Predicting software engineering’s future: Ensemble learning in action
For Developers

February 15, 2024

Predicting software engineering’s future: Ensemble learning in action

Imagine for a moment you had a crystal ball to see the future of your career. What questions would you ask? Would you consider relocating to a place brimming with opportunities? Or would you consider changing employers? Envisioning the future can be exciting and daunting, especially in dynamic fields like remote work and software engineering. Further, it would be incredibly empowering if you could confirm your thoughts through a panel of experts, each offering their opinion to make a more accurate and informed decision.

We delve into it as a strategic exercise. Using statistics, we grip the future of remote work and software engineering by proposing three possible scenarios: (1) The Rise of the Digital Remote Economy, (2) The AI Paradigm Shift, and (3) The Cybersecurity Imperative. Each scenario paints a different picture of the future, influenced by various factors such as societal trends, geopolitical developments, and educational changes. 

To gauge our predictions' accuracy, we suggest adopting ensemble learning. This approach, in a sense, acts like a panel of experts. Ensemble learning is a machine learning technique that merges multiple models to boost overall predictive performance. By leveraging the strengths of each individual model, we can significantly enhance our predictive power. 

We're excited about this model's potential and love to hear your thoughts. Moreover, we invite you to join us to contribute to its practical application. Whether you're a data scientist, a software engineer, a strategist or anyone else intrigued by the future of remote work and software engineering, we're open to your insights and collaboration. Let's shape the future of this industry together! 

Crafting Future Scenarios

We draw inspiration from Shell’s approach to long-term strategic planning, defining three potential scenarios. Each scenario reflects a distinctive future shaped by various driving forces. Along with each scenario, we identify key features - variables that would significantly impact that future.
Digital economy: key defining trends

1. The Rise of the Digital Remote Economy

We imagine a future where remote work becomes the default, fuelled by technological advances and societal changes. Nations worldwide compete to attract digital nomads, offering them tax breaks and other incentives.

Geopolitical Developments: As remote work becomes the norm, governments worldwide may loosen immigration restrictions for digital workers, increasing mobility and competition for skilled workers. Nations might also pass international treaties and agreements on digital work taxation and labour rights, necessitating careful navigation and understanding of these complexities. Portugal has established a special "Non-Habitual Residency" category that offers digital nomads tax reductions. This policy has made it an attractive hub for remote workers, boosting local economies and causing other countries to consider similar strategies.

Societal Trends: This future could increase the societal value placed on flexibility, autonomy, and work-life balance. Cities and countries might cater to digital nomads with robust digital infrastructures, co-working spaces, and communities, reshaping urban landscapes. There has been a boom in coworking spaces catering to digital nomads in Bali, Indonesia. These spaces often provide work facilities and host social events and wellness activities, reflecting a value shift towards work-life integration and community.

Education: With a distributed workforce, the importance of digital literacy would rise significantly. Education systems may shift to more remote learning strategies, and continuous learning or “upskilling” would become integral parts of a career. Universities like MIT offer "MicroMasters" programs, shorter, more focused versions of traditional degrees that can be completed remotely. This flexibility caters to the increasing demand for continuous upskilling in the digital economy.


Countries with strong digital infrastructure and attractive living conditions could see increased demand as companies and workers choose locations based on lifestyle rather than office location. Meanwhile, countries with high levels of technical education but lower living standards might become significant suppliers of digital nomads.

In this scenario, remote work becomes the norm, with countries competing to attract digital nomads. Key features include digital infrastructure quality, remote work-friendly policies, cost of living, and overall quality of life.

Demand for Software Engineers = α * Digital Infrastructure Quality + β * Remote Work Policies + γ * Cost of Living + δ * Quality of Life + ε

2. The AI Paradigm Shift

In the future, significant advances in AI and machine learning will automate many routine coding tasks. The role of the software engineer shifts towards overseeing and managing these AI systems.

Geopolitical Developments: Advances in AI could lead to international competition and tension, with nations vying to be the leading AI superpower. Governments might implement regulations to manage AI’s societal impacts, influencing how people use AI in the workplace. China has announced ambitious plans to become the world leader in AI by 2030. Such a decision drives significant investment in AI research, potentially leading to regulatory changes to support AI development and integration.

Societal Trends: As AI becomes integrated into everyday life, societal debates around the ethics of AI and data privacy could intensify. There might be increased public pressure on companies to use AI responsibly. In response to growing public concern about AI ethics, companies like Google have established AI ethics boards to oversee their AI development and ensure responsible use. These boards address concerns such as bias in AI algorithms and the potential impacts of automation on job displacement. 

Education: The educational focus could shift towards understanding AI, machine learning, and data science, emphasising ethics. Soft skills, like problem-solving, creativity, and emotional intelligence, become increasingly important as AI takes over more routine tasks. Universities like Stanford offer interdisciplinary programs combining computer science and ethics. These programs aim to prepare students to make informed decisions about the ethical implications of AI and other technologies.


Countries leading in AI and machine learning research, like the US and China, might see increased demand for software engineers with AI skills. However, the global nature of AI technologies means that the supply could be international, with remote workers contributing from anywhere in the world.

This scenario sees a significant shift in demand towards AI-specialized software engineers. Key features are AI adoption rate, industrial automation level, and AI education and training quality.

Demand for Software Engineers = α * AI Adoption Rate + β * Industry Automation Level + γ * Quality of AI Education and Training + ε

3. The Cybersecurity Imperative

In the future, a series of high-profile cyberattacks will lead to an intense global focus on cybersecurity. Governments enact stringent cybersecurity regulations, and secure coding practices become a fundamental part of the development process.

Geopolitical Developments: Cybersecurity could become a significant aspect of international relations, with nations cooperating to manage global cyber threats. At the same time, cyber warfare could also emerge as a new battlefield, adding another layer of complexity to geopolitical tensions. In response to major cyber attacks, the European Union has established a certification framework to ensure that digital products and services in the EU meet cybersecurity standards. This has resulted in stricter regulations for software developers in the EU.

Societal Trends: There might be increased public concern over data privacy and online security. Companies that prioritise cybersecurity could be more trusted and favoured by consumers. On the flip side, societal polarisation could increase if cyber threats lead to restrictions on digital freedoms. Following high-profile data breaches, there has been an increase in demand for privacy-focused products and services. Companies like Apple have responded by emphasising their commitment to privacy and implementing features like end-to-end encryption.

Education: Cybersecurity education could become a priority at all levels of schooling. Beyond technical skills, understanding digital security’s societal and ethical implications would be important. Lifelong learning would be key as cybersecurity threats and solutions continuously evolve. There is a growing push to integrate cybersecurity education into the K-12 curriculum in the United States. This is part of a larger effort to prepare students for the digital age and potentially encourage them to pursue careers in cybersecurity.


Countries with strict cybersecurity laws and high incidences of cybercrime might have a high demand for cybersecurity specialists. Meanwhile, the supply might be global, as cybersecurity skills are largely transferable across different contexts.

As the world leans heavily on digital systems, cybersecurity becomes paramount. Countries prioritising cybersecurity increase the attractiveness of software engineering jobs. Key features include cybersecurity regulatory stringency, cybersecurity workforce size, and the prevalence of data breaches.

Demand for Software Engineers = α * Cybersecurity Regulatory Stringency + β * Cybersecurity Workforce Size + γ * Data Breach Incidences + ε

These models provide a mathematical representation of the scenario-specific features influencing the demand for software engineers.

Ensemble learning

Ensemble Learning: A Comprehensive Predictive Approach

To weave together the insights from each of these scenarios, we use a technique called ensemble learning. This involves combining predictions from multiple models to get a more robust forecast.

Data Collection and Pre-processing

To train these models, we need data corresponding to each feature. Such data is obtained from various sources:

  • Digital infrastructure quality and AI adoption rate could be collected from global indices or reports published by international agencies.
  • Information on remote work policies, cost of living, and industry automation levels might be gathered from government or industry reports.
  • Data on cybersecurity regulation, workforce size, and breach incidences can be found in cybersecurity databases or publications.

Python libraries like Pandas or Beautiful Soup can be used to scrape data, or APIs can be utilised where available.

import pandas as pd 
import requests from bs4 
import BeautifulSoup 
# Example of web scraping using BeautifulSoup 
url = "
response = requests.get(url) 
soup = BeautifulSoup(response.text, 'html.parser') 
# Process the soup object to extract relevant data 

This data must be cleaned, encoded, and pre-processed to get it into a suitable format for our models.

# Example of preprocessing using Pandas 
df = pd.read_csv('data.csv') 
# Handle missing values 
df = df.dropna() 
# Encode categorical variables d
f = pd.get_dummies(df) 

Training Individual Models

We can use a Gradient Boosting algorithm to train individual models for each scenario. Each model learns to predict future software engineering demand based on the relevant features.

from sklearn.ensemble 
import GradientBoostingRegressor 
# Define the model 
model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3) # Fit the model (X is the feature matrix, y is the target variable), y) 
# Make predictions 
predictions = model.predict(X) 

Integrating Predictions with a Meta-Model

Once we have our models and their predictions, we combine these using a meta-model. This higher-level model uses the predictions of the individual models as input features to generate a final prediction.

from sklearn.linear_model 
import LinearRegression 
# Define the meta-model 
meta_model = LinearRegression() 
# Fit the meta-model (predictions are the input features, y is the target variable), y) 
# Make final predictions 
final_predictions = meta_model.predict(predictions) 

By combining multiple models and their predictions, the ensemble learning approach gives us a robust, comprehensive, and nuanced tool to anticipate the future of remote software engineering.

Future Avenues

At this stage, we've only proposed this model. Its practical implementation requires comprehensive data that accurately represents the variables involved in our scenarios. Gathering this data and refining our models are substantial tasks, and that's where we see an exciting opportunity for collaboration. 

We invite you, our readers, to join us in this endeavour. Whether you are a data scientist, a software engineer, a strategist, or just intrigued by the future of remote work and software engineering, your insights and contribution would be invaluable. Your collaboration will help us refine our predictions and uncover a wider array of potential outcomes. Together, we can leap from proposal to practice, harnessing collective wisdom to forecast the future more accurately. 

In our upcoming blogs, we will delve deeper into our proposed scenarios and gradually unfold the picture our 'crystal ball' reveals. So, stay tuned and join us in this exploratory journey as we collaboratively shape the future of software engineering and remote work. The journey towards our shared future starts here!


By appreciating the different futures and how they may influence the demand for software engineers, we can better prepare for the transformations that lie ahead in the field. The ensemble learning approach allows us to build on the strengths of each individual scenario, providing a balanced and informed prediction that can guide strategic decisions in a rapidly changing landscape.



Common Features across all scenarios:

  1. Continued Learning: With the fast pace of technological development, it’s necessary for software engineers to continuously learn new skills, whether that’s adapting to new AI technologies, understanding international tax and labour laws, or learning the latest in cybersecurity defences.
  2. Globalisation: As remote work grows, engineers can be located anywhere worldwide. This leads to a global marketplace for talent, irrespective of the specific future scenario.
  3. Ethics and Responsibility: Whether dealing with AI, cybersecurity, or digital work policies, ethical considerations and responsible practice are central to the role of software engineers.

Distinct Features in each scenario:
Scenario 1: The Rise of the Digital Nomad Economy

  1. Government Policies: The competitive offering of government incentives to attract digital nomads.
  2. Lifestyle Focus: Decisions about work are driven by lifestyle factors like quality of life, cost of living, and personal preferences more than job location.

Scenario 2: The AI Paradigm Shift

  1. Automated Coding: Automation of routine coding tasks, transforming the role of the software engineer.
  2. AI Oversight: The primary role of software engineers shifts towards overseeing and managing AI systems, defining problem parameters, and ensuring the quality of AI-generated code.

Scenario 3: The Cybersecurity Imperative

  1. Cyber Threats: The rise in cyber threats and the emphasis on cyber defence.
  2. Regulations and Compliance: The emergence of stringent cybersecurity regulations that companies and individuals must comply with.

By isolating these features, we can better understand each scenario's unique and shared elements, allowing for more nuanced and robust strategic planning.