Predicting whether a clinical trial will succeed or fail is notoriously difficult. Even experienced pharmaceutical analysts struggle with accuracy rates much better than a coin flip. But what if we could train an AI model to learn from thousands of past trials and improve its predictions?

I recently built a dataset of 1,366 clinical trial predictions and fine-tuned an 8B parameter language model to predict trial outcomes. This resulted to in a jump from 56% accuracy (barely better than guessing) to 73% accuracy, a 30% relative improvement. Here’s how I did it, what I learned, and why this matters for anyone working with prediction tasks.

The Challenge: Making Predictions from Historical Data

The pharmaceutical industry runs on uncertainty. When Eli Lilly announces a Phase 3 trial for a new obesity drug, analysts, investors, and competitors all ask the same question: Will it succeed?

Traditionally, answering this question requires:

1. Deep domain expertise in pharmacology
2. Knowledge of the company’s track record
3. Understanding of regulatory pathways
4. Access to clinical trial databases
5. Lots of time to research each case

Even then, human experts achieve modest accuracy. The question I wanted to answer is, could an AI model learn these patterns automatically from historical data?

The Data Problem: Labels Are Expensive

The biggest hurdle in building prediction models is getting labeled training data. Hiring medical experts to label thousands of clinical trial outcomes would cost tens of thousands of dollars and take months.

This is where I discovered Lightning Rod’s approach to data generation. Instead of manual labelling, their SDK uses what they call the “Future-as-Label” methodology. This approach makes the future outcome of a historical event its label.

Here’s how it works:

1. Find old news: Articles from 2023 about clinical trials starting
2. Generate questions: “Will Novo Nordisk’s Phase 3 trial meet endpoints by Q4 2024?”
3. Auto-label outcomes: Search recent news (late 2024/2025) to find what actually happened
4. Build dataset: Pair questions with verified outcomes

It does this without the need for human labellers. The Lightning Rod Python SDK automatically finds the answers by searching for what happened later.

Building the Dataset: 1,366 samples in 2 Minutes

Using Lightning Rod’s Python SDK, I generated the dataset with a simple pipeline:

from lightningrod import QuestionPipeline, NewsSeedGenerator, WebSearchLabeler
pipeline = QuestionPipeline(
    seed_generator=NewsSeedGenerator(
        start_date=datetime(2023, 1, 1),
        end_date=datetime(2024, 12, 31),
        search_query=["clinical trial Phase 3", "FDA approval"]
    ),
    question_generator=ForwardLookingQuestionGenerator(
        instructions="Generate binary questions about trial outcomes",
        examples=[
            "Will Eli Lilly's obesity drug trial meet endpoints by Q4 2024?",
            "Will the FDA approve Drug X by June 2024?"
        ]
    ),
    labeler=WebSearchLabeler(confidence_threshold=0.7)
)
dataset = lr.transforms.run(pipeline, max_questions=2000)

The SDK pulled news articles about clinical trials, generated forward-looking questions, and then searched for later outcomes. In about 10 minutes of compute time, I had 1,882 questions, with 72.6% successfully labeled, giving me 1,366 high-quality training examples.

Each example looked like this:

Question: "Will Novo Nordisk's CagriSema Phase 3 trial meet its 
primary endpoints by December 31, 2024?"
Answer: YES (1)
Confidence: 0.98

The labels weren’t guesses; they were verified facts from published trial results and FDA announcements.

The Experiment: Baseline vs Fine-Tuned Model

I split the data into training (85%) and test (15%) sets, then ran two experiments:

1. Baseline: Zero-Shot Prediction

First, I tested Llama-3–8B without any training. I gave each question and asked it to predict 0 (failure) or 1 (success).

Result: 56.3% Accuracy

The model was essentially guessing, with a slight optimistic bias (it predicted “success” too often). I wasn’t surprised because the base model has no special knowledge of pharmaceutical industry patterns.

2. Fine-Tuning: Teaching the Patterns

Next, I fine-tuned the model using LoRA (Low-Rank Adaptation) on the training data. LoRA is a parameter-efficient method that adds small adapter layers instead of retraining the entire model.

The setup I used:

• Model: Llama-3–8B with 4-bit quantization
• Method: LoRA fine-tuning via the Unsloth library
• Hardware: Free Google Colab T4 GPU
• Training time: ~21 minutes (3 epochs)
• Trainable parameters: Only 16M (0.2% of the model)

Result: 73.3% Accuracy

The fine-tuned model correctly answered 151 out of 206 test questions, achieving 73.3% accuracy. This represents a 17-percentage-point improvement over the baseline, a 30% relative performance gain achieved in just 21 minutes of training. Notably, this was done using only 0.2% of the model’s parameters over 3 training epochs, demonstrating highly efficient improvement with minimal compute.

What Did the Model Learn?

The most interesting part wasn’t just the numbers; it was understanding what patterns the model discovered in the data.

Pattern 1: Company Track Records Matter

The model learned that pharmaceutical companies have different success rates. Questions mentioning Eli Lilly, Novo Nordisk, or Merck were more likely to be “YES” (success), while smaller biotech startups showed higher failure rates.

This makes sense because established companies have more resources, experience, and proven track records. The model picked this up automatically from the data.

Pattern 2: Therapeutic Areas Have Different Success Rates

Obesity and diabetes drugs showed ~68% success rates in the training data, while oncology trials succeeded only ~48% of the time. The model learned these differences without being explicitly told.

Cancer is harder to treat. Metabolic diseases have clearer biomarkers. The model internalized these domain patterns solely from examples.

Pattern 3: Timeline Realism

One surprising discovery is that the model learned to spot unrealistic timelines.

Example questions the model corrected:

• “Will [Small Biotech] complete Phase 3 in 6 months?” → Predicted NO (correctly)
• “Will [Unproven Drug] get FDA approval in 3 months?” → Predicted NO (correctly)

The baseline model didn’t know that Phase 3 trials typically take 18–24 months. The fine-tuned version learned this pattern from the data.

Pattern 4: Better at Predicting Failures

The baseline model showed an optimistic bias, predicting “success” 63% of the time. The fine-tuned model was better calibrated at 52%, closer to the actual distribution.

More importantly, it learned to identify red flags like:

• Aggressive timelines
• Unproven mechanisms
• Companies with poor track records
• Challenging therapeutic areas

All five examples of “most improved” predictions involved the baseline incorrectly predicting success, while the fine-tuned model correctly predicted failure.

Why This Matters Beyond Clinical Trials

While this experiment focused on pharmaceutical trial outcomes, the real contribution goes beyond healthcare. It demonstrates a practical, repeatable workflow for building specialized prediction models from real-world data.

At its core, the approach is straightforward:

1. Identify a prediction task with historical data
2. Use temporal structure (what happened later becomes your label)
3. Generate datasets automatically instead of manual labeling
4. Fine-tune efficiently with LoRA on free GPUs
5. Achieve meaningful improvements in hours, not months

This same approach could work for:

• Product launch predictions: “Will Company X release Product Y by Date Z?”
• Policy outcomes: “Will Bill ABC pass by Q2 2026?”
• Market events: “Will Stock X reach price Y by month Z?”
• Sports forecasting: “Will Team X make the playoffs this season?”

Any domain with historical news or announcements, clear and verifiable outcomes, and sufficient examples can benefit from Lightning Rod’s Future-as-Label methodology.

It’s not limited to clinical trials; it serves as a scalable template for temporal prediction tasks across industries.

Technical Implementation Details

For developers interested in reproducing this:

Dataset Generation:

• Source: News articles via Lightning Rod SDK
• Questions: 1,882 generated, 1,366 valid (72.6%)
• Label confidence: Average 0.998, minimum 0.85
• Time: ~3 minutes

Model Training:

• Base model: Llama-3–8B
• Method: LoRA (rank=16, alpha=16)
• Quantization: 4-bit for memory efficiency
• Epochs: 3
• Batch size: 2 (effective 8 with gradient accumulation)
• Hardware: Google Colab free tier (T4 GPU)
• Time: ~21 minutes

Evaluation:

• Test set: 206 questions (15% holdout)
• Metric: Binary accuracy
• Baseline: 56.3% (116/206)
• Fine-tuned: 73.3% (151/206)
• Improvement: +17.0 percentage points

The full code and dataset are available on GitHub, and the dataset is published on Hugging Face for anyone who wants to reproduce or build on this work.

Limitations and Future Work

This isn’t a perfect crystal ball. The model still struggles with:

1. Novel drug mechanisms not seen in training
2. Rare diseases with limited examples
3. External factors (regulatory changes, manufacturing issues)
4. Very recent trials without outcome data yet

The 73% accuracy is a meaningful improvement over guessing, but it’s not prophecy. Think of it as moving from “coin flip” to “informed probability estimate.” For context, even experienced pharmaceutical analysts struggle to achieve accuracy above 65–70% in predicting trial outcomes.

Future improvements could include:

• Larger datasets (5,000+ examples)
• Additional features (company financials, prior trial data)
• Ensemble methods combining multiple models
• Continuous updating as new outcomes emerge

Key Takeaways

Three lessons from this project:

1. Automated labeling scales: Manually labeling 1,366 examples would have taken weeks and cost thousands. Lightning Rod’s Future-as-Label approach did it in 3 minutes.

2. Small models can specialize: You don’t need GPT-4 or Claude for domain-specific tasks. An 8B model, fine-tuned on focused data, achieved 73% accuracy on a challenging prediction problem.

3. Historical data contains learnable patterns: Company track records, therapeutic area success rates, and timeline realism all emerged naturally from the training data. The model discovered what experts know from experience.

Try It Yourself

The tools I used are all publicly available:

• Lightning Rod SDK: Open-source Python library for dataset generation
• Pre-trained Model: Skip training, use the fine-tuned model directly via Hugging Face
• Unsloth: Free library for efficient LoRA fine-tuning
• Google Colab: Free GPU access for training
• Hugging Face: Free dataset and model hosting

The barrier to entry for specialized AI models has never been lower. If you have a prediction task with historical data, you can build a custom model in a weekend.

The future of AI isn’t just giant general-purpose models; it’s also specialized models trained on focused, high-quality datasets for specific domains. This experiment is one example of what becomes possible when you combine automated data generation with efficient fine-tuning.

What prediction task would you build a model for?

The dataset is available on Hugging Face, and all code is on GitHub. Special thanks to Lightning Rod Labs for their SDK that made this project possible.

Resources:

• 🤖 Fine-tuned Model
• 📊 Dataset
• 💻 Code

Interested in building prediction datasets? Check out Lightning Rod or explore their examples on Hugging Face.

Training AI to Predict Clinical Trial Outcomes: A 30% Improvement in 3 Hours was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

In February 2025, a profound shift began rippling through corporate boardrooms worldwide. IBM laid off 8,000 employees, Microsoft eliminated 6,000 positions, and hundreds of smaller companies followed suit. This is not due to economic recession, but because artificial intelligence systems can now perform their jobs. This isn’t a distant dystopian future; it’s happening now. As we stand at the threshold of Artificial General Intelligence (AGI), we face an unprecedented transformation that will fundamentally reshape how humans work, earn, and contribute to the economy. The impact of AGI is not a matter of if, but when, and we must prepare for this inevitable disruption.

Artificial General Intelligence (AGI), also known as human-level AI, is a type of artificial intelligence that would match or surpass human cognitive abilities across any domain. It represents a quantum leap beyond today’s narrow AI systems because they would possess reasoning, problem-solving, and adaptability comparable to human intelligence. Rather than viewing AGI as a threat, we can see it as a tool with the potential to enhance our capabilities and lead to a more efficient, productive future.

We are all aware of the remarkable capabilities of AI systems such as ChatGPT and Claude in tasks like writing essays, analyzing data, and generating creative ideas. Now picture a future where ChatGPT can not only write for you but also manage your calendar, plan your meetings, and book your flights. You plug it into your vehicle, and it drives you safely to your destination. You take it into the kitchen, and it prepares any dish you want, learning new recipes instantly from the internet and adjusting them for your personal taste. That is what Artificial General Intelligence (AGI) represents: not just an assistant that follows commands, but a system that can understand context, make decisions, and autonomously perform any intellectual or physical task a human can.

Industry leaders confirm AGI’s imminent arrival. OpenAI CEO Sam Altman predicts 2025 as the year “AI agents join the workforce and materially change the output of companies.” Anthropic’s CEO forecasts that within five years, AI could eliminate 50% of entry-level white-collar jobs and spike unemployment by 10–20%.

This essay argues that AGI will soon replace approximately 80% of jobs in specific industries due to its superior performance in speed, accuracy, cost-efficiency, and 24/7 availability. Industries involving repetitive cognitive tasks, data processing, and predictable decision-making face the most significant risk. We will examine which sectors and jobs are most vulnerable, analyze the economic ramifications, and propose actionable strategies for individuals, companies, and governments to navigate this seismic transition.

Industries First Impacted by AGI

The initial wave of AGI disruption will not strike randomly across the economy. Specific industries have structural characteristics that make them especially vulnerable to automation: high volumes of repetitive tasks, rule-based decision-making processes, heavy reliance on data analysis, and standardised workflows. These industries are already experiencing significant AI adoption, making them the proving grounds for AGI’s transformative power.

Industry 1: Customer Service

The shift to AI-powered customer service is accelerating, driven by powerful data. A Tidio study projects that up to 95% of customer interactions could be handled by AI by 2025, and their research also shows that 3 out of every four companies that introduce AI see sales increase by over 10%. This aligns with Octonomy’s findings, which report that 55% of companies already deploy AI in customer service. The economic incentive is undeniable, as Desk365 highlights, noting that labour cost savings of up to 90% are possible when AI automates routine tasks. You can also attest that most of the websites and products you use today use AI for customer service and refer you to a human only when needed. The economics are irresistible for companies because AI never gets tired, never takes breaks, scales instantly, and costs a fraction of what human labor does. While high-touch customer relationships that require empathy and complex problem-solving may remain human-centred, the vast majority of customer interactions follow predictable patterns that AGI handles more efficiently.

Industry 2: Manufacturing and Logistics

This industry has long been a target for automation, but AGI will take mechanization to an entirely new level. Industrial robots now account for 44% of repetitive manufacturing tasks worldwide, but AGI-powered systems will go beyond physical automation. They will optimise entire supply chains in real time, predict equipment failures before they occur, coordinate autonomous vehicles and drones, and make complex quality-control decisions that previously required human judgment.

Unlike previous waves of automation that required extensive reprogramming for new tasks, AGI systems are presumed to adapt to changing production requirements, handle exceptions, and continuously optimize processes once the purview of human supervisors.

Industry 3: Financial Services and Banking

Financial Services and Banking are experiencing a seismic shift as AGI algorithms handle transaction processing, fraud detection, risk assessment, and investment management with superhuman speed and accuracy. As of 2027, AI is expected to manage over $1.2 trillion in banking assets. Approximately one-third of transaction-handling roles in financial institutions have already been automated. Robo-advisors automatically adjust investment portfolios based on market conditions and client preferences, often outperforming manual fund managers in retail banking. Natural language interfaces are taking over front-desk roles in digital banking, assisting customers with account queries and product discovery. The financial sector is expected to automate 70% of basic operations by 2027, with significant job cuts on Wall Street as algorithms prove more reliable than traders at parsing market signals and executing strategies.

Industry 4: Healthcare Administration and Back-Office Operations

Healthcare Administration and Back-Office Operations represent another vulnerable sector. While doctors and nurses perform irreplaceable human-centered care, the administrative machinery of healthcare is ripe for automation. Medical transcription is expected to reach 99% automation, with 40% of medical coding automated by 2025. Insurance claim processing, appointment scheduling, billing reconciliation, and patient record management tasks that currently employ millions of people can be performed more accurately and more quickly by AGI systems. The result is a bifurcated healthcare workforce: hands-on caregivers remain essential, while administrative staff face widespread displacement.

Jobs at Highest Risk of AGI Automation

The displacement effect of AGI operates at the task level, not just the job level, but certain occupations face near-complete automation within the next five years. Understanding which jobs are most vulnerable and why is crucial for workforce planning.

Why does AGI outperform humans in these roles? The advantages are multifaceted and decisive:

Not all jobs are equally at risk of automation. Positions that require human judgment, creativity, and emotional intelligence, such as healthcare providers, skilled tradespeople, and creative professionals, are relatively safe because of their complexity. However, these roles will still evolve as professionals adopt AGI for tasks such as diagnostics and strategic analysis. The future involves collaboration between humans and machines, which may render some jobs redundant.

ECONOMIC IMPACT OF AGI

Short-Term Impacts (2025–2030): The Disruption Phase

The immediate economic effects will be painful and destabilizing. Research from the World Economic Forum predicts that 85 million jobs will be displaced globally by 2025 and beyond, a staggering figure that signals turmoil across entire industries. In the United States, projections suggest that 30% of current jobs could be fully automated by 2030, while 60% will undergo significant task-level changes. These aren’t gradual shifts allowing smooth transitions; they’re rapid displacements concentrated in specific sectors.

Entry-level employment faces particularly acute pressure. Anthropic’s CEO predicts AI could eliminate 50% of entry-level white-collar jobs within five years, creating a “missing generation” problem in which young workers can’t gain the foundational experience that traditionally leads to senior positions. This hollowing out of entry-level roles threatens the entire career pipeline: if companies automate away junior positions, where do future managers and executives come from?

Unemployment will spike, but not uniformly. Projections suggest U.S. unemployment could peak in the high single digits among active labour market participants, with continental Europe potentially reaching the low double digits. Workers aged 18–24 are 129% more likely than those over 65 to worry that AI will make their jobs obsolete, and with good reason: entry-level positions face the highest automation rates. Women also face disproportionate risk, with 58.87 million women in the U.S. workforce in occupations highly exposed to AI automation compared to 48.62 million men.

Income inequality will likely widen dramatically in the short term. The wage gap between highly educated workers and others could explode as AI-literate professionals commanding premium salaries work alongside displaced workers competing for shrinking opportunities in low-skill service roles. One scenario envisions incomes for workers at the 90th percentile increasing by 15%, median wages staying flat, and bottom-quartile wages decreasing. This exacerbates existing wealth concentration, with capital owners who control AGI systems capturing disproportionate gains while labor’s share of national income declines.

Long-Term Impacts (2030–2050): The Transformation Phase

If we successfully navigate the transition, the long-term economic picture brightens considerably. Goldman Sachs projects that full AI adoption could boost U.S. labor productivity by 15% and global GDP by 7%, a permanent increase in economic output equivalent to adding trillions of dollars to the global economy each year. More optimistic projections suggest AI could add $19.9 trillion to the global economy by 2030, with compound effects raising GDP by 1.5% by 2035, nearly 3% by 2055, and 3.7% by 2075.

This productivity revolution operates through multiple channels: AGI eliminates wasteful inefficiencies, optimizes resource allocation, accelerates innovation through AI-assisted research and development, and enables entirely new products and services that would be impossible without machine intelligence. Banks could increase pretax profits by 12–17% by 2027, totaling $180 billion. Industries exposed to AI experienced productivity growth, jumping from 7% to 27% between 2018 and 2024.

Crucially, the World Economic Forum predicts that while 85 million jobs will disappear by 2025, 97 million new roles will emerge, a net gain of 12 million positions globally. These new jobs cluster in areas such as AI development and maintenance, AI ethics and governance, human-AI interaction design, data curation and management, and complex problem-solving that require human creativity and judgment. The question is whether displaced workers can transition to these new roles, which often require advanced education and technical skills.

Winners and Losers: The Distribution Challenge

The economic gains from AGI won’t be distributed evenly. Advanced economies with strong education systems, technological infrastructure, and adaptive institutions are positioned to capture disproportionate benefits. Developing nations risk being left behind if they lack the resources to implement AI or retrain their workforces. Within countries, geographic disparities will emerge: tech hubs like Silicon Valley, Boston, and Seattle will thrive as AI centers while regions dependent on automatable industries face decline.

Age-based divides will intensify. Older workers approaching retirement may ride out the transition, while young workers face careers requiring continuous reinvention. Gender disparities could widen given women’s concentration in administrative and clerical roles, facing high automation risk. Educational attainment becomes increasingly determinant of economic outcomes: 77% of AI-related jobs require master’s degrees and 18% require doctoral degrees, locking out workers without access to advanced education.

The central economic question is whether AGI leads to broadly shared prosperity or extreme concentration of wealth and power. Without intervention, market dynamics favor the latter: capital owners and highly skilled workers capture gains while everyone else faces declining opportunities. However, with appropriate policies, progressive taxation of AI profits, funded retraining programs, strengthened social safety nets, and a universal basic income, the productivity gains could be distributed more equitably.

Historical precedents provide mixed guidance. Previous technological revolutions, such as steam power, electricity, and computers, ultimately dramatically increased living standards, but the transitions involved decades of disruption, with winners and losers. AGI differs in speed (measured in years, not decades) and scope (affecting cognitive work, not just physical labor). The question is whether society can manage an exponentially faster transition with exponentially higher stakes.

Preparation Strategies

The AGI transformation is not a distant threat to be addressed eventually. It’s an immediate challenge requiring urgent action from individuals, companies, and governments. Those who adapt proactively will thrive, but those who delay face obsolescence.

Individual Strategies: Future-Proofing Your Career

1. Embrace Continuous Learning as a Lifestyle

The half-life of professional skills is shrinking rapidly. What you learned in college five years ago is increasingly irrelevant; what you’ll need five years from now doesn’t exist yet. Adopt a lifelong learning mindset and set aside time each week for skill development. This doesn’t necessarily mean returning to formal education. Micro-learning platforms like Coursera, LinkedIn Learning, and Google Career Certificates offer targeted, credential-bearing programs that take months, not years. The World Economic Forum projects that over 40% of workers will require significant upskilling by 2030; being in the vanguard of that transition provides a competitive advantage.

2. Develop AI Literacy Immediately

You don’t need to become a programmer, but you must understand how AI works, what it can and cannot do, and how to leverage it in your work. Familiarise yourself with tools such as ChatGPT, Claude, GitHub Copilot, and industry-specific AI applications. The workers who remain valuable aren’t those competing against AI, but those who use AI better than others. As one executive observed, “We’re not replacing workers with AI; we’re replacing workers who don’t use AI with workers who do.”

3. Build Skills Portfolios, Not Career Ladders

The traditional model, which climbs a hierarchical ladder within one company or industry, is obsolete. Instead, develop a portfolio of complementary skills allowing lateral moves across functions and industries. Employers increasingly hire based on demonstrated capabilities rather than years of experience or specific degree credentials. Leverage digital credentials, maintain an updated portfolio showcasing projects, and cultivate a professional network providing access to opportunities.

4. Consider Strategic Career Pivoting

If your current role faces a high risk of automation, begin planning a transition now rather than waiting for displacement. Identify adjacent roles that require similar foundational skills but pose lower automation risk. For example, data entry clerks might pivot to data quality management or customer success roles, and retail cashiers might transition to personal shopping advisory or e-commerce coordination. Employers and community colleges increasingly offer reskilling programs, so take advantage while you’re still employed.

Company Strategies: Responsible AI Adoption

1. Invest in Comprehensive Reskilling Programs

Companies that view employees as disposable costs to be automated away face long-term disadvantages because institutional knowledge evaporates, employer brands suffer, and social license erodes. Forward-thinking organizations invest substantial resources in workforce development. Amazon’s $700 million “Upskilling 2025” program aims to provide advanced skill training for more than 100,000 employees. IBM’s “New Collar” initiative focuses on skills-based hiring and internal mobility. These investments pay dividends through higher retention, greater employee engagement, smoother transitions to AI-augmented workflows, and preservation of institutional knowledge.

2. Redesign Work Around Human-AI Collaboration

Rather than simply replacing humans with machines, reimagine workflows that leverage the comparative advantages of both. AGI handles routine data processing, pattern recognition, and optimization while humans focus on strategic decision-making, relationship management, creative problem-solving, and ethical oversight. Create new roles like AI oversight managers, prompt engineers, and explainability specialists who bridge human and machine capabilities. This approach augments your workforce rather than eliminating it while achieving productivity gains.

3. Adopt AI Ethically and Transparently

Companies deploying AI without consideration for employee welfare and social impact face reputational risks, regulatory challenges, and employee resistance. Implement AI gradually with stakeholder input; provide clear communication about automation plans and support for affected workers; ensure AI systems are transparent and auditable; and establish ethics review boards to evaluate AI deployments. Responsible AI adoption builds trust and facilitates smoother transitions.

4. Cultivate a Culture of Adaptability

Organizations must become learning institutions where change is expected, skills development is prioritized, and experimentation is encouraged. This requires leadership commitment, demonstrated through resource allocation and role modelling; psychological safety that allows people to fail and learn; incentive systems that reward adaptability and learning; and continuous feedback mechanisms that enable rapid iteration. Companies with adaptive cultures weather technological disruption far better than rigid hierarchies.

Government Strategies: Managing the Transition

1. Reform Education Systems for the AI Era

Education systems for the industrial economy are outdated. Governments should overhaul curricula to focus on critical thinking, digital literacy, interdisciplinary problem-solving, and soft skills like communication, collaboration, creativity, and emotional intelligence. Countries like Singapore, Germany, and Canada are modernising their education systems; the U.S. needs similar reforms.

2. Establish Robust Retraining Infrastructure

The shift to an AGI economy needs massive investment in adult education and reskilling. Governments should fund accessible, subsidized training for high-risk workers, partner with colleges to develop relevant curricula, offer income support, and create digital skills passports. The Infrastructure Investment and CHIPS Acts allocate billions, but more is required.

3. Strengthen Social Safety Nets

During the transition phase, unemployment will spike and income inequality will widen without interventions. Governments must expand unemployment benefits with longer durations and higher payments, implement portable benefits not tied to specific employers, explore universal basic income pilots providing basic economic security, and strengthen progressive taxation to fund these programs. The Nordic countries offer models for comprehensive social insurance systems that cushion workers during transitions.

4. Regulate AI Development and Deployment

Unconstrained AI development risks racing toward AGI without adequate safety measures or societal preparation. Governments should establish AI safety standards and auditing requirements, mandate impact assessments for automation decisions that affect large workforces, protect workers’ rights to understand and contest AI decisions that affect them, and fund research on AI safety, alignment, and ethics. The EU’s AI Act provides a framework that other jurisdictions should adapt.

5. Promote Equitable Distribution of AI Benefits

Without intervention, AGI’s gains accrue primarily to capital owners. Governments can reshape these dynamics through progressive taxation of AI profits, funding social programs, equity ownership models that give workers a stake in AI-driven productivity, data sovereignty laws that ensure individuals control the data used to train AI systems, and antitrust enforcement to prevent AI monopolies. The goal is to ensure AGI benefits broad populations rather than further concentrating wealth.

Conclusion

We stand at an inflection point in human history. Artificial General Intelligence promises to be the most transformative technology of our era. The displacement of jobs in vulnerable industries, such as customer service, manufacturing, financial services, transportation, and healthcare, isn’t fear-mongering; it’s a likely outcome over the next decade. Entry-level positions face particular vulnerability, creating a “missing generation” problem, while economically, AGI presents both tremendous opportunity and serious risks. Without intervention, it risks exacerbating inequality to unprecedented levels.

The imperative for action is urgent. Individuals must embrace continuous learning and develop AI literacy. Companies must invest in workforce development and adopt AI responsibly. Governments must reform education, establish retraining infrastructure, and ensure equitable distribution of benefits. The choices we make now will determine whether AGI creates broadly shared prosperity or deepens societal divisions.

Like every disruptive innovation throughout history, AGI will displace some while enriching others. The mobile phone displaced call booths and telegrams, but it also revolutionized global communication. The automobile displaced horse-drawn carriages but transformed transportation and commerce. The internet displaced traditional retail but created unprecedented access to information and opportunity. In the same way, when managed wisely, AGI will displace many current roles while undoubtedly improving productivity, innovation, and the quality of life. The question is: are you ready, or are you working toward the right direction to avoid obsolescence and position yourself for opportunity? I leave that to you.

As a business leader, you’ve probably heard a lot about AI agents, autonomous systems designed to enhance customer support, streamline workflows, analyse data, and support your teams. The benefits are enticing: improved efficiency, lower costs, and enhanced customer experiences.

However, there’s a catch: not every AI framework is suitable for every situation. Choosing the wrong one can waste your budget, delay your projects, and frustrate your team. With new frameworks emerging regularly, navigating this complex landscape can feel overwhelming.

In this guide, we’ll cut through the noise and help you make informed decisions about building AI agents for your business. We’ll focus on what matters most: your team’s capabilities, your budget constraints, and your actual business needs, not just the technical hype.

The Real Questions Business Leaders Need to Ask

Before diving into specific frameworks or tools, you need clarity on three fundamental questions:

1. Do we have the technical talent in-house, or will we need to hire?

2. What’s our realistic budget, not just for software, but for people and infrastructure?

3. What business problem are we actually solving?

Let’s break down how to answer these questions systematically.

1. Assess Your Team’s Technical Capability

The most critical decision you’ll make is whether to go with a no-code platform or a code-based framework. This isn’t about what sounds more impressive; it’s about matching your team’s actual capabilities to the tool.

Do You Have Developers on Your Team?

If No: Go with No-Code Platforms

If your team includes business analysts, operations managers, marketers, or other non-technical professionals, don’t require them to learn programming. Modern no-code tools are powerful enough to build sophisticated agents without writing code.

Popular no-code options: n8n, Zapier, Copilot Studio, Voiceflow, Botpress, ChatFuel

What this means for you as a leader:

• Faster time to market (days or weeks, not months)
• Lower upfront costs (no need to hire expensive AI developers immediately)
• More ownership by business teams (they can iterate without waiting for IT)
• Trade-off: Less customisation and flexibility as you scale

If Yes → Explore Code-Based Frameworks.

If you already have developers, especially Python, JavaScript, or Java developers, you can leverage code-based frameworks that give you complete control and customisation.

Popular coding frameworks: LangChain, CrewAI, LangGraph, AutoGen, Semantic Kernel, LlamaIndex

What this means for you as a leader:

• Higher upfront investment in developer time
• Greater flexibility to customise agents to your exact needs
• Ability to integrate deeply with existing systems and databases
• Trade-off: Longer development cycles and higher maintenance overhead

2. Calculate the True Cost of Building AI Agents

Budget conversations about AI agents often focus only on software licensing, and that’s a mistake. You need to account for the full cost of ownership.

No-Code Platforms: Subscription + Scale Costs

No-code platforms typically operate on freemium or subscription models. Here’s what to watch for:

1. Free tiers: Tools like n8n and Pipedream offer free or open-source versions, which are great for prototyping. However, as you scale, you’ll hit limits on executions, integrations, or team seats.
2. Paid tiers: Platforms such as Zapier, Copilot Studio, and Voiceflow charge monthly fees that scale with usage. For example, Zapier charges $20–$ 2,000 per month, depending on the number of tasks and premium features. Copilot Studio starts at $200 per tenant per month, while Voiceflow ranges from $50 to $625+ per tenant per month, depending on team size and selected features.

You should also watch out for some hidden fees. Fees like integration fees, data storage, API calls to LLMs (like OpenAI or Anthropic), and support packages can add up quickly, so you have to also watch out for these.

Code-Based Frameworks: Developer Time + Infrastructure

Code-based frameworks are usually free and open-source (LangChain, CrewAI, AutoGen), but your real costs are in people and infrastructure:

1. Developer salaries: AI-focused developers command premium rates. Expect $100K–$200K+ annually for experienced talent in major markets. If you’re hiring contractors, rates range from $75 to $250/hour.
2. Development time: Building a production-ready agent can take 2–6 months, depending on complexity. That’s salary cost multiplied by timeline.
3. Infrastructure costs: Once built, you’ll need hosting (AWS, Azure, GCP), monitoring tools, and database storage. For a moderately complex agent handling thousands of requests daily, expect $500–$5,000+/month in infrastructure.
4. Ongoing maintenance: AI agents aren’t “set it and forget it.” They require monitoring, updates, and optimisation as your business needs evolve.

As a business leader, here is what you have to ask yourself:

• Can we afford 3–6 months of developer time upfront, or do we need something running in weeks?
• Are we prepared for ongoing costs, or do we need predictable monthly pricing?
• Do we have internal resources to manage infrastructure, or should a platform handle that?

These answers will help you decide whether to invest in no-code agility or code-based control.

3. Match Your Use Case to the Right Tool

Not every framework is built for every job. Some excel at conversational AI, others at data analysis, and still others at workflow automation. Choosing the wrong tool for your use case is like using a screwdriver to hammer a nail; it might work, but it’ll be painful. Below, we will outline where each of the tools we mentioned earlier fits.

4. Evaluate Team Size and Governance Needs

Your organisation’s size and structure should influence your framework choice, and here is how

• Small Business or Startup (1–20 people): As a small business or startup, your primary focus should be on speed, low cost, and the flexibility to pivot quickly. At this stage, it’s more important to validate ideas than to build complex, fully custom systems. Your best approach is to use no-code platforms or lightweight code frameworks such as Zapier, n8n, or simple LangChain implementations. These tools allow you to build and test ideas rapidly without needing a dedicated AI development team. Since governance overhead can slow progress, keep it minimal: focus on delivering a functional solution, collect user feedback, and iterate.
• Mid-Market Company (20–500 people): For mid-sized companies, the focus shifts to scalability, system integration, and moderate customisation. You likely have some technical expertise on your team, but resources remain limited compared to those of large enterprises. The best approach is a hybrid model: use no-code platforms for simpler use cases, such as workflow automation or basic chatbots, and code-based frameworks for more complex, high-value projects, such as advanced data analysis or multi-system integration. Governance should be moderate, establish clear rules for who can build agents, how they are tested, and when they’re deployed to production, ensuring consistency without stifling innovation.
• Enterprise (500+ people): For large enterprises, priorities centre on security, compliance, scalability, and governance. The best approach is to adopt code-based frameworks with enterprise support, such as Semantic Kernel, SpringAI, LangChain4j, Google ADK, or AWS Strands. These frameworks provide the control, auditability, and integration capabilities required for large-scale, secure operations within enterprise infrastructure. Given the complexity and risk involved, governance must be robust, with formal approval processes, security reviews, compliance checks, and centralised monitoring in place to ensure AI systems align with corporate policies, data protection standards, and long-term strategic goals.

5. Consider Your Existing Tech Stack

Don’t choose an AI framework in isolation. Consider what your team already uses and select a framework. The table below should help you make the right decision based on your existing tech stack.

It’s best always to choose tools that leverage your existing investments rather than forcing your team to learn entirely new platforms.

6. Prototype Fast, Then Scale Smart

Here’s the approach that works for most business leaders:

Phase 1: Validate with Prototypes (2–4 weeks)

• Choose a small, well-defined use case
• Build a lightweight prototype using the simplest tool that fits
• Test with real users (internal team or select customers)
• Measure concrete outcomes: time saved, tickets deflected, revenue impacted

Phase 2: Evaluate and Benchmark (1–2 weeks)

• Assess: Did it actually solve the problem?
• Measure: Speed, accuracy, cost per interaction, user satisfaction
• Compare: Would a different tool/framework have worked better?

Phase 3: Scale or Pivot (Ongoing)

• If successful: Invest in making it production-ready (better error handling, monitoring, security)
• If unsuccessful: Quickly pivot to a different approach without having wasted months

Business leader tip: Don’t get caught in “analysis paralysis.” Select something reasonable based on the criteria above, test it quickly, and be prepared to adjust if needed.

Your Decision Roadmap: A Quick Framework

Now that we’ve covered all the key considerations, let’s consolidate them into a simple, actionable plan. Below is a decision architecture we’ve created to help you make your choice more quickly and easily. Think of this as your visual roadmap. Start at the top and follow the path that best matches your situation to narrow down your options systematically.

This flowchart consolidates all the information we’ve discussed into a single decision tree. Instead of juggling all the factors in your head, you can follow the branches that apply to you.

Common Pitfalls Business Leaders Should Avoid

1. Choosing based on hype, not fit. Just because everyone’s talking about a framework doesn’t mean it’s right for your use case. Evaluate based on your specific needs.

2. Underestimating maintenance, AI agents aren’t “set and forget.” Budget for ongoing monitoring, updates, and optimisation.

3. Skipping the prototype phase. Going straight to a large-scale implementation without testing is risky. Start small, prove value, then scale.

4. Ignoring change management. Even the best AI agent will fail if your team doesn’t adopt it. Invest in training and communication.

5. Over-engineering early. Don’t build for every possible future scenario on day one. Build for today’s problem, then iterate.

Wrap-Up

Choosing the proper AI framework isn’t about finding the “best” tool in the abstract. It’s about finding the right fit for your business: your team’s capabilities, budget constraints, use case, and timeline.

The businesses that thrive with AI agents are those that progress thoughtfully yet swiftly, experimenting, learning, and adjusting rather than attempting perfection from the outset.

If you found this helpful, share your experiences and challenges. What framework are you considering, and what’s holding you back?

Hi there,
Just a quick one today 🤗

After 3 years as an AI Engineer, I’ve built and deployed machine learning models, created agentic workflows, and worked across various Gen AI projects. And while I’ve genuinely enjoyed building, one thing became increasingly clear to me:

I love writing about AI and explaining how things work just as much — if not more — than building them.

That realisation led me down a new path — a full pivot into Developer Relations and Technical Writing, still within the AI space.

The Pivot I Needed 🎯

Although I still enjoy the engineering side of things, I’ve found deep fulfilment in communicating technical ideas, creating content, and helping other developers learn and build.

Over the past 10 months, I’ve worked as a Technical Writer at Ready Tensor — a pioneering media platform for AI and data science innovation.
There, I:

• Wrote articles on trending AI and ML topics
• Engaged with the developer community
• Led the Agentic AI Developer Certification Program, a hands-on learning experience for over 500 learners

This experience confirmed what I’d already felt: writing, community building, and teaching are not just skills I enjoy, they’re where I thrive.

What’s Next?

I’m fully embracing my new path as a Technical Writer and DevRel Engineer, and I’m excited for what’s ahead.

From here on out, I’ll be writing more consistently about:

• AI, ML, and Generative AI
• Data and Python
• My career journey and lessons learned

📩 Follow me here on Medium and hit the Subscribe button if you’d like to stay updated on my posts.
I’d love to bring you along as I grow and contribute in this new direction.

I won’t stop building though 🚀

Thanks for reading ✌️
 — Victory Nnaji

A quick one today 🤗

I’ve been working as a Gen AI Engineer for over 1 and a half years now, and I’ve come to some realisations:

The first one is that AI Engineering isn’t a well-defined field yet and the requirements or tasks vary based on the needs of the company/project at hand.

Secondly, An AI Engineer is a blend of a data scientist, machine learning engineer, and software engineer, particularly in backend development.

Why do I say so?

1. As an AI Engineer, you either use pre-built APIs or train your models, thus taking on the role of a machine learning engineer.
2. As an AI Engineer, you’ll handle large datasets — cleaning, transforming, or otherwise manipulating them — tasks typically done by a data scientist or analyst.
3. You’ll also develop APIs, manage backend tasks, and uphold software engineering principles, akin to a software engineer. This involves making API calls, integrating various APIs, processing data at the backend, and working with databases before exposing results to the frontend.

So, if you’re building AI systems, be ready to wear multiple hats. You’ll tackle various exciting challenges and make them work. Remember, you’re a blend of an ML engineer, a data scientist, and a software engineer.

Thanks for reading ✌️

Python is a high-level, general-purpose programming language known for its readability, simplicity, popularity and versatility.

Let’s break it down

If you haven’t heard of Python before, I would want you to picture Python as a toolbox containing various tools that can be used for various works.

If you’ve ever seen a toolbox, you know it houses various tools. There are the cutting tools like scissors, measuring tools like rulers and tape, fastening tools like screws, and many others. These tools are utilised as needed, each serving different purposes. Whether you’re a DIY enthusiast, a mechanic, or anyone else, these tools cater to a range of tasks and roles.

Over to Python

Python, as a programming language, provides a variety of tools, commonly referred to as methods, functions, libraries, or frameworks. Similar to a regular toolbox where you decide what task you want to accomplish and then choose the appropriate tool, Python operates similarly. You need to comprehend your objective and select the specific framework, library, method, or function that can assist you in achieving it. Use that chosen tool to execute the task.

So why should I learn Python in 2024?

Beyond its widespread popularity, Python is extensively utilized in numerous companies, making it a valuable skill in various tech fields. If you’re looking to enter the tech industry and are uncertain about the advantages of learning Python, consider the following tech fields where Python knowledge can be advantageous. I will be listing but a few of them.

1. Data Science: Data science is the field of extracting insights and knowledge from data using scientific methods, statistics, and algorithms to make informed decisions and predictions. Python, along with libraries such as Pandas, NumPy, and Scikit-learn, is extensively used for data analysis, machine learning, and artificial intelligence.
2. Web Development: Web development is the process of creating and maintaining websites or web applications, involving tasks such as designing, coding, and organizing content for online platforms. Popular frameworks like Django and Flask make Python a go-to choice for building web applications.
3. Cybersecurity: This is the practice of protecting computer systems, networks, and data from theft, damage, or unauthorized access. Python is employed for ethical hacking, penetration testing, and developing security tools.
4. Machine Learning and AI: AI is the broader concept of creating machines or software that mimic human intelligence. Machine Learning is a subset of AI, focusing on developing algorithms that enable machines to learn from data and improve their performance over time. Libraries like TensorFlow and PyTorch leverage Python for developing and implementing machine learning models.
5. Data Engineering: Data Engineering is the process of designing and building systems to handle and organize large amounts of data effectively. Python is employed in data engineering tasks, including data pipeline development and ETL (Extract, Transform, Load) processes.
6. DevOps: DevOps is a collaborative approach that combines development (Dev) and operations (Ops) to streamline and automate the software development and delivery process, fostering quicker and more reliable releases. Python scripts are commonly used in DevOps for automation, infrastructure as code, and configuration management.

In conclusion, learning Python in 2024 is a strategic move given its widespread popularity and demand across various tech fields. The language’s versatility positions aspiring techies for success, making Python a valuable asset for those aiming to thrive in technology. Python’s user-friendly syntax makes it an ideal choice for beginners, providing an accessible entry point for those new to the world of programming.