Introduction: The AI Revolution in Stock Trading

The financial world is undergoing a paradigm shift, propelled by the relentless advancement of artificial intelligence. This isn’t just incremental change; it’s a fundamental reshaping of how markets operate, how investment decisions are made, and how financial institutions function. Among the most transformative developments is the rise of generative AI, a technology poised to revolutionize stock trading and portfolio management. Unlike traditional algorithms that rely on pre-programmed rules, generative AI can learn complex patterns from vast datasets and create entirely new content, from market predictions to optimized trading strategies.

For Overseas Filipino Workers (OFWs) diligently building emergency funds and seeking long-term growth, understanding these shifts is no longer optional—it’s crucial for safeguarding and growing their hard-earned savings. This technology has the potential to democratize access to sophisticated investment tools, offering OFWs a level playing field previously dominated by institutional investors. This article delves into the intricacies of generative AI in finance, offering a comprehensive look at its capabilities, benefits, risks, and future implications, specifically tailored to the needs and interests of individuals seeking to navigate the evolving investment landscape.

The impact of generative AI is already being felt across the financial spectrum. Hedge funds are using it to identify subtle market inefficiencies and develop complex trading strategies. Robo-advisors are leveraging its power to personalize portfolio recommendations for individual investors, aligning investment choices with specific risk tolerances and financial goals. Even established financial institutions are integrating generative AI into their existing workflows to automate tasks, improve risk assessment, and enhance decision-making. According to a recent report by McKinsey, AI-driven investment strategies have the potential to outperform traditional benchmarks by a significant margin, creating substantial value for both individual and institutional investors.

This potential for enhanced returns is particularly relevant for OFWs who are often seeking to maximize the impact of their investments. Beyond simply automating existing processes, generative AI is opening up entirely new possibilities. Imagine an AI that can not only predict market movements but also explain the rationale behind its predictions in clear, understandable language. Or consider an AI that can generate synthetic market data to test the robustness of trading strategies under various economic scenarios.

These are just a few examples of how generative AI is pushing the boundaries of what’s possible in finance. However, it’s crucial to approach this technology with a balanced perspective. While the potential benefits are substantial, there are also inherent risks, including data bias, model limitations, and the potential for unintended consequences. Understanding these risks is paramount for anyone seeking to leverage the power of generative AI in their investment strategies. Furthermore, the rise of generative AI is driving innovation in the Fintech sector, leading to the development of new tools and platforms that empower individual investors.

AI-powered trading platforms are becoming increasingly accessible, offering sophisticated analytics, personalized insights, and automated trading capabilities. These platforms can help OFWs make more informed investment decisions, manage their portfolios more effectively, and potentially achieve higher returns. As the Fintech landscape continues to evolve, staying informed about the latest developments in AI-driven investment tools will be essential for OFWs seeking to optimize their financial strategies. This article will provide valuable insights into the key trends shaping the future of AI in finance and offer practical guidance on how to incorporate these technologies into your investment approach. Finally, it’s important to recognize that the integration of generative AI into finance is not just about technology; it’s about empowering individuals to take control of their financial futures. For OFWs, who often face unique financial challenges and opportunities, understanding and leveraging these technologies can be a game-changer. This article serves as a roadmap, providing the knowledge and insights needed to navigate the exciting and rapidly evolving world of AI-driven finance.

Understanding Generative AI: Beyond Traditional Algorithms

Generative AI stands apart from traditional AI algorithms by its ability to create new content rather than simply analyzing existing data. While traditional AI excels at tasks like classifying transactions as fraudulent or predicting customer churn, generative AI ventures into uncharted territory. It learns underlying patterns from vast datasets and then uses this knowledge to generate entirely new outputs, such as potential market scenarios, optimized portfolio structures, or even synthetic trading data for backtesting. This creative capacity has profound implications for the financial world, opening doors to previously unimaginable investment strategies and risk management techniques.

Consider, for example, a generative AI model trained on decades of market data. This model could not only predict potential price movements but also generate a range of possible future market conditions, allowing investors to stress-test their portfolios against various economic scenarios. This goes far beyond traditional algorithmic trading, which typically relies on pre-programmed rules and historical data analysis. Generative AI empowers investors to anticipate and prepare for a wider range of possibilities, potentially mitigating risks and uncovering hidden opportunities.

For instance, by simulating market downturns or black swan events, investors can refine their strategies and optimize their portfolio allocations for enhanced resilience. One of the key technologies driving this revolution is deep learning, a subset of machine learning that utilizes artificial neural networks with multiple layers. These networks mimic the human brain’s structure, enabling them to process complex data and identify intricate patterns that traditional algorithms might miss. In finance, this translates to more accurate predictions, more nuanced portfolio optimization, and a deeper understanding of market dynamics.

For example, a deep learning model can analyze news sentiment, social media trends, and economic indicators to generate a holistic view of market sentiment, informing investment decisions with a level of granularity previously unattainable. This ability to synthesize diverse data sources and extract meaningful insights is a hallmark of generative AI and a key driver of its transformative potential in finance. Moreover, generative AI can be instrumental in developing sophisticated trading algorithms. By generating synthetic market data, it can create realistic but hypothetical scenarios to train and refine trading algorithms, enhancing their performance and robustness in real-world markets.

This ability to learn and adapt in simulated environments offers a significant advantage over traditional backtesting methods, which are limited by the availability of historical data. Furthermore, the application of generative AI extends beyond stock trading to encompass broader portfolio management strategies. By generating optimized portfolio allocations based on individual investor’s risk tolerance, financial goals, and market conditions, generative AI can personalize investment strategies at scale. Imagine an AI-powered platform that dynamically adjusts portfolio holdings based on real-time market data and individual investor preferences.

This level of personalized portfolio management, previously accessible only to high-net-worth individuals, could become commonplace thanks to the advancements in generative AI. This technology has the potential to democratize access to sophisticated financial advice and empower individual investors to achieve their financial objectives more effectively. However, it’s crucial to acknowledge the potential drawbacks and ethical considerations associated with generative AI in finance. Data bias, model explainability, and the potential for misuse are critical challenges that need to be addressed to ensure responsible and beneficial implementation of this powerful technology.

AI’s Multifaceted Role in Stock Trading

Generative AI’s role in stock trading is multifaceted, transforming how investors analyze markets and manage portfolios. It goes beyond traditional algorithmic trading by leveraging the power of deep learning and natural language processing to gain a more nuanced understanding of market dynamics. One crucial application is sentiment analysis, where AI algorithms process vast amounts of unstructured data, including news articles, social media posts, and financial reports, to gauge investor attitudes and predict potential market trends.

For instance, BloombergGPT, a large language model specifically trained on financial data, can analyze news sentiment with greater accuracy than traditional methods, providing valuable insights into market direction. This capability allows investors to anticipate market reactions to news events and adjust their strategies accordingly. Furthermore, generative AI excels in generating trading signals by identifying complex patterns in historical data that might be missed by human analysts. These AI-powered algorithms can analyze millions of data points, recognizing subtle correlations and predicting future price movements with increased accuracy.

For example, platforms like Kavout are using AI to identify patterns and generate trading signals for specific stocks, enabling investors to make more informed decisions. This data-driven approach minimizes emotional biases and allows for more systematic trading strategies. Moreover, these AI models can be backtested against historical data to assess their effectiveness and refine their predictive capabilities. Risk assessment is another area where generative AI is making significant contributions. By simulating various market conditions, including black swan events, AI can evaluate portfolio vulnerabilities and stress-test investment strategies.

This allows investors to understand how their portfolios might perform under different scenarios and adjust their risk exposure accordingly. Platforms like Kensho are using AI to analyze market risks and provide insights into portfolio resilience. This proactive approach to risk management is crucial for navigating uncertain market conditions and protecting investments. Generative AI also plays a vital role in portfolio optimization. By dynamically adjusting asset allocations based on predicted market changes and individual investor risk profiles, AI can create personalized portfolios designed to maximize returns while minimizing risk.

Robo-advisors, like Wealthfront and Betterment, are already using AI to create and manage personalized portfolios for individual investors. This personalized approach to portfolio management ensures that investment strategies are aligned with individual financial goals and risk tolerances. Finally, the development of explainable AI (XAI) is addressing concerns about the “black box” nature of some AI algorithms. XAI aims to provide insights into how AI models arrive at their decisions, increasing transparency and building trust among investors. This is particularly important in the financial industry, where understanding the rationale behind investment decisions is paramount. As XAI continues to evolve, it will further enhance the adoption of AI-driven trading strategies by providing greater clarity and control.

Real-World Examples: AI-Driven Success Stories

Several financial institutions are already leveraging generative AI with promising results, marking a significant shift in how investment strategies are formulated and executed. For instance, sophisticated hedge funds are deploying AI models, powered by machine learning, to analyze vast datasets encompassing market trends, economic indicators, and even alternative data sources like satellite imagery and social media sentiment. These models can identify mispriced assets with far greater speed and accuracy than traditional methods, leading to superior returns.

This is not just about faster data processing; it’s about the ability of generative AI to create novel insights and trading strategies that would be impossible for human analysts to conceive. The core of this advancement lies in the ability of these systems to learn from data patterns and generate entirely new scenarios, predicting potential market shifts with remarkable precision. A recent study by a leading financial technology firm demonstrated the efficacy of AI-driven trading strategies, revealing that such approaches outperformed traditional benchmarks by an average of 15% over a two-year period.

This isn’t just a marginal improvement; it’s a substantial increase in performance, showcasing the transformative potential of generative AI in stock trading. These AI systems, often employing complex algorithmic trading models, are not only optimizing for returns but also minimizing risk by proactively anticipating potential market downturns and adjusting portfolio allocations accordingly. The study further highlighted that the AI’s ability to adapt to changing market conditions was a key factor in its superior performance, demonstrating a level of agility that traditional human-led strategies often lack.

These results underscore the growing recognition of AI as a pivotal tool in quantitative finance. Beyond just hedge funds, the integration of generative AI is rapidly permeating other areas of the financial sector, including asset management and robo-advisors. For example, some firms are using AI to generate personalized investment portfolios tailored to individual risk profiles and financial goals. These systems utilize complex algorithms to assess a client’s investment horizon, risk tolerance, and financial situation, then create an optimal portfolio mix that maximizes returns while minimizing risk.

This level of customization, previously only available to high-net-worth individuals, is now becoming accessible to a wider range of investors through Fintech platforms. This democratization of sophisticated investment strategies is one of the key ways AI is transforming the financial landscape. Furthermore, the application of generative AI extends to risk management and fraud detection, two critical areas for any financial institution. AI models can analyze vast amounts of transaction data in real-time, identifying anomalies and potential fraudulent activities that might go unnoticed by human analysts.

This capability not only helps in preventing financial losses but also enhances the security and integrity of the financial system. The speed and precision with which AI can process this data is a significant advantage, allowing for faster responses to potential threats and greater overall stability. The use of AI in this context highlights its multifaceted role, extending beyond just investment strategies to encompass broader operational efficiency and security. However, while the successes are promising, it’s crucial to acknowledge that the development and deployment of AI in stock trading is an ongoing process.

Challenges such as data bias and model limitations need to be continuously addressed. Ongoing research is focused on creating more robust and transparent AI models that can mitigate these risks. The focus is not just on improving performance but also on ensuring ethical and responsible use of AI in finance. This involves developing regulatory frameworks and industry best practices to guide the implementation of these technologies. The future of AI in finance will depend on the ability of the industry to harness its power responsibly and ethically, ensuring that its benefits are accessible to all.

Benefits and Drawbacks: A Balanced Perspective

The integration of AI into stock trading presents a compelling value proposition with several key advantages. Enhanced predictive accuracy, stemming from AI’s ability to process vast datasets and identify subtle patterns often missed by human analysts, allows for more informed investment decisions. This translates to potentially higher returns and reduced risks through sophisticated modeling that accounts for a wider range of market variables and scenarios. For example, AI-powered platforms can backtest trading strategies against historical data with unparalleled speed and precision, optimizing portfolios for various market conditions and risk tolerances.

This is particularly beneficial for Overseas Filipino Workers (OFWs) seeking to maximize their returns while managing risk effectively within their investment portfolios. However, a balanced perspective is crucial, as the transformative potential of AI in finance is accompanied by certain limitations and challenges. Data bias, a significant concern, arises when AI algorithms are trained on incomplete or skewed datasets, potentially leading to inaccurate or discriminatory outcomes. For instance, an AI model trained primarily on historical data from bull markets might struggle to adapt to bearish market conditions, exposing investors to unforeseen risks.

Model limitations, where AI algorithms may fail to anticipate or accurately interpret unprecedented market events like ‘black swan’ occurrences, are another critical consideration. The 2008 financial crisis, largely unforeseen by traditional models, serves as a stark reminder of the inherent limitations in predicting complex market dynamics. Furthermore, the potential for AI to exacerbate existing market inequalities is a valid ethical concern. If access to advanced AI-driven trading tools is concentrated among a select group of investors, it could create an uneven playing field, further widening the gap between institutional and retail investors.

Algorithmic trading, while offering speed and efficiency, also carries the risk of flash crashes and market instability if not carefully monitored and regulated. Therefore, for OFWs and other investors, understanding these limitations and approaching AI-driven trading strategies with a critical and informed mindset is paramount. Due diligence, continuous learning, and a focus on risk management are essential for navigating this evolving landscape. Seeking guidance from qualified financial advisors and diversifying investment strategies can further mitigate potential risks associated with AI-driven trading. The evolving landscape of Fintech constantly introduces new tools and platforms, and staying informed about their capabilities and limitations is crucial for harnessing the true potential of AI in investment strategies.

The Future of AI in Finance: Emerging Trends

The future of AI in finance is rapidly evolving, promising a new era of personalized portfolio management, automated trading, and a redefined role for human analysts. AI’s ability to tailor investment strategies to individual risk profiles and financial goals will become increasingly sophisticated. Imagine an AI system that not only considers your risk tolerance and investment horizon but also dynamically adjusts your portfolio based on real-time market conditions and your evolving financial needs. This level of personalization, driven by generative AI’s capacity to learn and adapt, will empower investors with highly customized and potentially more effective investment strategies.

For instance, platforms are emerging that utilize AI to create personalized portfolios aligned with specific ethical and sustainable investment goals, catering to the growing demand for socially responsible investing. Automated trading strategies, powered by sophisticated AI algorithms, are poised to become more prevalent. These algorithms can analyze vast datasets, identify complex patterns, and execute trades at speeds far exceeding human capability. This shift towards algorithmic trading requires robust oversight and regulatory frameworks to mitigate potential risks, such as market manipulation and unintended consequences of high-frequency trading.

Regulators are actively exploring new rules and guidelines to ensure the responsible development and deployment of AI-driven trading systems. Examples include circuit breakers that halt trading in volatile conditions and stress tests designed to evaluate the resilience of AI algorithms in extreme market scenarios. The role of human analysts will undergo a significant transformation, shifting from manual data analysis to model validation, ethical oversight, and strategic decision-making. Rather than being replaced, human expertise will be augmented by AI, allowing analysts to focus on higher-level tasks such as interpreting AI-generated insights, assessing the ethical implications of AI-driven decisions, and developing innovative investment strategies.

This collaboration between humans and AI will be crucial for navigating the increasingly complex financial landscape. For example, analysts can leverage AI to identify potential biases in data used to train trading algorithms, ensuring fairer and more accurate outcomes. They can also play a critical role in developing and implementing robust risk management strategies that account for the unique challenges posed by AI-driven trading. These advancements will require both financial institutions and individual investors to adapt and acquire new skills.

Understanding the fundamentals of AI, machine learning, and algorithmic trading will become essential for navigating the evolving financial ecosystem. Educational resources, online courses, and professional certifications are becoming increasingly available to bridge the knowledge gap and empower individuals and institutions to effectively leverage AI in finance. Moreover, the demand for specialized roles, such as AI ethics specialists and algorithm developers, is expected to grow significantly, creating new career opportunities in the financial sector. Finally, the increasing integration of AI in finance raises important questions about data privacy and security. As AI systems rely on vast amounts of data to learn and make decisions, protecting sensitive financial information becomes paramount. Robust cybersecurity measures, data anonymization techniques, and ethical data governance frameworks are crucial for safeguarding individual privacy and maintaining trust in the AI-driven financial system. The development and implementation of these safeguards will be essential for ensuring the long-term sustainability and responsible growth of AI in finance.

Actionable Insights: Incorporating AI into Your Strategy

For Overseas Filipino Workers (OFWs) seeking to integrate AI into their investment approaches, education is paramount. A strong foundation in both AI and finance is crucial for navigating this evolving landscape. Begin with exploring online resources and courses that offer insights into AI algorithms, machine learning in finance, and portfolio management strategies. Reputable platforms like Coursera, edX, and Udacity provide a wealth of knowledge, from introductory courses to specialized programs in quantitative finance and algorithmic trading.

Understanding the underlying principles of these technologies empowers OFWs to make informed decisions and critically evaluate AI-driven investment advice. Beyond theoretical knowledge, practical experience is essential. Start by utilizing AI-powered financial analysis tools, many of which offer free or trial versions. These tools can provide data-driven insights into market trends, company performance, and portfolio optimization. Familiarize yourself with different platforms and compare their features, focusing on those that align with your individual investment goals and risk tolerance.

As your understanding grows, gradually explore more advanced AI trading platforms. These platforms often incorporate sophisticated algorithms, including generative AI, to identify trading signals, automate trades, and manage portfolio risk. Before committing real capital, rigorous backtesting is crucial. Backtesting involves simulating the performance of an AI-driven trading strategy using historical market data. This process helps assess the potential effectiveness of the strategy and identify potential weaknesses. Several AI trading platforms offer backtesting functionalities, allowing OFWs to experiment with different parameters and refine their strategies before deploying them in live markets.

Remember that past performance is not indicative of future results, but backtesting provides a valuable framework for evaluating the potential risks and rewards of an AI-driven approach. Diversification remains a cornerstone of prudent investing, even in the age of AI. While AI can offer valuable insights and potentially enhance returns, it’s essential to avoid over-reliance on any single technology or strategy. Diversify your portfolio across different asset classes, sectors, and geographies to mitigate risk and enhance long-term growth potential.

Consider integrating AI-driven insights with traditional investment strategies, leveraging the strengths of both approaches. For example, use AI-powered tools to analyze market sentiment and identify potential investment opportunities, but maintain human oversight in making final investment decisions. Furthermore, understanding the limitations of AI is crucial. AI models, particularly those based on machine learning, are susceptible to biases present in the data they are trained on. This can lead to skewed or inaccurate predictions, especially in dynamic and unpredictable markets.

Be aware of the potential for “black box” scenarios, where the decision-making process of the AI is opaque. Transparency in how the AI arrives at its recommendations is critical for building trust and understanding the underlying rationale behind its suggestions. Finally, remain vigilant about the evolving regulatory landscape surrounding AI in finance. As these technologies become more prevalent, regulatory bodies are likely to implement new rules and guidelines to ensure transparency, fairness, and investor protection. Staying informed about these developments is vital for navigating the complexities of AI-driven investing.

The Broader Impact of AI in Finance

The integration of generative AI into stock trading and portfolio management represents a paradigm shift, transforming how financial decisions are made. It’s not merely a technological advancement; it’s a fundamental reshaping of the investment landscape. While increased accuracy and reduced risk are substantial benefits, a balanced perspective is crucial, particularly for Overseas Filipino Workers (OFWs) who navigate unique financial complexities. Understanding these advancements is paramount for OFWs seeking to enhance their financial security and growth.

By staying informed and adopting a cautious yet forward-thinking approach, they can harness AI’s power to achieve their financial goals. Generative AI’s ability to analyze vast datasets, identify intricate patterns, and generate predictive models offers unprecedented opportunities. For instance, traditional quantitative finance relies on historical data and established algorithms. Generative AI, however, can create new models that adapt to evolving market conditions, potentially uncovering hidden alpha and optimizing portfolio diversification. This dynamic approach contrasts sharply with static models, offering the potential for more robust and responsive investment strategies.

Imagine an AI that not only predicts market fluctuations but also generates synthetic data to stress-test portfolios under various economic scenarios, preparing investors for unforeseen events. This level of sophisticated analysis empowers investors, particularly OFWs managing their hard-earned savings, to make more informed decisions. The implications for algorithmic trading are equally profound. Generative AI can design and refine complex trading algorithms, automating buy and sell decisions based on real-time market data and predictive analytics. This automation can significantly enhance trading efficiency and potentially improve returns.

However, it also introduces new challenges, such as the risk of algorithmic bias and the need for robust oversight. Regulators are increasingly focusing on these issues, developing frameworks to ensure responsible AI implementation in finance. For OFWs exploring AI-driven trading platforms, understanding these regulatory developments and choosing platforms with transparent and ethical AI practices is essential. Furthermore, generative AI’s capacity to personalize investment strategies is transformative. By analyzing individual risk profiles, financial goals, and market conditions, AI can tailor portfolios to meet specific investor needs.

This personalized approach moves beyond traditional robo-advisors, offering bespoke investment solutions previously accessible only to high-net-worth individuals. For OFWs with diverse investment objectives, ranging from building emergency funds to planning for retirement, this level of personalization can be invaluable. Finally, the rise of AI in finance necessitates a shift in the role of human analysts. Rather than being replaced, human expertise becomes even more critical in areas such as interpreting AI-generated insights, managing risk, and ensuring ethical considerations are integrated into AI-driven strategies. The future of finance will likely involve a collaborative partnership between humans and AI, where human intelligence complements and guides the power of artificial intelligence. For OFWs, this means focusing on developing financial literacy and understanding the capabilities and limitations of AI, empowering them to leverage these tools effectively and navigate the evolving financial landscape.

Conclusion: Embracing the AI-Driven Future of Finance

Generative AI is not merely an incremental improvement but a fundamental paradigm shift in the financial landscape, offering unprecedented opportunities for enhanced stock trading and portfolio management. Its capacity to synthesize vast datasets and generate novel insights surpasses traditional analytical methods, providing a significant edge in identifying potential market inefficiencies and predicting price movements. While risks and limitations, such as algorithmic bias and model opacity, undeniably exist, the potential for improved accuracy and returns is compelling, making it an essential area of exploration for both seasoned investors and those new to the market.

For Overseas Filipino Workers (OFWs), who are often seeking to maximize the value of their hard-earned savings, a critical and informed perspective on these technologies is paramount, allowing them to leverage AI-driven tools while mitigating potential pitfalls. The integration of generative AI into investment strategies is reshaping how portfolios are constructed and managed. Algorithmic trading, powered by sophisticated machine learning models, can now execute trades with speed and precision that were previously unattainable. These algorithms analyze real-time market data, news sentiment, and macroeconomic indicators to identify optimal entry and exit points, thereby potentially enhancing returns and reducing risks.

For instance, AI-driven portfolio rebalancing tools can automatically adjust asset allocations based on pre-defined risk parameters and market conditions, ensuring that investment portfolios remain aligned with individual financial goals. This level of automation and precision is particularly beneficial for OFWs who may not have the time or expertise to actively manage their portfolios. Furthermore, the application of generative AI extends beyond simple trading signals to encompass more complex financial modeling and risk assessment. AI algorithms can simulate various market scenarios, including black swan events, to stress-test portfolios and identify potential vulnerabilities.

This capability allows investors to proactively adjust their strategies and mitigate potential losses. In the realm of Fintech, AI is also facilitating the development of personalized investment platforms that cater to individual risk profiles and financial goals. These platforms use machine learning to analyze user data and provide customized investment recommendations, making sophisticated investment strategies more accessible to a wider audience. The rise of these AI-driven tools is democratizing finance, offering opportunities for OFWs and other investors to participate in markets with greater confidence and effectiveness.

However, it’s crucial to acknowledge that the effective use of AI in stock trading and portfolio management requires a deep understanding of both the technology and the underlying financial principles. Data bias, a common challenge in machine learning, can lead to inaccurate or unfair outcomes if not addressed carefully. Therefore, investors must critically evaluate the AI tools they use and ensure that they are trained on unbiased and representative data sets. Continuous learning and adaptation are also essential, as the financial markets are constantly evolving and AI algorithms must be updated to remain effective.

OFWs, in particular, should prioritize education and seek out reputable resources to develop a solid foundation in both AI and finance. This includes understanding the nuances of quantitative finance and the importance of backtesting any AI-driven strategy before deploying it with real capital. Ultimately, the future of finance is inextricably linked to the continued advancement of AI. As generative AI becomes more sophisticated, its role in stock trading, portfolio management, and financial planning will only increase.

For OFWs building their financial futures, embracing these technologies with a critical and informed perspective is not just an option; it’s a necessity. By combining AI-driven tools with sound financial principles, they can navigate the complexities of the market and achieve their long-term financial goals. This involves actively seeking educational opportunities, staying abreast of the latest developments in AI and Fintech, and adopting a cautious yet optimistic approach to this transformative technology. The era of AI-powered finance is upon us, and those who are prepared to adapt and learn will be best positioned to succeed.