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AMD was founded in 1969 by Jerry Sanders and a group of engineers from Fairchild Semiconductor.

Dr. Lisa Su joined as Senior VP and General Manager, Global Business Units in 2012, becoming CEO in 2014 and reimagining what the ailing business could do.

Whilst she brought new vision and direction to the company, Su was under no illusion that the road to success would be long and fraught with challenges along the way. Nothing of significant value was ever going to be achieved quickly.

Under Su’s leadership, AMD delivered an exceptional turnaround with a focus on high-performance computing and high growth areas such as datacenter and AI. Such is this transformation that the market valuation is now approximately $400 billion at the time of writing, representing a valuation uplift in excess of 9,000% since Lisa Su took over.

So, how is AMD now a preeminent leader, when formerly considered a laggard?

Simplified product offering

Su made a strategic shift to reduce the number of areas AMD were working on and instead focus on high performance CPU and GPU architecture, competing directly with fast moving competitors such as Intel and NVDIA, only this time, with a renewed focus due to the removal of many distractions that previously brought strain on valuable resources.

Prior to Su’s leadership, AMD sold off its chip manufacturing business, which was helpful in the later turn around as it reduced further strain on resources.

Clear Business Structure

AMD is currently organized into four key business areas, allowing for a clearer strategy and focus in each of these segments.

1. Datacenter: High-growth core, generating the largest revenue share. It focuses on CPUs and AI-enabled GPU accelerators for cloud, enterprise, and supercomputing.

2. Client: Focused on Ryzen CPUs and APUs for desktop and laptop PCs, including the new Ryzen AI processors.

3. Gaming: Focussed on Radeon GPUs for consumer graphics and the lucrative semi-custom chips designed for major gaming consoles such as PlayStation and Xbox.

4. Embedded: Focuses on specialized computing solutions for industrial, automotive, and communications applications.

R&D and Strategic Acquisitions

AMD invested internally in R&D despite their financial constraints, understanding that investing in new ideas could lead to breakthroughs that could help them identify new opportunities and enable them to leapfrog competitors through a first-mover advantage.

Investing in R&D enabled AMD to develop the Zen architecture, which became the foundation of their modern day high performance processors.

With a clearer view of their strategic focus, AMD was able to purposefully acquire companies that fit their future direction. Notable recent acquisitions include:

• Xilinx (Completed February 2022): A significant acquisition for approximately $49 billion. Xilinx is a leader in Field-Programmable Gate Arrays and adaptive System-on-Chips, strengthening AMD's datacenter, embedded systems, and communications capabilities.

• Pensando (Announced April 2022): Acquired for approximately $1.9 billion. Pensando is focused on high-performance, programmable packet processors (DPUs) and a cloud-centric distributed services for enterprise and cloud datacenters.

• Silo AI (Announced intent to acquire, September 2024): Acquired a leading European AI lab to bolster its AI software and services expertise.

• ZT Systems (Acquired March 2025, the manufacturing unit was later divested): Acquired for approximately $4.9 billion. ZT Systems is a provider of rack-scale AI and cloud computing datacenter equipment.

• Untether AI Team (Strategic agreement to acquire employees, Announced Mid-2025): Acquired the team of AI hardware and software engineers to advance AMD's AI compiler and kernel development capabilities.

• Brium (Acquired Mid-2025): Acquired for its compiler and AI software experts with deep expertise in machine learning, AI inference, and performance optimization to strengthen AMD's AI solutions.

• Enosemi (Acquired May 2025): A silicon photonics startup acquired to support the development of photonics and co-packaged optics solutions for next-gen AI systems, enabling faster data movement in server racks.

Embedded within each of AMDs business lines is of course AI, where AMD is heavily investing in expanding AI variants of it’s offerings. This approach enables AMD to be ready for the compute workloads that are emerging today, and will become a staple in the near future. Examples of AMD’s AI products include:

• Instinct MI350 Series: Datacenter GPUs focussed on accelerated next-gen AI training and inference compute.

• Instinct MI450 GPUs: Next generation GPUs for AI infrastructure and large deployments, expected late 2025/2026.

• ROCm Open Software Ecosystem: Open-source AI software stack, enables developers to deploy models on AMD accelerators.

Lisa Su's leadership blends strategic transformation, patience and resilience in an industry that is known to be cyclical. Su’s key leadership attributes include:

• Strategic Vision and Execution: Focused the company on a clear, simplified product roadmap focussed on high performance compute and AI.

• Resilience and Problem-Solving: Su’s personal habit of, "Running towards problems," creates a culture of accountability and innovation, pushing the company to tackle its weaknesses head-on.

• Collaborative and Empowering: Su emphasizes empowering her team and promoting a culture of open communication and collaboration to drive innovation from the ground up.

• Focus on Long-Term Value: The strategy focuses on sustainable growth by aligning the product roadmap with enduring customer needs (performance, efficiency, and affordability), which secured the company's relevance in emerging tech like AI.

Phase Gates

In the semiconductor industry, which involves high risk, multi-year development cycles, AMD primarily utilizes a structured, sequential approach known as the Phase Gate Process for product development and new product delivery. Phase gates are quality control checkpoints at the end of each phase to confirm key criteria are met before the product moves to the next stage.

If like me you’ve come from a project and program delivery background, you’ll think that phase gating may be a thing of the past due to being synonymous with waterfall delivery methods, however, when operating in an environment with annual hardware release cycles and complex supply chains, having gated controls can be essential to ensure quality and time expectations are met.

Integrated Design

To ensure the physical chip and software stack are delivered successfully and reliably, AMD integrates specific technical methodologies into the development process:

• Design for Test: Prioritizing the ability to detect problems quickly and identify root causes during the design and manufacturing phases, extending throughout the product lifecycle.

• Design for Manufacturability (DfM): Mitigating risks and optimizing operational excellence by focusing on how a device will be produced at scale, ensuring it meets quality and reliability standards.

• Design for Reliability (DfR): Employing advanced methodologies, including machine-learning, to shorten development processes and meet stringent reliability requirements for critical applications.

• Product Validation: Conducting pre-silicon activities (modelling and simulations) and intensive post-silicon testing (for speed, functionality, and reliability) in parallel with software validation to ensure seemless operation.

Core competitive advantages AMD now boasts include:

A round-up of some of AMD’s key achievements under Lis Su’s stewardship include:

NVDIA is the clear market leader in GPUs, as they have first mover advantage through their CEO’s vision and lengthy investment in R&D. NVDIA now powers a significant portion of AI training and related software across all industries.

Knowing that AMD could at best come second to NVIDA in the AI training compute space, they dedicated significant R&D into the next wave of AI compute demand, which is Inference.

AI training is where machine learning models learn from large automated datasets to help predict with accuracy. AI training is highly compute hungry requiring high-end GPUs and memory.

AI inference is where the trained model is used to make real-world predictions or classifications on new unseen data. This type of compute requires less powerful GPUs, CPUs or edge devices, and it’s demand is expected to grow exponentially as more hardware and software use cases arise.

In theory, our daily need for inference will eventually far surpass the need for training compute, and this has been AMD’s focus in recent years, ready for what lies ahead.

Simplify your focus

One of the first actions Lisa Su undertook when taking over as CEO was to simplify their product roadmap and refocus valuable resources on key areas where they can succeed and take significant market share. This meant cancelling great ideas to allow focus on a few exceptional ideas.

This reminds me of Steve Jobs, who behaved in exactly the same way when he rejoined Apple in his second stint as CEO. He cancelled many projects and products, focussing on a handful of winning ideas, which led to the development of the iPod and later the iPhone.

Quite often, less is more and saying “no” to good or great ideas is a powerful act of discipline and leadership, enabling teams to excel in bringing exceptional ideas to life.

Invest in yourself

Investing internally in R&D and strategic acquisitions may have appeared a drain on valuable resources when the focus should have been on cost cutting and short term profitability, however, making those strategic choices to invest in new ideas, as well as acquiring the skills and technology through acquisition, helped AMD to absorb potential competitors within their own entity, and increase their speed to market.

R&D is a long term endeavour that requires vision and an acceptance that the return on investment may take years to be realised, however thinking short term without investing in R&D may solve immediate financial challenges, but will not set up the business for long term survival or growth.

Long term means long term

Playing the long game really means playing the long game. Most of us think in months or perhaps a couple of years, Dr. Lisa was planning in decades with the understanding that anything worthwhile rarely happens quickly.

There is a saying that it takes 10 years to become an overnight success. Ultimately what people don’t see is the failures, lessons and incremental wins along the way. That said, it doesn’t mean we should accept that success in the workplace should take decades, there are certainly fast track routes to individual career progression and corporate market dominance, but transformative change takes time to cultivate and eventually reap the rewards.

Start creating the future today

Finding an space that no competitor is playing in, and quietly becoming a master in that field will increase your value and market position. This is true on an individual basis and from a corporate strategy perspective. Whilst AMD has grown exponentially since Lisa Su took over, there is more growth ahead due to their focus on AI inference technology ad other bets.

Whilst the market for AI inference is very nascent and barely existed when AMD were preparing for it, they had to take bold steps and invest early with the confidence that they could lead a new industry before the opportunity became obvious to potential competitors.

Focussing on todays technology is important, but seeking to build lasting success, creating and leading tomorrows innovation is a must.

This requires long term planning, envisioning future trends and creating what you believe to be the future before people are asking for it, so that it’s ready when they need it. Thinking ahead in this way enables first movers to become eventual market leaders.

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