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Track the AI Boom Without the Bubble: Scraping Transition Stock Signals (Defense, Infrastructure, Materials)

UUnknown

2026-02-15

9 min read

Build a signal feed that scrapes procurement, filings, and news to find durable AI exposure in defense, infrastructure, and materials.

Hook: Track the AI boom without the bubble — for data teams and investors

If you’re a data engineer, quant, or investor frustrated by noisy headlines and headline-driven market froth, you need a different signal set: real procurement, contract awards, and filings that show capital actually shifting into AI-related defense, infrastructure, and materials. This article maps a practical, production-ready data product that scrapes news, SEC filings, and procurement announcements to surface non-bubble exposure to AI — the "transition stocks" Bank of America flagged for 2026.

Executive summary: What you’ll get — fast

Most important takeaways first:

Data product design that combines news scraping, filings ingestion, and procurement monitoring into a single signal stream.
Concrete scraping patterns: APIs, RSS, EDGAR bulk, procurement portals (SAM, USASpending, TED), and robust anti-bot strategies (Playwright, residential proxies, caching).
Signal engineering: scoring rules that prefer contract value, repeat awards, and cross-source corroboration — tuned to defense, infrastructure, and materials sectors.
Pricing & packaging and integration options for analytics, ML pipelines, and investor dashboards.

Why this matters in 2026

By early 2026 the AI investment cycle split into two tracks: high-multiple platforms and an underlying capital buildout for compute, network, and mission systems. Bank of America and other institutional research teams point to defense, infrastructure, and transition materials as durable, non-speculative exposure to the AI wave.

Recent developments shaping this landscape:

Post-2025 ramp of CHIPS Act funding and national semiconductor initiatives increased government procurement and public-private projects for fabs and materials.
Defense budgets in late 2025 emphasized autonomy, AI-enabled sensors, and secure compute at the edge; contracts and RFPs accelerated in Q4 2025.
Data center and grid modernization projects expanded as cloud providers and telcos announced multi-year CapEx commitments in early 2026.

Define the target signals

Focus on signals that indicate capital allocation and product demand, not just sentiment:

Procurement awards & RFPs — contract value, awardee, scope mentioning "autonomy", "AI", "edge compute", "accelerators".
SEC filings — CapEx guidance, 8-Ks announcing contracts, 10-K/10-Q language drift toward "data center expansion", "specialized materials".
Regulatory & subsidy announcements — CHIPS grants, DOE grid modernization awards, regional incentives for buildouts.
High-quality trade press — procurement-focused outlets, defense journals, industry tenders corroborating awards.

Architecture: End-to-end pipeline

Design the pipeline to be modular, observable, and resilient to blocking:

1) Ingestion layer

News: RSS feeds, site APIs (e.g., Reuters, Bloomberg if licensed), keyword-based web scraping for trade sites.
Filings: SEC EDGAR API + bulk feed; use filings metadata to locate 8-K/10-K sections mentioning relevant keywords.
Procurement portals: SAM.gov API, USASpending bulk datasets, TED (Tenders Electronic Daily) feeds, local government procurement pages.

2) Parsing & enrichment

Extract structured fields: issuer, counterparty, award value, RFP id, dates, and named technical terms. Enrich with external datasets:

Company identifiers: CIK, ISIN, ticker mapping.
Industry taxonomy: NAICS, SIC, custom mapping for defense/infrastructure/materials.
Geo metadata: awarding agency, location of project.

3) Signals engine

Score each event using a composite rule-based + ML model:

Recency & decay
Contract value & award frequency
Cross-source corroboration boost
Sector weight: defense > government R&D; infrastructure weighted by CapEx scale

4) Storage & access

Store raw docs in object storage, parsed records in a columnar warehouse (e.g., Snowflake or BigQuery), and time-series signals in a metrics DB for dashboards.

5) Delivery & UX

API for programmatic access (signal-by-company, signal-by-sector).
Streaming alerts (webhooks) for high-confidence awards.
Dashboard with timeline, geo-heatmap, and co-occurrence graphs (vendors, materials, agencies).

Practical scraping patterns and code

Below are pragmatic snippets for production use. These are patterns you can start with and harden for scale and anti-bot defenses.

News scraping (RSS-first, fallback to Playwright)

Strategy: prefer RSS & APIs; fall back to headless browsers for JavaScript-heavy pages. Always respect robots.txt and site TOS for unlicensed sources.

# Python: fetch RSS, parse items, extract article url and headline
import feedparser
feeds = ["https://www.defensenews.com/feeds/all", "https://www.example-trade-site.com/rss"]
for url in feeds:
    feed = feedparser.parse(url)
    for item in feed.entries:
        # quick keyword filter
        text = (item.title + ' ' + item.summary).lower()
        if any(k in text for k in ['ai', 'autonomy', 'edge compute', 'accelerator', 'data center']):
            print(item.title, item.link)

Filings ingestion (SEC EDGAR)

Use EDGAR's bulk or API and target 8-K/10-K sections for contract mentions. EDGAR supports modern JSON endpoints for search.

# Example: query EDGAR for filings mentioning 'contract' and 'data center'
# Use the SEC API / full text search or download the filings bulk

Procurement portals

Prefer official APIs (SAM.gov, USASpending) and bulk data. For local government sites, normalize HTML scraping logic and index RFP IDs.

# Pseudocode: SAM.gov file download
# GET /api/procurement/v1/awards?keyword=AI&from=2025-01-01

Handling anti-bot, scale, and data quality

Real-world scrapers face blocking. Production approaches in 2026 include:

Respectful throttling and cache-first design to reduce requests.
Headless browsers (Playwright) for JS pages; rotate user agents and employ concurrency limits.
Proxy management: combine cloud and residential proxies for reliability; use IP affinity for sites with session-based access.
CAPTCHA handling: avoid bypassing terms; use human-in-loop where permitted and paid CAPTCHA-solving services only under compliant conditions.
Monitoring: collect HTTP status metrics, latency, and content-change diffs to detect throttling or paywall triggers.

Signal engineering: how to convert scraped items to investment-grade indicators

Raw scraped text is noisy. The following steps turn items into reliable signals:

Entity resolution: map mentions to canonical companies (CIK/ticker) using fuzzy matchers and knowledge bases.
Semantic extraction: parse monetary amounts, award durations, product names, and technical terms via rule-based NLP plus a transformer model for long documents.
Cross-validation: require corroboration from at least two source types for a high-confidence tag (e.g., procurement award + 8-K).
Score composition: combine normalized contract value, recency, source authority, and corroboration into a 0-100 signal.

Example scoring rule (pseudocode)

score = 10 * log10(1 + normalized_contract_value)
score += 20 if cross_source_corroboration
score += 15 if awarding_agency in ['DoD','DOE','GSA']
score += 10 if filing_type in ['8-K','10-Q']
score -= decay(days_since_event)  # exponential decay

Feature examples for machine models

Contract value, award count in last 12 months
Semantic proximity to AI terms (embedding similarity)
Supplier dependency (percentage revenue tied to defense/infrastructure)
Geopolitical risk flags (export controls, sanctions)

Use cases & datasets: pricing, SEO, monitoring

Design product tiers and dataset outputs to match buyer needs:

1) Pricing datasets (for quants & researchers)

Raw events feed (timestamped, parsed fields) — priced per API call or per event.
High-confidence signal feed — subscription for daily batches or stream.
Historical batch exports for backtesting (time-series with decay-adjusted signals).

Analysts and IR teams need to monitor narrative drift. Provide a content-delta dataset:

Keyword trend time series (weekly volume for "AI-enabled sensors", "edge accelerators").
Competitor mentions & linked procurement IDs — to identify peer contract wins.

3) Real-time monitoring & alerts (for portfolio managers)

Use webhook alerts for threshold-crossing events (e.g., award > $50M mentioning "AI"). Include confidence and links to raw sources for compliance audits.

Implementation checklist & examples

Checklist for an MVP:

Pick 10 authoritative sources across news, EDGAR, SAM.gov, TED. (Start small.)
Implement RSS + EDGAR crawler + SAM API fetcher.
Entity resolution pipeline mapping to tickers and CIKs.
Simple scoring and alerting; store events in warehouse.
Backtest signal vs. sector returns over 18–24 months to validate predictive power.

Legal & compliance notes

Scraping procurement records and public filings is generally permissible, but:

Always check terms of use and robots.txt; prefer APIs and public bulk data where available.
Avoid re-publishing full-text copyrighted articles without license; deliver extracted metadata and links instead.
Protect PII and adhere to data retention policies in your jurisdiction.
Consider commercial licensing for major news providers if you need full article content in product.

Commercial packaging and pricing strategy

Common pricing tiers that worked in 2025–26:

Freemium: daily digest email + limited API calls (good for analysts scouting ideas).
Standard: company-level signals, 3-month history, webhook alerts.
Enterprise: full historical exports, white-glove integration, on-premise connector for sensitive clients.

Charge by a mix of monthly subscription + per-event credits to align costs with heavy consumers (quant funds) while keeping the product accessible for smaller teams.

Case study (hypothetical, based on common patterns)

Q1 2026 — A quant firm wanted non-bubble exposure to AI. They ingested:

SAM.gov award feed (filtered for >$10M and keywords)
EDGAR 8-K stream for disclosed vendor contracts
Selected trade press RSS feeds for corroboration

After three months their transition-stock signal (score>70) strongly correlated with outperformance in a basket of mid-cap defense suppliers and materials firms involved in advanced substrates. Important lesson: procurement lead times and contract durations were stronger predictors than one-off press mentions.

Advanced strategies & future predictions (2026+)

Expect these trajectories:

Signal fusion becomes essential: combining satellite imagery (data center construction), supplier invoices, and procurement feeds will separate durable buildouts from short-term announcements.
Regulatory signals (export controls, subsidy disbursements) will act as early indicators of concentration risk for certain materials and fabs.
Edge compute procurement for defense and infrastructure will generate mid-market opportunities in 2026–27 as nations accelerate sovereign capabilities.

Actionable next steps (start building today)

Identify 5 authoritative sources (EDGAR, SAM.gov, 2 trade RSS feeds, one regional procurement portal).
Implement RSS + EDGAR ingestion and a simple entity resolution for 30 target tickers.
Score signals with the composite rule above and validate against historical returns for the past 18 months.
Expose results via a small API and a daily digest email — iterate on user feedback and add corroboration rules.

“Bank of America argued in late 2025 that defense, infrastructure and transition materials are optimal indirect exposure to AI. Track procurement and filings — not just headlines — to find the real capital flows.”

Final checklist before you launch

Legal review of target sources and license requirements.
Operational playbook for blocked endpoints and CAPTCHAs.
Data retention & audit trail for each signal (source snapshots).
Backtest results and confidence thresholds for alerts.

Call to action

If you’re building a data product or investment signal for 2026’s AI transition, start with procurement and filings — they reveal where real money is going. Want a starter repo, schema, and scoring notebook made for quant teams? Grab our open-source starter kit or request a demo of our signal API to pilot a transition-stock feed tuned for defense, infrastructure, and materials.

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