TL;DR: AI adoption has surged across investment firms, but alpha is harder to find because everyone feeds models the same public data. The edge has shifted to proprietary primary research: verified, human-centric intelligence AI cannot scrape. Firms winning today feed their systems finished intelligence, not raw access.

Core Answer:

• Public data is commoditised. When all algorithms process the same information, speed replaces insight.

• BlackRock's proprietary model outperforms public LLMs by 11.6 percentage points because of data architecture, not algorithms.

• 83% of senior leaders say weak data infrastructure slows AI adoption. Better inputs, not more inputs, create differentiation.

• Primary research provides the "why" behind the "what." AI flags patterns. Human intelligence explains causation.

• Finished intelligence replaces middleman models. Verified, structured insights cost less, deliver faster, and require zero analyst logistics.

What Is the Information Paradox in Investment Research?

Organisational AI use jumped from 55% to 78% in twelve months. Generative AI adoption more than doubled. Every fund I talk to runs models, scans datasets, and deploys algorithms at scale.

Alpha is harder to find. The technology works. The problem is what people feed it. When every algorithm scrapes the same earnings transcripts, SEC filings, and sentiment data in milliseconds, the informational edge disappears. Public data has become a commodity.

Key Point: More AI adoption does not equal more alpha when all firms train models on identical public information.

Why Public Data No Longer Produces an Edge

You are not competing against slower analysts. You are competing against machines that process public information faster than you open the file. BlackRock's proprietary AI model achieved 61.3% accuracy forecasting post-earnings stock returns. GPT-4 and other public LLMs scored between 49.7% and 54.9%.

The difference was not the algorithm. The difference was the data architecture underneath.

Key Point: Proprietary data infrastructure separates high performers from the field. Algorithms are commoditised. Verified inputs are not.

How Weak Infrastructure Limits AI Performance

83% of senior business leaders say AI adoption would accelerate with stronger data infrastructure. Two-thirds say weak infrastructure holds them back. Legacy systems were built to move files, not insight. You cannot feed decision-grade intelligence into models when your architecture was designed for quarterly reporting cycles.

The edge belongs to firms that feed proprietary, verified, human-centric intelligence into their models. Not more data. Better data.

Key Point: Infrastructure is the bottleneck. Speed without quality creates noise, not conviction.

Where Does Alpha Hide in an AI-Driven Market?

91% of investment managers use or plan to use AI in research. AI itself is table stakes. Differentiation comes from what you feed the system. Public information is commoditised. Proprietary primary research is not.

Channel checks with the right executives. Surveys of decision-makers who control budget allocation. Expert interviews revealing why something is happening, not only what. AI tells you what. Primary research tells you why.

Traditional expert networks and survey platforms sell access, not answers. You still do the vetting, the interviewing, the note-taking, the verification. You are paying research prices for logistics work.

Key Point: Primary research fills the gap AI cannot close. Machines process patterns. Humans explain causation.

What Is Finished Intelligence and Why Does It Matter?

Firms pulling ahead are not collecting more calls or running more surveys. They buy decision-ready intelligence already verified, structured, and contextualised. They have stopped paying the middleman tax.

Expert networks charge £1,200 per call. When 40% of calls are useless, your real cost per useful insight climbs past £2,000. Add analyst time for scheduling, interviewing, and note-taking, and you burn 14+ hours monthly on logistics instead of conviction-building.

Modern primary research platforms eliminate the friction. You hand over a 10-minute brief. You get back finished intelligence: ID-verified, cross-referenced, structured for IC memos. No calls. No scheduling. No compliance exposure.

Key Point: Finished intelligence trades logistics for outcomes. You pay for answers, not access.

How Should Investment Firms Redesign Research Workflows?

AI high performers (the 6% generating 5%+ EBIT impact from AI) are not chasing incremental gains. They redesign workflows around transformation. They treat proprietary data infrastructure as a competitive moat.

Every AI vendor has access to public information. What they do not have is your enterprise data. Your verified expert insights. Your channel checks with the executives who control purchasing decisions. Alpha lives there.

The question is not whether you use AI. The question is whether you feed it intelligence your competitors cannot replicate.

Key Point: Proprietary primary research is the moat. Public data trains everyone's models. Private intelligence trains yours alone.

Frequently Asked Questions

Why does more data lead to less alpha?
When all firms access the same public datasets, speed replaces insight. Algorithms commoditise public information. Differentiation requires proprietary inputs AI cannot scrape.

How do proprietary AI models outperform public LLMs?
BlackRock's model beat GPT-4 by 11.6 percentage points because of data architecture, not algorithm sophistication. Proprietary, verified inputs produce better forecasts than raw public data.

What is the middleman tax in primary research?
Expert networks charge £1,200 per call. With 40% useless calls, real cost per useful insight exceeds £2,000. Analyst time for scheduling, interviewing, and note-taking adds 14+ hours monthly. You pay research prices for logistics work.

What is finished intelligence?
Finished intelligence is decision-ready research: ID-verified, cross-referenced, structured for investment memos. You hand over a brief. You receive verified answers without calls, scheduling, or compliance exposure.

How does primary research complement AI?
AI identifies patterns in existing data. Primary research explains causation behind those patterns. AI tells you what is happening. Humans tell you why.

Why is data infrastructure a bottleneck for AI adoption?
83% of senior leaders say weak infrastructure slows AI adoption. Legacy systems move files, not insight. Real-time, verified intelligence requires architecture designed for speed and quality.

Who benefits most from proprietary primary research?
Portfolio managers, analysts, and deal teams at hedge funds, private equity, and venture capital firms running concentrated books and tight diligence timelines. Anyone paid to be precisely right, not generally informed.

What separates AI high performers from the rest?
The 6% generating 5%+ EBIT impact treat proprietary data infrastructure as a moat. They redesign workflows around transformation, not incremental optimisation. They feed models intelligence competitors cannot replicate.

Key Takeaways

• Public data is commoditised. When all algorithms process identical information, informational edge evaporates.

• Proprietary data architecture separates high performers from the field. BlackRock's model outperformed public LLMs by 11.6 points because of inputs, not algorithms.

• 83% of senior leaders say weak infrastructure limits AI adoption. Better data, not more data, creates alpha.

• AI identifies what is happening. Primary research explains why. Causation requires human intelligence, not pattern recognition.

• Finished intelligence replaces the middleman model. Verified, structured research costs less and delivers faster than access-based platforms.

• The 6% of AI high performers treat proprietary intelligence as a competitive moat. They feed models data competitors cannot replicate.

• Alpha lives in proprietary primary research: channel checks, expert interviews, and verified surveys traditional networks cannot deliver at scale.