Neural or Nothing - Rewiring Business for the AI Age

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Introduction

On January 28, 2025, Manhattan Venture Partners (MVP) hosted a webinar titled Neural or Nothing - Rewiring Business for the AI Age. The event brought together a panel of industry leaders to discuss the transformative impact of artificial intelligence (AI) on businesses and society. Sunil Rajgopal, senior software analyst at Bloomberg Intelligence, moderated the webinar which featured insights from experts in the evolving AI space, autonomous systems, emergency response through AI, and vibrant venture capital focus on AI.

The panel included:

• George Matthew, Managing Director at Insight Partners, with over two decades of experience in scaling high-growth technology companies
• Michael Martin, CEO of RapidSOS, revolutionizing emergency response through AI‍
• Don Burnette, Founder and CEO of Kodiak Robotics, a pioneer in autonomous transportation‍
• Vibhu Narwekar, former Microsoft AI strategist and founder of an AI startup, focusing on enterprise AI adoption‍
• Santosh Rao, Head of Research at MVP, providing macroeconomic and industry insights‍
• Sunil Rajgopal, Senior Software Analyst, Bloomberg Intelligence (Moderator)

Key Takeaways

• AI is rapidly evolving, driven by advances in infrastructure, foundation models, and AI applications.
• Investment in AI is focusing on tools that enable scalable and efficient model development, such as MLOps and LLMOps.
• AI applications are expanding beyond traditional use cases, influencing industries like marketing, emergency response, and autonomous transportation.
• The emergence of generative AI (Gen AI) and large language models (LLMs) is reshaping business strategies, productivity, and workforce requirements.
• AI will augment human capabilities, leading to job transformation rather than job loss, with new roles like AI prompt engineers and data ethicists emerging.
• The democratization of AI tools and infrastructure is lowering costs and enabling broader adoption across industries.

Discussion Highlights

"The webinar began by highlighting AI’s shift from a breakthrough technology to a business necessity. With Goldman Sachs projecting AI to add $7 trillion to global GDP over the next decade, yet only 15% of companies currently using generative AI in production, the  discussion underscored the gap between potential and adoption—making it clear that businesses must act now to stay competitive."

Q&A

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The main discussion was followed by a Q&A session where experts shared their insights on AI innovation, regulation, and its impact on business and technology.

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Sunil: With the recent developments in the AI space (referring to news flow related to Chinese AI start-up DeepSeek), where do you see the innovation cycle heading? Specifically, how are compute and memory requirements evolving?

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Don: The recent news has been incredibly exciting but also serves as a wake-up call. For years, the focus has been on scaling— bigger models, more compute, larger data centers. The perception has been that AI innovation is primarily about deploying massive resources. However, the latest breakthroughs, particularly from companies like DeepSeek, challenge that notion.
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We’re seeing a shift toward efficiency—building AI models that deliver comparable value at a fraction of the cost. The winners of the future won’t necessarily be the companies with unlimited capital, but rather the ones that optimize their models for lower- cost deployment while maintaining performance. This is happening not just in LLMs but across autonomy and other AI-driven sectors. The key takeaway is that innovation isn’t solely about scale—it’s about making AI more accessible and cost-effective.
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George: I’d like to add to that. We’ve seen how LLMs, particularly with the transformer architecture, have scaled in the past few years. The arrival of GPT-3 and now GPT-4 has demonstrated how training trillion-parameter models with enormous datasets—six to seven trillion tokens—can create some of the most complex artifacts in human civilization. These models have fundamentally changed our understanding of reasoning and knowledge.

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However, as Don pointed out, we may have leaned too heavily on the idea that simply adding more compute was the only path forward. The real breakthrough is happening in efficiency—new methodologies that optimize model training and inference. DeepSeek is a great example of this shift, showing that it’s possible to build competitive models at a fraction of the traditional cost.
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We should also keep in mind Jevons’ Paradox—historically, greater efficiency in resource usage has led to increased overall consumption. In AI, this means that while we may develop more efficient models, the demand for compute will continue to rise. The difference now is that compute will become more democratized, accessible to more players rather than concentrated among a few major firms.

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Vibhu: I’ll add to that. Scarcity drives invention. A couple of months ago, I was talking to a Gartner analyst who said LLMs are a
$13 billion market growing at a 54% CAGR. I told him that while this is a great opportunity, don’t put all your eggs in one basket. Innovation will happen, but differentiation among models is shrinking. The real gap is in monetization.
Many big AI providers are offering subscriptions at $20–$30, yet they’re losing money. The AI ecosystem isn’t just about LLMs— it includes DevOps tools, infrastructure, and applications. Now, with DeepSeek building models at a fraction of the cost, the paradigm is shifting.

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People focus too much on benchmarks and narrow use cases. But real competitive advantage comes from solving real-world
business problems — improving efficiency, driving productivity, and creating tangible top-line and bottom-line value.
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Sunil: In the age of “neural or nothing,” how do you think about monolithic applications and their future? How do you consider this in your investment decisions?

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George: When we look at existing software architectures built over the last few decades, we have to acknowledge that platforms and tools needed to evolve. That’s why we’ve focused a lot on MLOps, LLMOps, and the modern data stack—these are the foun- dations necessary to build today’s AI systems.

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From a pure software perspective, there’s a fundamental shift happening. Historically, software has been built around rules engines—predefined, rigid workflows. Now, we’re transitioning to reasoning engines. This is a massive change — probably the biggest shift in software history.

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Instead of encoding every rule manually, we now have systems with near-human reasoning. This changes how software is built and consumed. It’s already happening in areas like sales and business development, where AI-powered SDRs (Sales Develop- ment Representatives) are replacing traditional workflows. These systems aren’t just automating tasks—they behave like human agents.
The impact goes beyond software—it’s extending into services and even labor. AI isn't just transforming software products; it’s reshaping entire business functions. That’s where we see the opportunity.

Sunil: With a new administration in place, what are your expectations for the startup ecosystem? What does it mean for M&A and IPO activity?

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Don: The market has responded positively, especially in autonomy and AI-driven physical systems. One big challenge has been the patchwork of state regulations on AI and autonomy. Right now, laws vary by state—some, like Texas, are more permissive, while others are restrictive. The hope is that this administration will establish a clear federal framework. If that happens, it’ll be a huge tailwind for investment.

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On the IPO side, the pipeline has been building. We’ve seen companies holding off, waiting for better market conditions. But in 2Q25, maybe even late 1Q25, we expect to see some of the best-positioned companies start pricing. Once that happens, there’ going to be a flurry of activity for the rest of the year.

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M&A is harder to predict, but generally, when IPOs pick up, private markets benefit too. We’re optimistic that the conditions are
aligning for a strong year ahead.

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Sunil: Vibhu, you previously touched on the societal implications of AI. As we see AI being adopted to optimize costs, enhance productivity, and unlock new business opportunities, these shifts seem to be happening simultaneously. Given this rapid evolution, do you think we are moving too fast, or are we still not moving fast enough?

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Vibhu: In my view, we are still moving too slow. As we’ve shifted from deterministic to more stochastic AI processes, reaching PhD-level human-like reasoning, many organizations are still in the early stages of leveraging AI’s full potential. From my experience developing solutions and working with Fortune 500 clients, the typical starting point is automating repetitive tasks— chatbots, RPA, and conversational AI, which have been around for years.

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Many companies still see AI as a cost-cutting tool. They deploy AI agents to automate tasks, which delivers immediate ROI and builds internal momentum. AI has become a boardroom priority for CEOs across both Fortune 500 and SMBs, but initial pilots are often focused solely on cost reduction.

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As confidence in AI grows, businesses start reimagining productivity by integrating AI into more complex workflows. AI moves from being a basic task handler to a collaborative problem solver, transforming into what we call “digital workers.” This shift frees human talent to focus on higher-value initiatives like strategic decision-making, product innovation, and richer customer engagement.
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Ultimately, AI’s real power isn’t just in reducing costs—it’s in uncovering entirely new revenue streams and business opportunities. Companies that thrive will be the ones that leverage AI across all three dimensions: cost reduction, productivity improvement, and  new  revenue  generation. Treating  these  as  isolated  goals  limits AI's potential, but done right, AI  doesn’t  just  optimize processes—it fundamentally reshapes how businesses generate value.

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About the Analyst

Santosh Rao has over 25 years of experience in equity research with a primary focus on the technology and telecom sectors. He started his equity research career at Prudential Securities and later moved to Dresdner Kleinwort Wasserstein, Gleacher & Co, and Evercore Partners, where he followed Telecom and Data Services. Prior to joining Manhattan Venture Partners, he was the Managing Director and Head of Research at Greencrest Capital, focusing on private market TMT research. Santosh has an undergraduate degree in Accounting and Economics, and an MBA in Finance from Rutgers Graduate Business School. While at Gleacher & Co he was ranked leading telecom equipment analyst by Starmine/Financial Times

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