Jan 30, 2025, 5:00 PM ETInterview, Fireside Chat

How Emergence Capital Bet Early on Zoom, Salesforce, Veeva—& Now Together.ai

Gordon and Yaz assert that while the VC environment faces headwinds from exit market lockups, "VC is not dead" but rather that the asset class has evolved; the current "darkest" moments often present the best time to invest aggressively.
The industry currently holds excess capital from the 2020–2021 pandemic era, creating a divergence where large funds must target massive markets (or private equity-style deals) while "right-sized" funds like Emergence focus on high-conviction early-stage hits.
Emergence maintains a "right-sized" strategy to generate strong LP returns with only one or two massive hits per portfolio, avoiding the pressure to deploy capital into dozens of mid-tier deals required by mega-funds.
The firm resists expanding into opportunistic later-stage buyouts despite frothy market conditions, adhering to a values-driven approach focused on B2B software and early-stage innovation to drive future returns.
Gordon predicts the IPO market will remain volatile and potentially unfavorable in 2025 due to a lack of economic stability, potential interest rate increases, and a backlog of companies stuck at 2021 valuations.
Secondary markets are viewed as a scale-dependent opportunity; Emergence does not currently prioritize secondaries as a core strategy due to their smaller portfolio size compared to mega-firms that can carve out pieces of large portfolios.

The partners identify the Viva (now part of a larger entity) success story as proof that "unobvious" opportunities yield outsized returns, noting Viva grew to a $37 billion market cap from just $3 million in capital and 25 employees.
Viva's model succeeded by vertically integrating layers of software (e.g., from content management to CRM) within specific industries (life sciences), a feat that was technically impossible in the client-server era but achievable in the cloud.
The speakers argue that building iconic businesses in 2025 requires less capital than in previous decades, with nimble teams capable of achieving significant scale without raising billions before an exit.
They caution founders against modeling themselves on past playbooks; instead, new successes will come from "brilliant teams" creating entirely new market layers or horizontal solutions rather than copying the vertical integration of the past.
The "Viva model" of capital efficiency is becoming more replicable as AI reduces the need for massive engineering teams, allowing startups to achieve rapid growth with leaner operations.

The $500 billion "Stargate" AI infrastructure commitment is analyzed as a mix of equity and debt, with approximately $100 billion representing risk capital; 75% of the capital is allocated to hardware (chips) and cooling, while only 25% covers physical infrastructure.
National security is cited as the primary driver for the massive capital deployment, with the US seeking to outpace China, while the commercial viability of such spending remains unproven for pure profit-seeking investors.
AI ROI measurement is defined by three core metrics for end-buyers: cost reduction, speed acceleration (time to value), and quality improvement; the best companies deliver on all three simultaneously.
Proprietary "moats" in AI are increasingly dependent on "coaching networks" where systems learn from the specific habits and expertise of a company's own employees, creating a perpetual learning loop that general-purpose models cannot replicate.
To prevent an "AI plateau" caused by the exhaustion of public training data, the next wave of growth will rely on accessing private commercial data behind firewalls and leveraging proprietary data generated by user interactions within specific business workflows.
Regulatory and legal challenges are shifting from pure cybersecurity to contractual protections, as corporations must prevent employees from inadvertently leaking IP into public or open-source models during interactions.

Traditional SaaS is not dead but has evolved; the recurring revenue model remains viable, but AI is becoming a core component of all workflows rather than a separate feature.
Pricing models are bifurcating between traditional per-seat licensing (hybridized with usage gates) and outcome-based or volume-based pricing (e.g., per ticket resolved or per application modernized).
Application-layer companies are increasingly hiring applied researchers to manage the integration of multiple models (open and closed source) to optimize cost, speed, and accuracy for specific use cases.
The firm views the "workflow" software category as enduring until human neural interfaces replace physical interaction entirely, maintaining that humans will always need a UI (chat, forms, or voice) to input data and receive outputs.

Emergence employs a continuous thesis development process where partners test hypotheses through investments, even if the thesis is not fully fleshed out initially, using the investment process itself to refine future strategy.
The firm's "five-way marriage" partnership structure relies on an equal partnership model where senior partners are developed internally over many years to ensure alignment and reduce the risk of turnover or conflicting incentives.
Recent high-profile "regretted" passes include Figma and Twilio; the firm missed Figma due to an over-reliance on revenue traction over user engagement metrics and missed Twilio by underestimating the "king maker" power of developer ecosystems.
The firm's most recent significant risk was a $40 million Series B investment in Bland AI, a voice agent platform that moves beyond text-based interaction to power complex, opinionated customer service experiences.
Future investment theses are focusing on "security and trust" guardrails for AI deployment, as well as the emergence of new UI/UX modalities (voice, vision) that replace traditional form-based inputs.