Mohammad Nazzal

Large language models do not behave neutrally in economic decisions. In preference tasks, more advanced models become increasingly human-like and irrational. In belief tasks, they become more statistically rational. Simple role priming modestly improves performance, but deeper debiasing fails. AI agents inherit—and selectively correct—human behavioral distortions .

A tractable firm-level framework explains why countries grow at the frontier’s rate yet remain persistently behind it. Absorptive capacity, institutions, credit frictions, and technology appropriateness determine relative income levels—not long-run growth. Development becomes a question of incentive design, selection, and adoption speed rather than invention alone.

Democrats report higher workplace AI use and exposure than Republicans, but differences largely reflect education and occupational sorting rather than ideology. Once controlling for industry and job characteristics, partisan gaps disappear. AI adoption follows structural labor market patterns, suggesting technology diffusion mirrors human capital distribution more than political affiliation.

Artificial intelligence increases the speed and scale of information distribution, but it also weakens the economic foundations that sustain credible journalism and research. As misinformation becomes cheaper and truth more costly to produce, the information ecosystem faces structural instability, requiring strategic intervention to preserve long-term knowledge integrity.

Artificial intelligence holds the promise of accelerating scientific discovery, engineering breakthroughs, and productivity across industries. Yet the magnitude of its impact depends on three factors: the share of research tasks AI can perform, how productive AI is at those tasks, and the bottlenecks that limit overall progress. The paper presents a structured model showing that even extremely powerful AI produces limited gains if it operates on only a minority of research tasks. Conversely, broad automation across the research pipeline—far more than incremental improvements—determines whether...

Traditional SWOT often becomes outdated and superficial without real-time data. Integrating AI and machine learning allows organizations to quantify strategic factors, prioritize risks and opportunities, and refresh analyses regularly, transforming SWOT into a dynamic, credible decision support tool.

Research shows IT skills, not physical IT infrastructure, drive firm productivity and revenue growth. A 1% rise in IT skill intensity is linked to measurable sales gains and higher innovation output. Digital transformation strategies must prioritize workforce capability development over hardware investment to unlock sustained competitive advantage.

Big Tech plays a dual role in the startup ecosystem, providing capital and infrastructure while also creating competitive and dependency risks. Corporate venture investment can accelerate growth, but founders must manage talent, platform exposure, and governance carefully to preserve strategic independence and long-term innovation capacity.

Generative AI significantly boosts productivity for junior workers, increasing performance by up to 34%, while offering limited gains for senior employees. AI accelerates learning and reduces performance gaps, but its uneven impact requires strategic workforce redesign and tailored integration to maximize long-term organizational benefits.