The Generative AI Advantage in Private Equity and Venture Capital

Here is what’s new in the AI world.

From Data to Deal Flow: How Top Firms Are Harnessing Generative AI

As more investment dollars seek great opportunities, every competitive edge matters. For you, Generative AI presents a seemingly powerful option: the ability to leverage large language models (LLMs) to analyze investment ideas with previously unimaginable speed.

The sentiment in your industry reflects this, with a majority of investors believing Gen AI will have a "transformational" impact and viewing its use as a high priority.

While LLM-derived analysis is undisputedly rapid, you, as an intelligent investor, need to harness that speed through a first-class process. The goal is not merely to be fast, but to be right, to quickly discover risks and reallocate your resources toward only the very best opportunities.

Recognizing the Limitations of Gen AI in Your Process

Investment decisions demand precision. A key challenge you face with the largest enterprise LLMs is their reliance on broad data pools, like the public internet.

These models are subject to inherent biases, can present contradictory information with false authority, and may offer overly optimistic or simplistic analyses.

This "false precision" can lead you to misallocate your team's precious time, pursuing deals that should have been quickly dismissed.

A better approach for your team is to use your proprietary research data to rigorously test and compare the answers provided by public LLMs. Your internal notes from management meetings, expert calls, and financial adviser discussions contain nuanced, ground-level details that are invisible to general AI models.

Recent analysis comparing expert interviews with LLM outputs across more than 100 structured queries revealed critical shortcomings you must account for:

1. The "Happy Talk" Bias

In seven out of ten industries analyzed, Gen AI reports portrayed a much more uniformly optimistic outlook than reports based on expert interviews. Your expert sources are more likely to ground analysis in cautious realism, reflecting both potential and on-the-ground challenges.

For instance, an LLM might declare a product's penetration universally successful, while your expert calls reveal strong adoption by enterprises but little need among small and medium-sized businesses.

2. Significant Divergences and Contradictions

The analysis found that LLM-generated reports often diverged, sometimes substantially, from what industry experts shared. These weren't minor discrepancies; they spanned core metrics like market size, growth rates, pricing, and margin structures.

Relying on this unvalidated public data without checking it against your proprietary insights carries significant risk and warrants either deeper investigation or a decision to move on.

3. Critical Omissions

Approximately 40% of important data points uncovered in expert interviews were completely absent from corresponding LLM answers and could not be unearthed with further prompting.

These missing insights are often deal-critical but invisible to public data: conventional contract structures, true unit economics, channel breakdowns, and regulatory hurdles.

A Case in Point

When examining the US baby and kids' apparel market, the expert-driven report painted a picture of a resilient, incrementally growing sector.

In contrast, the Gen AI report contained confidently stated inaccuracies, overstating online sales penetration, understating annual spending per child by a factor of three, and incorrectly stating the market had shrunk.

It also lacked vital details on gross margins, sales by age group, and inventory norms, all of which are crucial for your investment decision.

Adopting a Balanced and Rigorous Approach for Your Team

The point is not that your proprietary data is always right and LLM answers are always wrong. Often, neither source alone will provide a complete picture. Instead, you should actively look for these misalignments and use them as a tool.

The gaps and contradictions between AI output and expert insight help you identify the most material open questions that will determine an opportunity's true potential.

Your approach should be balanced:

• Use Expert Insights for Reality Checks: Complement LLM products with industry-specific expert interviews to gain a realistic understanding of operational realities and market-specific risks.

• Institute Rigorous Cross-Checking Processes: Develop a culture and set of processes for checking all information sources. Gen AI accelerates discovery but does not safeguard quality without your active guidance.

This disciplined approach yields analytical thoroughness, allowing you to confidently identify genuine growth while anticipating pitfalls.

It creates investment rigor by supporting your due diligence with comprehensive, traceable data that directly impacts valuation accuracy and risk assessment.

When you have granular, verified data at your fingertips, you make better decisions about allocating both capital and time throughout the investment process.

As one investment leader aptly put it, with Gen AI, you must put your process on a "deliberate diet." You must be careful about what information you consume and allow into your investment process, avoiding the "junk" and focusing on the high-quality, verified insights that build true conviction.

CES 2026: Major Tech Companies Unite to Advocate for Open-Model AI Ecosystem

The recently concluded 2026 Consumer Electronics Show (CES) marked a significant shift in the global tech industry, placing a strong emphasis on international collaboration and the growing prominence of open-source AI and software models.

This focus reflects a collective movement towards fostering a more inclusive and transparent technological ecosystem.

A notable trend was the influential role of Chinese technology companies in advocating for open-source systems. Firms like Alibaba and DeepSeek are releasing advanced AI models, such as the Qwen series and the R3 reasoning model, under open licenses.

This strategy aims to promote global digital sovereignty and reduce technological disparities by allowing developers worldwide to access, modify, and build upon their foundational work, which has already spawned tens of thousands of variations and adaptations.

This approach is also being adopted by Chinese robotics companies like Unitree and Agibot, which are integrating open-source AI to enhance robot capabilities, helping to set new international standards for transparent AI ecosystems.

The spirit of partnership was a central theme, highlighted at events like the CES Asia Night, where leaders called for deeper U.S.-Asia relationships.

This collaboration extended to specific ventures, such as Dolby Laboratories partnering with Chinese TV manufacturers Hisense and TCL to advance display technology with its new Dolby Vision 2 standard.

The push for openness is a global phenomenon. Industry leaders like Nvidia's CEO Jensen Huang emphasized that the true proliferation of AI depends on open innovation, ensuring digital intelligence does not leave anyone behind.

This philosophy is driving powerful synergies between industrial and tech giants. For instance, Siemens and Nvidia are jointly developing an industrial AI operating system that integrates "open physical AI" and agentic software.

This platform allows for the creation of virtual "digital twins" to design and optimize factories before they are physically built. Similarly, partnerships like the one between Boston Dynamics, Google DeepMind, and Nvidia are accelerating advancements in robotics, demonstrating how shared research accelerates dexterity and autonomy.

The benefits of this open-source movement are creating tangible impacts across sectors:

• Healthcare: Companies like Abridge are using open systems to automate clinical documentation, freeing up doctors' time. Open-source frameworks like BioNeMo are democratizing advanced tools for drug discovery, allowing smaller institutions worldwide to participate in high-level research.

• Media & Content Creation: Open-source "neural rendering" tools are making extreme photorealism and high-speed animation accessible to independent creators and small studios, a capability once reserved for large corporations.

• Smart Homes & Regulation: Trade groups like the Home Connectivity Alliance advocate for open ecosystems to ensure devices from different brands work together seamlessly. Regulatory bodies, including the European Union through its AI Act, are also encouraging open-source models to boost competitiveness and ensure safety through transparency.

Analysts conclude that the widespread advocacy for open-source cooperation at CES 2026 signals a pivotal transition from isolated, proprietary competition toward a more integrated global technology landscape.

By sharing foundational models and building collaborative standards, companies worldwide are working to ensure that artificial intelligence serves as a universal tool for progress across all fields.

Sorry, Open-Source Purists: Meta is Right to Go Closed with AI

Meta Platforms appears to be at a strategic crossroads, re-evaluating its initial open-source approach to AI. With its flagship LLaMA model reportedly falling short of expectations, the company is now pivoting towards developing proprietary, closed-source models.

This move places it in direct competition with the current market leaders OpenAI, Google (with Gemini), and Anthropic (with Claude), in a high-stakes race for dominance at the frontier of AI development.

While OpenAI's ChatGPT and Google's rapidly advancing Gemini 3.0 currently lead the pack, the competitive landscape is intensely dynamic. Anthropic is also making significant inroads, particularly in coding applications, as it prepares for a potential IPO.

This activity could intensify the battle for investment, especially if OpenAI also pursues a public offering this year, potentially redirecting capital from other major tech players.

A Potential Value Opportunity in a Shifting Strategy

From an investment perspective, this strategic shift may present an opportunity. Despite playing catch-up, Meta's new direction, combined with its acquisition of the agentic AI asset Manus, could position it as a potential value play within the "Magnificent Seven" tech stocks.

The argument is that if Meta can demonstrate meaningful progress with its new AI models, it might undergo a significant re-rating from investors, similar to Alphabet's past performance surge when its AI capabilities were more fully recognized.

The company's next-generation models, codenamed Avocado (for general intelligence) and Mango (for image/video generation), are central to this comeback thesis.

The analysis suggests that moving to a closed-source model is a key strategic correction, aligning Meta with the industry's prevailing path toward higher-margin, economically defensible, and potentially safer AI systems.

The Ingredients for a Surprise Breakthrough

Crucially, the case for Meta's success is not based on spending alone but on a unique combination of assets:

• A world-class Superintelligence research team.

• A strategic acquisition in Manus, a sought-after platform for developing agentic AI.

• An unconventional training strategy that reportedly incorporates techniques like distillation learning, world models, and even leveraging aspects of Alibaba's open-source Qwen model.

This distinctive approach suggests Meta is not merely scaling compute power but exploring novel technical pathways. This could enable the company to produce a competitive model that surprises the market and narrows the gap with established leaders.

Consequently, Meta's new proprietary strategy and its research direction make it a compelling company to watch through 2026, with the potential for a market-moving AI breakthrough.

OpenAI Buys Torch to Dominate AI Health Assistants

OpenAI has acquired the health-tech startup Torch, integrating its four-person team to focus on developing health and wellness features for ChatGPT. Torch co-founder Ilya Abyzov announced the acquisition on January 13.

Founded in 2024, Torch was led by Abyzov, who previously co-founded Forward Health, alongside co-founders Eugene Huang, James Hamlin, and Ryan Oman. A report from The Information cites an unnamed source stating the deal was valued at approximately $100 million in equity.

The core mission of the Torch team at OpenAI will be to build "ChatGPT Health into the best AI tool in the world for health and wellness."

Torch's technology was designed as a unified platform to aggregate an individual's fragmented medical data, from hospitals, laboratories, wearables, and consumer health services, into a single, coherent "medical memory for AI."

The company argued that AI's utility in medicine is limited when personal health data is scattered across numerous disconnected systems and portals.

Abyzov stated that the decision to join OpenAI was driven by the immense scale of ChatGPT's existing user base. He noted that hundreds of millions of people already use ChatGPT for health-related questions weekly, presenting an unprecedented opportunity to deploy Torch's technology.

He also directly addressed potential privacy concerns, asserting that the move was made with confidence in OpenAI's commitment to privacy, safety, physician collaboration, and high-quality consumer product development.

The Torch team brings shared experience from their prior work at Forward, where they aimed to build large-scale healthcare services. Abyzov reflected that while the path was unexpected, integrating Torch into OpenAI significantly advances the mission they began years ago.

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-Shen & Towards AGI team