Why Gen Z matters (and how they invest differently)

Gen Z is already reshaping how financial advice must be delivered:

• In the U.S., 20% of young investors have used a robo-advisor, while 26% have used a cryptocurrency exchange. (YouGov: Inside the Minds of Gen Z and Millennial Investors)

• Trust, cost, and accessibility dominate their decision criteria: 57% of young investors say service fees are important, and 68% prioritize trustworthiness. (YouGov)

• Many younger investors are bypassing homeownership (due to affordability constraints) and turning directly toward investing to build wealth. (Business Insider: Young people locked out of the housing market are turning to the stock market)

Implication: For robos to win Gen Z, they must serve digital natives who expect transparency, low friction, and alignment with personal values.

Macro tailwinds and market opportunity

• The global robo-advisor market is projected to grow strongly. One projection sees the market expand at a CAGR of ~30.5% from 2024 to 2030. (Grand View Research: Robo Advisory Market Report)

• Another report forecast the U.S. robo sector’s assets under management to surpass $520B by 2025, up from ~$350B in 2023. (CoinLaw: Robo Advisors Market Statistics)

• The rise of AI is accelerating the shift. As noted in Reuters, 50% of global investors say they would use AI tools for portfolio decisions, and 13% already do—propelling growth in automated advice. (Reuters: “AI fuels boom in robo-advisory market”)

These tailwinds give new entrants a wide runway—if they can out-execute incumbents and deliver what Gen Z expects.

Six key evolutions robo-advisors must make

1. Democratized direct indexing and customization

Direct indexing—owning individual securities vs ETFs—lets users personalize by excluding sectors, tilting toward factors, or applying value filters. It also enables deeper, security-level tax-loss harvesting. (Advisor Perspectives: Revisiting Direct Indexing in 2025)

Importantly, 76% of financial advisors say they currently use or plan to use direct indexing in the next 12 months. (FTSE Russell: Direct Indexing Survey 2025)

To serve Gen Z, robos must make direct indexing scalable and accessible at lower balances (via fractionalization, efficient execution, tax overlay algorithms).

2. Fractional shares + recurring micro-funding

Younger investors often contribute smaller sums regularly. Fractional share support is essential for enabling these micro-investments in expensive securities. Bundling with auto-topups, roundups, or “carryover cash” systems helps maintain engagement.

3. Explainable AI with strong guardrails

AI can power recommendations (e.g., which tilt, when to rebalance), but it must be paired with transparency. Users should see the why behind each suggestion, view decision logs, and override or toggle recommendations.

A new framework in “Robo-Advisors Beyond Automation” argues for fiduciary duty, adaptive personalization, technical robustness, fairness, and auditability as core AI design principles. (Feng, Li, Liu, “Robo-Advisors Beyond Automation”)

4. Reinforcement learning and dynamic allocation

Rather than static portfolios, next-gen systems may employ reinforcement learning to adjust allocations in real time across regimes. For example, models like G-Learner allow welfare-based dynamic investing. (Dixon & Halperin, G-Learner & GIRL RL models)

This enables portfolios that evolve with volatility, momentum, or macro signals—while staying within risk constraints.

5. Alternative exposure and modular add-ons

Gen Z is more willing to experiment with crypto, tokenized real estate, carbon credits, or ESG thematic baskets. Robo platforms should support optional “tilt modules” for these, gating liquidity or exposure caps to balance risk.

6. Behavioral design, feedback loops, and engagement

The platform must act more like a wellness app than a textbook:

• Nudges (e.g., “idle cash waiting—want to invest?”)

• Progress reports (taxes saved, drift corrected)

• Visual goal trackers (e.g. “By 2030, this tilt path gives you $X more”)

• Micro-learning modules embedded in UI (definitions, trade-offs, sources)

Without these, many Gen Z users may sign up then drift away.

Headwinds, risk factors & regulatory pressures

• Model risk & overfitting. Reinforcement systems can behave poorly during regime shifts unless robustly tested.

• Operational complexity. Managing many individual holdings per account, executing trades, batching, tax lots—all scales nonlinearly.

• Trust & transparency. If users feel suggestions are “black boxes,” adoption suffers.

• Regulatory scrutiny. As robo firms deploy AI and shift to discretionary models, regulators emphasize standard of conduct, explainability, and liability (see Schwarcz’s “Regulating Robo-Advisors” paper). (Schwarcz, WLU Law Review: Regulating Robo-Advisors)

• Fiduciary liability. Advisors must ensure algorithmic advice aligns with client best interest—especially for younger, inexperienced investors.

Why Surmount is especially well-positioned

When you align Gen Z’s expectations with the technical and regulatory demands of next-gen robo, Surmount’s design naturally fits in this sweet spot:

• Modular strategies with built-in guardrails. Users can choose base templates then tweak (tilts, exclusions, ESG screens), but all changes stay within diversification and risk constraints.

• Transparent rationale and logs. Every suggestion has an explainable rationale, metadata, and sensitivity view—so users never feel blind.

• AI + rule architecture. The AI assists in discovery and scenario comparison, but execution always passes a rules engine and is auditable.

• Fractional + micro support. Users can invest tiny amounts in expensive components.

• Optional modules for alternative exposure. Select crypto or thematic tilts with clear limits and impact visuals.

• Ongoing engagement features. Nudges, performance summaries, learning modules, and visual goal progress keep users active and informed.

• Built-in compliance and audit readiness. From day one, Surmount’s architecture anticipates regulation: versioning, logs, guardrails, audit trails.

With these elements, Surmount does more than keep up—it leaps ahead in capturing Gen Z’s tribal loyalty.

Ending Note

Gen Z is not just another generational cohort—they are digital-first, value-driven, transparency-demanding investors who will set the rules for the future of advice.

Robo-advisors that don’t evolve—customizable direct indexing, explainable AI, engagement design, modular alternatives, and compliance by default—will be left behind. Surmount’s architecture and ethos already align with these demands. Try Surmount now — create a custom portfolio, explore tilts, simulate tax effects, and interact with explainable AI.