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Mark Beccue

Mark Beccue is a veteran market research analyst with more than 25 years of experience in market research and business strategy. Mark is one of a handful of pioneering analysts who began to focus on AI market research in 2015. Today with Futurum Group and previously as a principal analyst within the AI practice area at Omdia and Tractica, he has advised clients and provided them with syndicated and custom qualitative AI research services. His expertise in AI use cases, applications and software, natural language AI and broader trends surrounding AI market adoption have made him a well-known and sought after speaker, panel moderator, conference chair and media resource within the AI business community. He has served in those roles for events including the AI Summits in London, Singapore, New York and Silicon Valley, IOT World, Smart Home Summit, UX Next and Telco AI Europe. Prior to joining Tractica, Mark was an independent market research analyst focused on emerging technologies. Before going independent, Mark served as in house market intelligence analyst for Syniverse, where he helped guide overall business and product line strategies. For 4 years Mark worked as a Senior Analyst for ABI Research, a global technology research firm, focusing on mobile consumer services. Prior to ABI, Mark worked for 10 years for Syniverse in product management, greenhouse innovation and marketing. Specialties: AI B2B and B2C market intelligence, analysis and insights. Natural Language AI. Operationalizing AI in the Enterprise. AI market adoption trends and issues. Strengths - AI and other technical market analysis designed for business readers, writing, thought leadership, forecasting, market sizing

5 articlesLinkedIn ↗
Contributor Brief·Mark Beccue · 5 articles
Updated May 14, 2024

Infrastructure, not software, determines AI competitiveness and requires proactive risk governance

Beccue argues that AI success depends fundamentally on hardware infrastructure decisions made today, not tomorrow—and that this infrastructure reality applies equally to on-premises and cloud deployments. He further contends that responsible AI adoption demands risk management systems implemented before deployment, not reactive safeguards after problems surface.

4

major US AI firms forming collaborative risk governance alliance

“Hardware infrastructure, not just software, determines who wins and falls behind.”

Strategic Investments In AI Accelerators Ensure Ongoing Competitiveness in the Generative AI Era

AI adoption success factors across infrastructure and governance dimensions

AI accelerator hardware investment as competitive necessity9
Data center compute capacity as primary AI bottleneck8
Risk management systems required before AI deployment8
On-prem versus cloud parity in infrastructure demands7

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28%AI accelerator
AI accelerator hardware investment as competitive necessity
Data center compute capacity as primary AI bottleneck
Risk management systems required before AI deployment
On-prem versus cloud parity in infrastructure demands

3

major risk safeguards: watermarking, differentiation, standards establishment

Businesses must rethink their infrastructure strategy as AI workloads reshape what data centers can actually deliver.

On-Prem or Cloud? Either Way, Data Center Compute Demands Necessitate the Need for AI Accelerators (Pro AV)

Responsible AI adoption requires robust safeguards before deployment, not after problems emerge.

As Organizations Ask If "Should" Launch AI Projects, A Risk Management System Becomes Essential (Pro AV)

“Companies racing to deploy generative AI are discovering hardware infrastructure determines winners.”

Themes:Hardware infrastructure as competitive moat in AI eraRisk governance must precede, not follow, AI deploymentOn-prem and cloud parity demand identical accelerator investments

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