11-20-2025  Brokerage platforms enhanced by Artificial Intelligence (AI), such as Public.com, Robinhood, and emerging large language model (LLM)-based tools are altering the investment landscape. These platforms provide retail investors with real-time research, factor analysis, and conceptually generated portfolios at low or no cost. As these tools become more sophisticated, financial advisors face an increasingly common client question: “Why should I pay for your advice?”

Executive Summary

This white paper addresses the capabilities and limitations of AI brokerage platforms and explains the enduring value of professional financial advice. Importantly, it highlights why experienced active management, specifically the fundamental long/short discipline applied to The Future Fund Long/Short ETF (FFLS), is critical to pursuing the potential for consistent, risk-adjusted outcomes that AI tools are unlikely to replicate.

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1. Overview of AI-Enhanced Brokerage Platforms
AI brokerage platforms provide investors with natural-language interfaces that generate research summaries, thematic screens, valuation insights, and conceptual long/short ideas. Tools such as Public.com’s “Alpha” and “Generated Assets” can:

• • • Synthesize public data into narrative explanations
    • Screen securities by factor characteristics
    • Provide high-level long and short candidate lists
    • Offer market context and historical performance metrics

While these capabilities significantly enhance investor education, they are not designed to serve as regulated investment advice, nor are they capable of implementing institutional-quality portfolio management processes.

2. Structural Limitations of AI-Generated Portfolios
Despite their utility, AI-generated investment constructs have identifiable constraints:

• • • No risk management framework: AI cannot maintain beta neutrality,
      manage sector exposures, or apply volatility-based sizing
    • No fundamental interpretation: AI tools lack qualitative assessment of
      competitive dynamics, management credibility, and earnings quality
    • No portfolio discipline: AI cannot run ongoing hedging, rebalance strategies, or
      liquidity modeling
    • No regulatory oversight: AI does not assume fiduciary responsibility or adhere to
      40-Act requirements

These limitations reinforce that AI-generated baskets and conceptual long/short constructs are educational tools rather than investable strategies.

3. Advisor Value in an AI-Driven Marketplace
As AI tools increase investor access to information, advisors must clearly articulate their differentiated value. Key areas where advisors provide irreplaceable benefits include:

• • • Personalization: Aligning investment solutions with risk tolerance, financial goals, tax considerations, and liquidity needs
    • Behavioral coaching: Guiding clients through volatile markets and preventing emotionally driven mistakes
    • Complexity management: Implementing tax-loss harvesting, retirement income design, and multi-account coordination
    • Access to institutional strategies: Identifying and allocating to products requiring active oversight, including long/short ETFs, that retail AI tools cannot replicate

Advisors help clients translate information into actionable, risk-aware, and goal-aligned financial decisions.

4. The Need for Experienced Active Management in Long/Short Investing
Long/short strategies require continuous oversight, risk calibration, and disciplined portfolio construction. Effective implementation depends on:

• • • Fundamental assessment of earnings quality, competitive position, and financial strength
    • Interpretation of macroeconomic trends and sector rotations
    • Volatility-based position sizing and hedging
    • Monitoring factor exposures and maintaining balance over time
    • Liquidity analysis and execution discipline

Such activities demand professional experience, which, in our opinion, is unlikely to be automated through AI platforms currently available to retail investors.

5. How Megatrends Drive Long/Short Positioning in FFLS
A core differentiator of The Future Fund’s long/short approach is the team’s disciplined use of global megatrends to identify what they believe will be the structural winners and structural losers. Gary Black and David Kalis have collaborated since 2013, applying deep fundamental research and catalyst-driven modeling to evaluate how major secular forces reshape industries, business models, and competitive dynamics.

“Global forces are impacting businesses, economies, and individuals, and unleashing macro trends that will produce both winners and losers.”
- Gary Black, The Future Fund, Managing Partner

“Our investment process recognizes the disruptive innovation that is reshaping the economy, and it identifies the transformative companies that will displace legacy industry incumbents across several key sectors.”
- David Kalis, CFA, The Future Fund, Managing Partner

This philosophy is central to FFLS.
The strategy seeks long candidates with the potential to exploit enduring secular tailwinds, and short candidates that may be structurally disadvantaged or directly disrupted by those same forces.

This megatrend-driven long/short approach adds a dimension of forward-looking risk management that we believe is not replicated by AI-generated lists or rules-based thematic baskets. It requires professional judgment, qualitative research, and a nuanced understanding of how secular shifts translate into revenue durability, margin trajectory, and competitive advantage.

Under this framework, Gary and David evaluate potential longs and shorts through three lenses:

1. Secular tailwinds and headwinds: Identifying where macro forces create asymmetric growth and decay
2. Relative positioning: Determining which companies they believe possess
the business models, capital allocation, management credibility, and financial strength to capitalize on these opportunities
3. Disruption vs. disrupted: Pairing long innovators with short incumbents structurally challenged by technological shifts or cost pressures

6. The Role of Experienced Portfolio Managers in FFLS
FFLS reflects an institutional long/short investing approach guided by experienced portfolio managers. This includes:

• • • Applying a consistent, research-driven fundamental process
      Incorporating quantitative signals such as earnings revisions, momentum, and
      quality factors
    • Managing portfolio exposures with attention to volatility, beta, and sector balance
    • Assessing qualitative variables such as management credibility, capital allocation
      strategy, and competitive durability
    • Executing trades with liquidity awareness and discipline
    • Operating within a regulated framework with daily oversight and transparency
    • The deep experience of the portfolio managers is central in seeking to achieve repeatable long/short spread performance and navigating diverse market environments.

CONCLUSION
AI brokerage platforms offer powerful educational capabilities and will continue to influence investor expectations. However, they remain insufficient substitutes for the expertise, judgment, and risk management provided by professional financial advisors and experienced active managers.

Long/short strategies, in particular, require a sophisticated blend of quantitative insight, fundamental research, and disciplined implementation. The Future Fund Long/Short ETF (FFLS) leverages this institutional framework to deliver an actively-managed long/short program.

In an environment where information is increasingly commoditized, the differentiated value lies in interpretation, implementation, and experience—attributes that remain firmly in the domain of advisors and skilled portfolio managers.

About The Future Fund
The Future Fund is an independent investment firm focused on high-conviction, actively managed ETFs that invest in transformational companies shaping tomorrow’s economy. Led by seasoned investors Gary Black and David Kalis, the firm’s disciplined investment management strategies are available in accessible, transparent vehicles for investors.
Learn more about The Future Fund Long/Short ETF at futurefundetf.com
or contact Jeff Ulness, This email address is being protected from spambots. You need JavaScript enabled to view it. or 917-903-0360.

By Jeffrey M. Ulness, Global Head of Distribution

Definitions
Artificial Intelligence (AI)is the simulation of human intelligence in machines that are programmed to think, learn, and solve problems like humans.
Beta neutrality is a strategy where a portfolio is constructed to have a net beta of zero, meaning its value is unaffected by overall market movements

References
Public.com. Introducing Alpha: AI-Powered Investment Research (2024).
Public.com. Generated Assets: AI-Powered Research Tools (2024).
Robinhood Markets. Robinhood Research & AI Assisted Screens (2024).
FINRA. Artificial Intelligence (AI) in the Securities Industry (2023).
Morningstar. The Impact of AI on Investment Decision Support (2024).
Deloitte. AI-Augmented Research in Wealth Management (2023).
SEC Office of Investor Education. FINRA/SEC Guidance on AI & Automated Tools (2023).
FINRA Regulatory Notice 20-31. Use of Artificial Intelligence in the Securities Industry.
SEC. Investment Advisers Act of 1940 — Fiduciary Duty Interpretation.
CFA Institute. AI in Investing: Limitations in Portfolio Management (2023).
J.P. Morgan. AI and the Future of Portfolio Construction (2024).
MSCI Research. Factor Exposures & Risk Controls in Quant Models (2023).
Vanguard. Advisor’s Alpha Study (2023).
Morningstar. The Value of Good Advice (2022).
Cerulli Associates. U.S. Advisor Metrics (2024).
BlackRock. Foundations of Long/Short Equity Strategies (2023).
AQR Capital. The Mechanics of Long/Short Equity (2022).
Goldman Sachs. Why Long/Short Equity Requires Active Oversight (2024).
State Street. Risk Budgeting & Beta Control in Hedge Fund–Like Strategies (2023).
McKinsey. The Limits of AI in Fundamental Equity Analysis (2024).
Harvard Business Review. Why Human Judgment Still Matters in Finance (2023).
CFA Institute. Qualitative Assessment in Equity Analysis (2023).
SEC. ETF Rule 6c-11 — Transparency & Daily Holdings.
Investment Company Institute (ICI). ETF Liquidity & Trading Mechanics (2023).
State Street Global Advisors. ETF Market Structure & Transparency (2024).

IMPORTANT INFORMATION
Investing involves risk, including loss of principal. There is no guarantee that the Fund will achieve its investment objectives. In general, prices of equity securities are more volatile than those of fixed income securities. The prices of equity securities fluctuate in response to issuer-specific activities as well as factors unrelated to the fundamental condition of the issuer, including general market, economic and political conditions along with other factors. While the shares of ETFs trade on secondary markets, they may not readily trade in all market conditions and may trade at significant discounts in periods of market stress.

Long/Short investing: A long/short strategy seeks to balance investments by holding long positions in stocks expected to do well and short positions in stocks expected to underperform. Long investing involves buying an asset, such as stocks, with the expectation that its price will increase over time. The investor holds the asset for an extended period to benefit from potential price appreciation and any dividends or interest it may generate. Shorting, or short selling, involves borrowing shares of a stock and selling them at the current market price, with the expectation that the price will decline. The investor plans to buy back the shares at a lower price to return them to the lender.

Short selling involves the sale of securities borrowed from a third party. The short seller profits if the borrowed security’s price declines. If a shorted security increases in value, a higher price must be paid to buy the stock back to cover the short sale, resulting in a loss. The Fund may incur expenses related to short selling, including compensation, interest or dividends, and transaction costs payable to the security lender, whether the price of the shorted security increases or decreases. The amount the Fund could lose on a short sale is theoretically unlimited. Short selling also involves counterparty risk – the risk associated with the third party ceasing operations or failing to sell the security back.

The Fund is actively managed and is thus subject to management risk. The Advisor will apply its investment techniques and strategies in making investment decisions for the Fund, but there is no guarantee that its techniques will produce the intended results. Derivative risk is the possibility that an investor will not be able to sell a derivative position at a reasonable price or quickly. Leverage risk is the possibility that losses will be magnified when using borrowed money to increase the potential returns of an investment. This is because leverage can also multiply losses if the income from the asset is less than the financing costs or the value of the asset decreases. Limited history of operations: FFLS is a relatively new ETF with only two years history for investors to evaluate. Shareholders may pay more than NAV when buying Fund shares and receive less than NAV when selling Fund shares, because shares are bought and sold at current market prices. Shares of FFLS are bought and sold at market price (not NAV) and are not individually redeemed from the Fund. Brokerage commissions will reduce returns.

Investors should consider the investment objectives, risks, and charges and expenses of the Funds before investing. The FFLS prospectus contains this and other information about the Funds and should be read carefully before investing. The prospectuses may also be obtained by calling 877.466.7090.

The Future Fund Long/Short ETF is distributed by Northern Lights Distributors, LLC, member FINRA/SIPC. The Future Fund LLC is the investment advisor to the Funds, and is not affiliated with Northern Lights Distributors, LLC.