst, take a look at https://finance.yahoo.com/quote/CSCO/ chart!)

Understanding how we got here helps us better navigate where we’re headed, while managing hype cycle. Whether in AI, mobile, or past tech revolutions, the cycle of winners and losers may change—but innovation always moves forward.

We invite you to learn from our real-world experience in developing and executing AI strategies, and discover how we can support your organization's AI journey.

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BusinessBlackPepper LabsFebruary 3, 2025

One of the early use cases of Generative AI has been around, how to create, manage and operate Agents for supporting existing customers. As you might have noticed, Salesforce has been promoting AI agents in their platforms, so do other providers like Servicenow, Zendesk. Naturally it is the extension of their business model to protect existing business from over AI optimization, while making sure a their version of AI narrative is promoted. However there are plenty of AI-First agent products have been coming to market in 2024. We have seen good adoption of these agents in enterprise context.

As these technologies mature, a crucial conversation has emerged around leveraging AI for end-to-end customer support and success. This includes new customer onboarding to providing product support(including tech support in most cases).

So, if you are a Chief Customer Officer or Leader in this organization, naturally you have to be able to create Ai strategy to adopt agentic software capabilities. In most organizations, these capabilities are provided by IT function. However it is crucial to be able to understand and create your own strategy around AI for your department beyond toolings. Following are crucial points that keeps coming up in conversations between Exec Leadership and Chief Customer Success office.

"How can we leverage AI Agents to best support to customers to make them successful?"

"What's the fastest path to optimize existing customer support systems and data?"

"How can we increase key KPIs such as NPS and CSAT while optimizing customer success/suport investments?"

"How do we maximize our existing talent's efficiency?"

"Which current Gen AI/Agent tools can deliver immediate productivity gains?"

While these questions are critical, the answers vary significantly based on organizational role and AI expertise. This has created three distinct stakeholder groups:

1. Investors, particularly in PE and VC spaces, seeking efficiency gains in customer success and support budgets beyond offshoring.

2. Organizational leaders driving acceleration and digital transformation initiatives need to show ROI backed by key metrics including efficiency gain metrics

3. Product Support, Success and onboarding leaders tasked with adoption, and implementation

For those in the third category, the responsibility is significant. Success requires crafting both short and long-term strategies through careful discovery, stakeholder education, and adaptive road mapping - all while navigating this rapidly evolving technological landscape collaborating with technology function.

The challenge isn't just about adopting new tools - it's about understanding their true potential and limitations in your specific customer success, product and operational context. As we move forward, the winners won't be those who simply embrace AI, but those who strategically integrate it into their customer lifecycle managment processes while maintaining focus on business outcomes.

We invite you to learn from our real-world experience in developing and executing AI strategies, and discover how we can support your organization's AI journey.