Tech Transformed

The semiconductor industry is undergoing one of its most profound transformations in decades. Driven by the insatiable demand for compute power largely fueled by AI workloads, engineers are moving away from traditional monolithic chips and shifting toward complex multi-die designs. This shift brings a new set of challenges that conventional design and validation methods simply cannot handle.
In a recent episode of the Tech Transformed podcast, host Dana Gardner sat down with Shekhar Kapoor, Executive Director of Product Line Management at Synopsys, to explore how the growing complexity of semiconductors is changing the way engineers design and validate modern systems. From thermal management to AI-driven automation, the conversation reveals why the old way of building chips is no longer good enough and what the future looks like.
Multi-Die Design
Kapoor explains that the transition to multi-die design is no longer a matter of preference but a necessity. He attributes this shift to the relentless demand for greater compute capacity, driven largely by the rapid growth of AI.
Traditional monolithic chips are hitting hard limits. Reticle sizes are maxing out, and rising yield and cost challenges make it increasingly impractical to pack more functionality onto a single die. Multi-die designs solve this by disaggregating functionality across smaller dies, each targeting the most appropriate process technology, then integrating them into a unified, optimised package.
Leading AI systems already integrate multiple compute and I/O dies alongside large high-bandwidth memory (HBM) stacks, scaling to 3x–5x reticle-class designs and beyond. The design challenge is very different. As Kapoor puts it: "You're no longer optimising a single chip, you're optimizing a system of chips."
This requires system-level co-design from day one, spanning architecture, silicon, packaging, power delivery, and interconnect strategy simultaneously. Engineers must think in terms of System Technology Co-Optimisation (STCO), not just chip-level optimization. The design tools, methodologies, and team workflows all need to change. For engineers and technology leaders looking to explore these trade-offs, Synopsys has published a comprehensive eBook on accelerating multi-die design and innovation.
Thermal Analysis and Multi-Physics Validation
Historically, thermal, power, and electromagnetic analyses were performed as downstream validation steps once the core design was complete. In a multi-die world, that approach is no longer viable.
"Thermal management is becoming the number one issue when designing these multi-die designs. It has to be managed across a range of scales, from transistor activity to package and board level," Kapoor says.
The problem with late-stage validation is timing. By the time thermal or power integrity issues surface, the most critical decisions are already locked in floorplans, interconnect topologies established, and packaging assumptions embedded.. At that point, the only options are costly ECOs, excessive margining, or a full redesign. Industry estimates suggest over-design can lead to up to 30-35 per cent wasted silicon and hundreds of millions of dollars in optimisation loss.
The solution is a shift-left approach that embeds multiphysics analysis from the earliest stages of design. When thermal hotspots, voltage drop issues, and electromagnetic interactions are identified early, engineers can adjust partitioning and placement strategies before they become expensive problems.
This is the methodology detailed in the Synopsys ebook on Multiphysics Fusion for multi-die design, which covers how teams can build continuous multiphysics validation into their flows to avoid late-stage surprises and protect both performance and reliability.
Multiphysics Fusion and AI-Driven Chip Design
To operationalise the shift-left methodology at scale, Synopsys has introduced the concept of Multiphysics Fusion. This is the native integration of AI-powered EDA technologies with ANSYS's gold-standard multiphysics sign-off analysis capabilities.
Within the 3DIC Compiler platform, this means unifying the implementation environment with RedHawk-SC, RedHawk-SC Electrothermal, and HFSS-IC technologies. This brings IR drop, thermal, signal, and power integrity analysis directly into the design loop. The result is greater predictability, tighter correlation between in-design analysis and sign-off, and significantly fewer design iterations.
The impact on design closure times has been substantial. According to Kapoor, teams using the Multiphysics Fusion solution have seen turnaround times shrink "from weeks to days, and in some cases even hours" even for large, high-performance multi-die designs.
AI amplifies these gains further. Synopsys employs AI in two primary ways: assistive automation through its 3DSO.ai technology, which integrates multiphysics feedback into the optimization loop in real time, and agentic workflow orchestration, which becomes increasingly critical as system complexity scales toward designs incorporating hundreds or even thousands of GPUs. As Kapoor notes, at that scale, "agentic workflows could help engineers converge faster" and manage trade-offs that would otherwise be intractable. If you would like to find out more about this, download the full eBook: Multiphysics Fusion Technology for Multi-Die Designs Explained from Synopsys, which expands on each of these themes with real-world examples, design methodologies, and guidance for implementation teams. You can also connect with Shekhar Kapoor on LinkedIn.
Takeaways
Multi-die architectures and their drivers.
Challenges of traditional monolithic chips.
Importance of early multi-physics analysis.
Multiphysics fusion and its benefits.
AI's role in design automation.
Reducing time-to-market through integrated platforms.
System-level co-design.
Thermal management in 3D IC stacking.
Shift left approach in multi-physics validation.
Future trends in semiconductor design.

Chapters
00:00 Introduction to Semiconductor Complexity
02:00 The Shift to Multi-Die Designs
04:30 Challenges in Multi-Die Design
08:11 The Importance of Early Multi-Physics Analysis
10:05 Introducing Multiphysics Fusion
12:37 AI's Role in Semiconductor Design
16:37 Reducing Time to Market
19:39 Applications Beyond AI
21:12 Real-World Examples of Multi-Physics Validation
26:20 Practical Advice for Engineers

When most people hear "digital divide," they picture communities without broadband. But in 2026, that definition is dangerously outdated. "The digital divide is no longer just about internet access." These words from Graeme Gordon, Chief Executive Officer of Converged Solutions Group, set the tone for one of the most pressing conversations in technology today.
In this episode of Tech Transformed, host Trisha Pillay sits down with Gordon to unpack the changing digital divide, the massive impact of AI adoption, and what it truly takes. Gordon, whose background spans electrical engineering, oil and gas robotics, and three decades of founding and scaling tech companies, says that the new digital divide is about meaningful participation in the AI-driven economy, not just connectivity.
“More people are connected than ever before,” Gordon explains. “But connection without capability is just noise.” He points to mobile internet adoption as a case in point. Billions of people now access the internet via smartphones. However, the gap between scrolling social media and using cloud-based AI tools to build products and services remains wide.
This participation gap is the new frontier of digital exclusion. The implications stretch well beyond individual users. Organisations, governments, and education systems that fail to close this gap risk being locked out of the innovation economy entirely.
AI Adoption Without Education
Few developments have accelerated the digital divide conversation quite like the arrival of ChatGPT in late 2022. Gordon calls it plainly: "ChatGPT has disrupted and transformed the sector," and not just for technologists. The tool put generative AI in the hands of business professionals, students, and everyday users almost overnight.
Gordon says it's time to rethink our approach to AI. At a recent event he attended with 100 business leaders in the room, every hand went up when asked if they had used an AI platform in the last 24 hours. When asked who had received any formal training on how to use those tools, not a single hand was raised. This is the core paradox of AI adoption today. The tools are everywhere. The understanding of how to use them safely, strategically, and effectively is not. Without structured digital literacy and education, rapid AI adoption becomes a liability rather than an asset for individuals and organisations alike.
Barriers to Digital Inclusion
Gordon identifies several interconnected barriers preventing organisations from fully participating in the digital economy. Let's have a look:
Skills gaps remain the most acute. Technology evolves faster than most training programmes, let alone formal education curricula. University degrees and annual school terms were not designed for the pace of AI-driven change.
Trust and credibility are equally critical. Gordon warns of what he calls "AI slop", the growing proliferation of AI-generated content and half-built solutions that look polished but lack substance or security. Organisations that rely on AI without proper oversight risk undermining the customer trust they're trying to build.
While infrastructure quality is improving globally, it still creates disparities, particularly around data sovereignty. The question of where your data sits, who can access it, and under what compliance framework is no longer just a legal concern. It is a competitive and ethical one.

Sovereign AI
One of the most forward-looking concepts Gordon introduces is sovereign AI, the idea that organisations must control not just their data, but the AI infrastructure that touches it. Just as data sovereignty became a boardroom priority, AI sovereignty is now following the same path.
"Business leaders type sensitive information into ChatGPT or Copilot without thinking twice," Gordon cautions. The solution isn't to avoid AI, it's to build internal AI agents and platforms that interact with large language models without exposing proprietary data to the open web. This is why hyperscaler data centres are appearing in unexpected geographies: latency is secondary; sovereignty is the driver.
Gordon's advice to business leaders is refreshingly direct: go experiment. "You won't break anything," he says. The AI-driven economy rewards curiosity, iteration, and speed of learning, not perfection. Leadership teams need to model responsible AI use, invest in upskilling their people, and treat education as a strategic asset. This applies as much to frontline healthcare workers as it does to C-suite executives.
If you would like to find out more, connect with Graeme Gordon on LinkedIn.
Takeaways
The evolving digital divide from access to participation.
Impact of AI and ChatGPT on business and society.
Importance of secure and sovereign AI infrastructure.
Role of education in digital literacy for all.
Leadership strategies for AI adoption and trust.
Barriers to digital inclusion: skills, trust, infrastructure.
Practical steps for organisations to implement AI responsibly.

Chapters
00:00 Understanding the Digital Divide
02:49 The Role of AI in Participation
06:01 Barriers to Digital Adoption
09:07 The Importance of Education
11:45 Building a Secure AI Foundation
14:51 Trust and Credibility in AI
18:11 Practical Advice for Organisations

AI isn't just speeding up recruiting; it's actually forcing companies to redesign work itself, blending human judgment with agentic execution across hiring, mobility, and skills development. As a result, most conversations these days are about AI in the enterprise centre on software development and engineering. Recruiting, hiring, and talent management get far less attention, but they may be where AI's impact is most immediate.
In a recent episode of Tech Transformed, host Dana Gardner spoke with Meghna Punhani, Chief People Officer at Eightfold AI, about how organisations are rethinking talent acquisition, workforce planning, and employee development in an AI-driven world. Meghna Punhani's perspective is shaped by nearly two decades at Google, a stint leading employee experience at Palo Alto Networks, and her current dual role at Eightfold AI, where she both leads the people function and helps build the product her team relies on. That vantage point gives her a practical, ground-level view of what works and what doesn't when AI meets HR.
Reimagining the Talent Lifecycle with AI
Punhani's central argument is that most legacy HR systems were designed for a different purpose, one that has evolved as work itself has changed and the workforce now includes AI agents alongside people. Simply bolting automation onto existing processes, she argues, isn't enough. Organisations that are succeeding are the ones re-engineering roles, workflows, and organisational structures from the ground up, treating this as an operating-model shift rather than an IT upgrade.
This shift touches the entire talent lifecycle, from how companies find candidates and evaluate skills instead of just job titles to how they support internal mobility. Punhani points out that skills now have a much shorter shelf life than in the past, which means static job descriptions are giving way to dynamic, skills-based decision-making. AI, she says, helps surface pathways for employees that traditional resumes and titles would never reveal, including her own nontraditional route into HR leadership.
How AI Is Reshaping Workforce Strategy
Trust is the recurring theme throughout the discussion. Punhani is candid that employees often fear AI-driven decisions, especially around jobs and evaluations. Her approach is focused on transparency first. When Eightfold rolled out digital twins internally, employees were uneasy until leadership explained how the technology worked and used it themselves, which helped build organisation-wide confidence.
That same principle shows up in Eightfold's own hiring practice. One example is the company's campus recruiting programme in India, where its AI interviewer conducted roughly 90 per cent of interviews. This enabled recruiting to scale from around eight or 10 university partners to more than 150, and from approximately 5,000 applications to 15,000, without pulling engineers away from their day-to-day duties.
Time-to-offer dropped from around six weeks to as little as four days in some technical roles, largely because interviews could happen around the clock rather than around a recruiter's or hiring manager's schedule. Beyond recruiting, Eightfold's internal initiative, nicknamed Project Andromeda, applies the same re-engineering approach across sales and finance, reportedly reclaiming thousands of employee hours through redesigned, agent-assisted workflows.
AI and the Future of Talent
Looking ahead, Punhani doesn't frame AI as a threat to human contribution, but she frames it as an amplifier of it. As tools become more accessible across every function, she believes the people who will succeed won't be the ones who know the most facts, since AI can answer those questions. Instead, it will be the people who ask better questions, orchestrate multiple AI agents, and apply judgment where the right answer isn't obvious.
For HR leaders specifically, Punhani's advice is to claim a seat at the table now, rather than letting AI adoption happen without a people-first lens. This means learning the technology firsthand, demonstrating its value to non-technical teams, and partnering closely with CTOs and CIOs to shape decisions jointly. Her advice for individuals entering this shifting job market is similarly grounded: focus on learning agility over any single technical skill, since the skills in demand today may look different within months.
Future of AI in Talent Management
Across the conversation, Punhani returns to one idea, and that is AI in talent management isn't primarily a technology problem; it's a leadership and trust problem. Organisations that treat it that way, redesigning work with both humans and agents in mind, are the ones seeing measurable gains in speed, candidate experience, and internal mobility.
For HR leaders exploring AI adoption, the takeaway from this episode is to start before you feel ready, build trust through transparency, and let AI handle evaluation and execution so people can focus on judgment, empathy, and connecting the dots across the organisation. If you would like to find out more, visit eightfold.ai or connect with Meghna Punhani on LinkedIn.
Takeaways
AI's impact on talent acquisition and management.
Reengineering work processes with AI.
Building trust and transparency in AI systems.
Skills-based internal mobility and workforce planning.
AI-driven candidate evaluation and employee development.

Chapters
00:00 Introduction to AI in Talent Management
02:59 Understanding AI's Role in Talent Acquisition
06:07 AI's Impact on Workforce Planning and Skills Development
10:02 Building Trust in AI for Hiring Processes
13:04 Internal Use of AI at Eightfold AI
18:58 Measuring ROI from AI in Talent Acquisition
25:02 Enhancing Candidate Experience with AI
29:53 Future Directions for AI in Talent Management

With enterprises now rushing to integrate AI agents into their operations and security, the most imperative focus now becomes the AI model itself. However, Eric Tschetter, Chief Architect at Imply, believes the real challenge is within the data infrastructure that supports these systems.
In the recent episode of the Tech Transformed podcast, Kevin Petrie, BARC Vice President of Research, sat down with Tschetter to talk about how AI is actually increasing the current needs around scale, performance, and data access.
“Agents are always running queries. They’re always doing stuff,” Tschetter stated.
Unlike human analysts, AI systems work continuously, producing much higher query volumes and putting more pressure on the data platforms underneath. This leads to a greater demand for observability architectures that can manage more data, more users, and more machine-to-machine interactions without losing speed.
For Tschetter, the solution is not to create new observability tools, but to rethink the data layer that supports them.
Key Takeaways
AI is transforming observability and security disciplines.
The observability warehouse concept is gaining traction.
AI agents increase the volume of queries significantly.
Data silos remain a major challenge for enterprises.
Collaboration between IT and security teams is essential.
Observability and security teams often consume the same data.
A decoupled architecture can enhance data accessibility.
The semantic layer must support multiple query languages.
Effective data management is crucial for AI-driven workloads.
Data should be stored once and accessed from multiple platforms.

Chapters
00:00 Introduction to AI and Observability
02:08 Challenges in Observability with AI
06:44 Modernising Architecture for Observability
10:49 Decoupled Observability and Semantic Layers
16:31 Collaboration Between IT and Security Teams
22:23 Imply's Observability Warehouse and Data Lakes

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Across every industry, boards are approving AI budgets. Inside many enterprises, however, the reality is the same. Pilots never scale, tools sit unused, and transformation programmes struggle to justify their investment. In this episode of the Tech Transformed podcast, host Trisha Pillay sits down with Darin Patterson, VP of Product Advocacy and Market Strategy at Make, to find out what separates the organisations genuinely operationalising AI from those still running expensive experiments.
AI Adoption Gap
Enterprise AI investment is accelerating. What is not accelerating at the same pace is business value. Patterson is direct about why he believes that most organisations are measuring the wrong things, assigning ownership to the wrong people, and deploying tools before they have defined the problem.
"The AI adoption gap is real," Patterson tells Pillay, "and it starts at the top. Leaders are approving investments without a clear framework for what success looks like."
For C-suite executives, this is a critical signal. AI adoption is not primarily a technology challenge; it is an organisational one. Strategy, culture, and accountability structures determine if AI initiatives produce compounding returns or accumulate as technical debt.
Ownership Models
One of the most instructive conversations in this episode concerns who should own AI inside an enterprise. Patterson's position is that ownership must live with the people closest to the business function being transformed.
"Ownership models are often unclear," he says. "And unclear ownership is where AI initiatives go to die."
When AI is owned exclusively by a central IT or data science function, it becomes disconnected from the operational realities of the teams it is meant to serve. When it is owned entirely by individual business units without central governance, you get fragmented tooling, inconsistent data practices, and security exposure. The hybrid model Patterson advocates centralises governance standards, security, and infrastructure while pushing execution authority down to functional leaders. This structure creates accountability at the point of value creation rather than at a remove from it.
For C-level executives building or restructuring their AI operating model, the actionable question is: do the leaders of each business unit have both the mandate and the capability to own AI outcomes in their domain?
Stop Starting With the Tool
A pattern Patterson sees consistently across enterprises is what he calls tool-first thinking. An organisation identifies a capable AI platform, deploys it, and then attempts to work backwards to the business problem it should solve.
"Focus on your business process first," he advises. "The tool is never the strategy."
This is especially relevant for executives evaluating vendor proposals. The quality of an AI platform matters far less than the clarity of the problem definition sitting upstream of it. Organisations that achieve sustainable AI ROI typically begin by mapping their highest-friction processes, quantifying the cost of those inefficiencies, and only then evaluating which AI capability best addresses the root cause. The discipline of process-first thinking also prevents a common failure mode by automating a broken process rather than fixing it. AI applied to a flawed workflow does not eliminate the flaw but rather accelerates it.
Culture Is the Multiplier
Patterson also points to a softer but critical success indicator, which is cultural adoption. If the teams closest to an AI deployment are not using it willingly and consistently, the business case will not hold, regardless of what the pilot showed.
The final, and perhaps most important, dimension Patterson raises is culture. Technical capability and strategic clarity are necessary but not sufficient conditions for AI success at scale. The organisations that are genuinely ahead are those that have invested in building an AI-literate workforce, not just an AI-enabled one.
"Invest in people as much as you invest in AI," Patterson says. "The technology will keep improving. Your competitive advantage comes from people who know how to use it well."
For C-level leaders, this means reframing AI investment as a human capability programme as much as a technology programme. Training, change management, and psychological safety around experimentation are not soft additions to an AI strategy, but they are core to its delivery.
Listen to the full conversation with Darin Patterson on the Tech Transformed podcast. Connect with Darin on LinkedIn and explore Make's automation platform at make.com.
Takeaways
AI adoption challenges
Organisational culture and AI
Ownership models for AI
Measuring AI success
Operational AI examples

Chapters
00:00 The AI Adoption Landscape
03:01 Bridging the ROI Gap in AI
05:48 Ownership and Responsibility in AI Implementation
08:57 Strategic Approaches to AI
11:57 Measuring Success in AI Initiatives
15:00 Cultural Transformation for AI Success
18:53 Real-World AI Implementation Examples
24:00 Advice for C-Level Leaders on AI Investment