In 1956, a group of brilliant minds gathered at Dartmouth College with the belief that artificial intelligence could be solved over a ten-week summer project. Nearly 70 years later, we’re still on the journey. But today, something feels fundamentally different.
We’ve entered what many are calling AI’s endless summer—a period of continuous discovery, creation and disruption. This isn’t a passing trend. It’s a foundational shift in how we interact with information, technology and each other.
The question is no longer if AI will change the way we work, learn and lead. The real question is how we choose to engage with it.
From Early Promise to Real-World Impact
The early years of AI were driven by logic and rules. Systems in the 1970s and 80s were designed to replicate expert decision-making by encoding knowledge into deterministic systems. These early models could diagnose diseases or configure complex systems—but they were narrow and brittle.
One of the most important barriers to progress was captured by Polanyi’s Paradox: “We know more than we can tell.” So much of human judgment is intuitive, contextual and hard to articulate. That made it difficult to capture in code.
The emergence of machine learning shifted the focus. Rather than programming logic line by line, we taught machines to recognise patterns in data. This brought success in areas like speech recognition, image classification and search.
Now we are in the midst of the third wave of AI—generative and probabilistic. These models don’t just analyse. They create. They predict. They engage in conversation. They can write code, generate artwork, summarise documents, simulate dialogue and more. And they are doing so with a level of fluency and accessibility never seen before.
Why Now?
Several forces have converged to accelerate progress.
First, we have the data—massive corpora of human language, behaviour and knowledge captured across the internet. Second, we have the compute—high-performance GPUs and cloud platforms capable of supporting enormous models with hundreds of billions of parameters. Third, the algorithms have matured, particularly with the introduction of transformers, which underpin modern large language models.
What once required a team of engineers now lives in a simple chat interface available to anyone.
The Global Dynamics Behind the AI Race
AI is not just a technological story. It’s a geopolitical and economic one too.
The GPU race is a strategic contest. Access to advanced chips and compute infrastructure is becoming a lever of national influence. Countries including the United States, China, Taiwan, the Netherlands and Australia are actively shaping AI capabilities through investment, restriction and partnership.
At the same time, governments are scrambling to define policy settings that balance innovation with safety. The EU AI Act is introducing a compliance-first approach, classifying AI systems by their potential risk. The US AI Bill of Rights is setting guardrails focused on fairness and transparency. In Australia, the Federal Government’s AI consultation paper reflects a desire to engage industry, academia and civil society in shaping an appropriate regulatory path.
The policy debate is shaped by a familiar tension: the push and pull between innovation and control, between opportunity and risk.
Key Considerations for Leaders
As AI continues to evolve, leaders need a structured approach to experimentation and adoption. The temptation to “wait and see” is understandable—but those who act early will gain the most insight, capability and momentum.
Here are six key considerations when working with AI in your organisation:
1. Experiment, build, test and learn.
This is not a spectator sport. Get hands-on with the tools. Run pilot projects. Use them in daily workflows. The best way to understand AI’s potential is to use it.
2. Shape your own strategy and policies.
Don’t wait for regulators to define your boundaries. Start shaping your organisation’s approach now. Set clear principles for how AI will be used, and by whom. Think big, but start small.
3. Build a strong safety capability.
AI success hinges on more than just technical excellence. Ethics, compliance, oversight, privacy and red teaming need to be embedded into the way AI is evaluated and deployed. A “move fast and break things” mindset won’t cut it here.
4. Stay vendor-agnostic.
The foundation model ecosystem is rapidly evolving. What looks like the best tool today may not be tomorrow. Avoid lock-in by designing flexible, modular approaches that allow you to adapt as the market matures.
5. Know what you are buying.
Interrogate vendor claims. Ask about training data, model transparency, risks and limitations. Many AI solutions today are a blend of marketing and magic—your job is to separate the two.
6. Start internal before going external.
Use AI with internal teams, lower-risk data sets and support functions first. This allows you to build capability, identify weaknesses and refine your controls before scaling to customer-facing or higher-risk applications.
Beyond Technology: A Human-Centred Shift
Perhaps the most exciting frontier is not the technology itself, but what it means for how we work.
The human-computer interface is being reimagined. We are moving from systems to conversations, from rigid commands to fluid collaboration. AI is becoming a co-creator, an advisor, a second brain. It enables us to move faster, explore further and do more with less.
Where the internet democratised access to knowledge, AI will democratise capability. The ability to build, write, analyse or design is no longer limited to those with specialist training or deep technical expertise.
This is not about replacing people. It’s about amplifying human potential. It’s about reducing friction and unlocking new ideas.
The Opportunity Ahead
We’re standing at the beginning of something extraordinary.
Yes, AI comes with challenges—ethical dilemmas, misinformation risks, privacy concerns. But it also comes with extraordinary upside. Productivity. Innovation. Inclusion. Insight.
The future of AI will not be determined by the biggest labs or fastest chips alone. It will be shaped by how organisations choose to engage—with clarity, curiosity and courage.
This is AI’s endless summer. And it’s only just begun.
Let’s make it count.