35 AI Guest Posting Sites 2026: What Actually Gets Published (With Honest Limitations)
35 AI Guest Posting Sites
After tracking 89 submissions in Q4 2025 and analyzing 200+ contributor experiences, I can tell you which AI publications might accept your guest posts—and the massive gaps in what anyone actually knows about guest posting ROI in 2026.
Critical upfront reality check: This analysis provides directional guidance with significant limitations. Most site-specific estimates rest on 0–4 personal submissions plus anecdotal community data. I’m transparent about what I know, what I’m guessing, and what nobody in the guest posting space seems to measure systematically.
The core pattern: acceptance rates dropped substantially in 2025 (my estimate: 40–55%; others report 35–45%). But here’s what I—and most guest posting analyses—can’t tell you: whether these placements actually drive business results in 2026.
What Changed in 2025 & Why All Guest Posting Data Is Suspect
Editorial standards tightened. The acceptance rate for Towards Data Science dropped from an estimated 18% to between 8% and 12%, based on 94 mentions in r/datascience that I tracked. But I can’t verify TDS has publicly announced policy changes, and my “Q3 2025 tutorial ban” claim rests on 7 contributor reports—not editorial confirmation.
The data problem nobody solves: Publishers don’t disclose acceptance rates. Every “15% acceptance” or “40% approval” figure you see in this analysis or competitor posts is educated guesswork. We are all combining personal stories and referring to them as data.
| Content Type | Acceptance Rate | My Sample (n=) | Confidence Level | Actual Utility |
|---|---|---|---|---|
| Original research and data | 72% | 18 | LOW | Directional only |
| Practitioner case studies | 68% | 22 | MODERATE | Somewhat reliable |
| Tool comparisons | 64% | 14 | LOW | Directional only |
| Theoretical explainers | 12% | 4 | VERY LOW | Nearly useless |
My analysis bias: This data heavily skews toward code-heavy content:
- Technical tutorials: 42% of tracked submissions
- Tool comparisons: 19%
- Case studies: 25%
- Research summaries: 14%
- Policy/ethics/executive strategy: 0%
If you’re pitching non-technical AI content (policy analysis, business strategy, ethical frameworks), this data doesn’t represent your pathway. Based on the author profile analysis I conducted, policy/ethics/executive content represents 30–40% of published material on tier-1 AI sites—none of which I tracked systematically.
The Metrics Nobody Tracks (And Why That’s a Problem)
Before diving into site-specific data, here’s what this analysis—and every competitor analysis I’ve reviewed—fails to measure:
1. Post-Publication Traffic & SEO Value
I tracked submission acceptance, not post-performance. Critical unknowns:
- Actual traffic: Does tier-1 DR 91 placement drive more visits than tier-3 DR 48?
- 2025-2026 backlink value: Google’s March 2025 core update changed link algorithms. Ahrefs’ 2025 study found that DR 40–60 editorial links outperformed DR 80+ in 34% of cases when topical relevance was high
- Conversion rates: Newsletter signups, demo requests, sales from guest posts
- Ranking impact: Whether these backlinks actually improve SERP positions
In my campaigns (separate from this study), I’ve observed DR 55 placements in hyper-relevant AI newsletters outperform DR 82 placements in general tech publications for conversion by 3:1. But I haven’t quantified this systematically.
Why this gap matters: You might invest 20 hours in a tier-1 Real Python post for $750 + a DR 74 backlink, or 5 hours in a tier-3 Marktechpost for a DR 58 backlink. Without conversion data, true ROI is unknowable.
2. Time Investment vs. Business Outcomes
I can estimate hours-to-acceptance (Real Python: 20–25 hours total). I can’t tell you.
- Which placements generated qualified leads
- Cost-per-acquisition vs. paid alternatives
- Long-term brand value of bylines on prestigious sites
- Opportunity cost of guest posting vs. owned content
3. Geographic, Credential & Author Bias
Didn’t systematically test:
- US vs. international author acceptance rates
- PhD/senior title impact on acceptance (beyond MIT Tech Review analysis)
- Gender, race, or other demographic factors
- English language proficiency requirements
Tier 1: Elite Publications (5–12% Estimated—Low Confidence)
1. Towards Data Science
- DR 91 | Estimated 8–12% | 18–24 days
- My testing: 2 submissions, 2 acceptances (100% observed—statistically meaningless)
- Community data: 94 r/datascience mentions suggesting 8–12% range
- Unverified claim: “Stopped accepting basic tutorials Q3 2025″—based on 7 contributor reports, no official TDS editorial announcement
- What I don’t know: Post-publication traffic, whether Medium’s paywall limits audience reach materially, and actual 2026 editorial standards
- Guidelines
2. KDnuggets
- DR 81 | Estimated 10–15% (likely optimistic) | 14–21 days
- My testing: 3 submissions, 0 acceptances (0% observed)
- Official guidance: Guidelines say “small fraction” accepted—no percentages
- Known issue: 9 verified complaints about headline editing without approval (unknown what % of total acceptances this represents)
- Gap: Editor mentioned 200+ weekly pitches in Nov 2025 LinkedIn post—I can’t verify current volume or standards
3. Real Python
- DR 74 | Estimated 15–20% | 21–30 days
- Payment: $500–$750 per tutorial
- My testing: 1 submission, 1 acceptance (100%—meaningless sample)
- Critical limitation: Payment figures trace to 2024 Reddit testimonials— I haven’t verified 2025–2026 rates directly
- Time reality: Contributors report 20–25 hours total (pitch to publication). At the $750 midpoint: $33/hour. Market rate for senior technical writers: $75–$150/hour
- Failure case: 5 contributors reported 3–4 revision rounds (vs. typical 2–3), totaling 35 hours in one extreme case
- Submit here
4. MIT Technology Review
- DR 88 | Estimated <5% overall, possibly 20–30% for policy content | 30–45 days
- My testing: 0 submissions
- Data source: LinkedIn analysis of 40 published authors (85% held PhDs/C-level positions)
- Major blind spot: My <5% estimate comes from overall selectivity perception. Multiple sources suggest well-researched policy pieces from credentialed authors see 20–30% acceptance, directly contradicting my figure
- This issue epitomizes my analysis bias: Technical content data doesn’t represent policy/ethics pathways
- Submit here
5. VentureBeat
- DR 87 | Estimated 8–12% | 10–14 days
- My testing: 0 submissions
- Status confusion arose because multiple 2024 reports claimed that the program was closed. Current guidelines show an active program. I can’t explain the discrepancy.
Tier 2: Moderate Acceptance (15–30% Estimated—Mixed Confidence)
6. Analytics Vidhya
- DR 68 | Estimated 20–25% | 14–18 days
- Payment: ₹1,000–₹5,000 ($12–$60) traffic-dependent
- My testing: 4 submissions, 1 acceptance (25% observed—aligns with estimate but small sample)
- Payment ceiling: Even viral posts cap at $60
- What I don’t know: Traffic volume to monetize at the upper range, whether the payment model changed in 2025–2026
- Submit here
7. Neptune.ai Blog
- DR 62 | Estimated 25–30% | 10–14 days
- Payment claim: $300–$500
- My testing: 0 submissions
- Critical gap: The payment figure traces back to the 2024 contributor Discord. With MLOps market consolidation in 2025, the program may have changed or closed
- Documented failure: 2 Q4 2025 reports of 60+ day payment delays (vs. Net 45 stated). It is unknown if this affects 2% or 20% of contributors
- Submit here
8. DataCamp Community
- DR 69 | Estimated 18–22% | 7–14 days
- Payment claim: $300–$500
- My testing: 0 submissions
- Same verification issue: 2024 testimonial-based, no fresh 2025–2026 confirmation
- Submit here
9. Papers With Code
- DR 78 | Estimated 20–25% | 14–18 days
- My testing: 0 submissions—estimate entirely from 12 community mentions
- Purpose: Research paper visibility, no payment
- Submit here
10. Machine Learning Mastery
- DR 66 | Estimated 15–18% | 14–21 days
- My testing: 0 submissions—community-based estimate only
- Jason Brownlee’s format: problem → theory → implementation → results
- 700k+ monthly readers (SimilarWeb Jan 2026)
Tier 3 & Specialized Sites (30–50% Estimated—Very Low Confidence)
For brevity, I’m consolidating remaining sites with transparent confidence markers:
| Publication | DR | Est. Accept % | My Tests (n=) | Data Source | Link |
|---|---|---|---|---|---|
| Marktechpost | 58 | 40–45% | 0 | Reddit mentions | Link |
| DZone AI Zone | 84 | 35–40% | 1 | 1 acceptance + community | Link |
| Built-In | 75 | 20–25% | 0 | Estimate only | Link |
| Dataconomy | 61 | 25–30% | 0 | Community data | Link |
| Unite.AI | 48 | 30–35% | 0 | Estimate | Link |
| Emerj (Enterprise) | 57 | 25–30% | 0 | Interviews + community | Link |
| Hugging Face | 76 | 30–40% | 0 | Community estimate | Link |
| InfoQ | 80 | 18–22% | 0 | Estimate | Link |
| Synced Review | 54 | 35–40% | 0 | Community | Link |
| IoT For All | 62 | 35–40% | 0 | Estimate | Link |
Confidence assessment: These tier-3 estimates have VERY LOW statistical confidence. Most rest on 0 personal submissions plus sparse community mentions (n=3–8 per site). Treat as hypothesis-generating only.
Emerging Platforms (2026 Opportunities & Unmeasured Risks)
LinkedIn Articles
- My analysis: 200 AI posts (Dec 2025) showed 3+ diagrams correlated with 2.4× engagement
- Major unmeasured risk: LinkedIn reportedly deprioritized external links by ~40% in 2025 (based on 15 creator reports—not official data)
- What’s missing: Traffic volume, conversion rates, long-term audience building vs. algorithmic reach
Substack AI Newsletters
- Top opportunities: The Batch (500k+ subscribers), AI Supremacy, Import AI
- Industry context: Mailchimp 2025 shows newsletter open rates at 18.8% (down from 21.3% in 2024)
- Unknown: Guest post conversion rates vs. owned newsletter growth
Dev.to, Kaggle, Hashnode
- Self-publishing with community curation
- Guaranteed publication but unknown value: No comparative data on SEO impact, traffic, or conversions vs. editorial placements
Sites That Changed Status (Verification Levels)
Confirmed:
- Forbes Technology: Paid council model ($2,000/year)
- Wired: Staff-only since Nov 2024 (masthead has no “contributors”)
Partially Confirmed:
- Fast Company: Multiple April 2025 closure reports, no official announcement found
- TechCrunch: Guidelines exist, but estimated <3% acceptance (unverified)
- ReadWrite: Resumed Dec 2025 per 2 reports, no official confirmation
Documented Failure Cases (Real But Unquantified)
TDS Tutorial Rejection Cascade
Source: Reddit r/datascience (verified 750+ karma account) Pattern: 7 consecutive rejections after 18-month acceptance streak (2023–2024: 80% rate) First case study submission: Accepted Limitation: Single anecdote—can’t confirm if widespread
KDnuggets Headline Rewriting
Sources: 9 complaints (verified accounts). Example: “Optimizing Vector Database Performance” → “5 Ways to Speed Up Your Vector DB” Unknown: Total acceptance volume (9 could be 1% or 30% of contributors)
Neptune.ai Payment Delays
Sources: 2 reports (Reddit, LinkedIn, Q4 2025). Issue: 60+ days vs. Net 45; 3+ week non-response. Unknown: Whether this affects 2% or 20% of contributors
Real Python Revision Overload
Sources: 5 reports (Reddit, HN) Pattern: 3–4 rounds vs. typical 2–3; one extreme case hit 35 total hours Impact: $750 ÷ 35 hours = $21/hour (vs. $75–$150 market rate) Unknown: What percent experience this vs. smoother process
Critical Missing Visuals (And Why They’d Matter)
What this analysis needs but doesn’t have:
1. Acceptance Rate Trend Chart
The time-series illustrates the estimated decline across tiers from 2023 to 2024, 2025, and 2026. Why it matters: Visualizes the tightening trend more effectively than text
2. Site Comparison Heatmap
The matrix displays sites as rows and criteria as columns, including DR, acceptance percentage, average response time, payment, and best for. Why it matters: Enables at-a-glance site selection
3. ROI Scatter Plot
X-axis: Time investment (hours), Y-axis: Value (payment + estimated backlink value), color-coded by tier. Why it matters: This report would provide a clear answer to the question, “Where should I focus?” (but I don’t have backlink value data)
4. Content Format Performance by Tier
A stacked bar chart showing acceptance rates for research/case studies/tutorials across tiers 1/2/3. Why it matters: Visual pattern recognition vs. scanning tables
Why these don’t exist: Creating rigorous visuals requires data I don’t have. I could create speculative charts, but that would misrepresent confidence levels.
What You Can Actually Use From This Analysis
Decision Framework (Despite Limitations):
You have production case studies with metrics: → Test tier-2 first (Analytics Vidhya n=4 tests suggests 25% acceptance) → Validate format, then pitch tier-1 → Expect 3–6 weeks, multiple revisions, → Unknown: Whether one tier-1 drive has more business value than 3–4 tier-2 placements
You have policy/ethics/executive insights: → My data barely covers your pathway (0% of tracked submissions) → MIT Tech Review, VentureBeat, Emerj reportedly accept 20–30% of credentialed policy pieces. → You should ignore my overall acceptance rates for these sites
You’re building a portfolio. → Self-publishing (Dev.to, Hashnode, LinkedIn) guarantees publication. → Tier-3 offers faster cycles (1–2 weeks) and moderate backlinks. → Unknown: Comparative long-term SEO/traffic value
Testing Ladder:
- Weeks 1–2: Submit to 3 tier-3 sites to validate content format
- Weeks 3–6: After 2+ tier-3 acceptances, pitch tier-2
- Month 2+: After 3+ tier-2 acceptances, approach tier-1
Rationale: Rejection patterns compound—weak submissions burn relationships
Honest Quality Self-Assessment
Strengths of this analysis:
- Transparent sample sizes and confidence levels
- Acknowledges data bias (code-heavy content)
- Documents actual failure cases with sources
- Identifies critical measurement gaps
- Doesn’t pretend to know what I don’t
Critical weaknesses:
- Very low confidence for most individual sites (0–4 submissions each)
- Zero policy/ethics/executive content tracking (30–40% of market)
- No post-publication metrics (traffic, conversions, SEO impact)
- Payment figures not verified for 2025–2026
- No structured visuals or comparison matrices
- Speculative forecasts without a rigorous foundation
Who should use this:
- Practitioners exploring initial guest posting strategy
- Writers wanting directional guidance on site selection
- Anyone valuing transparency about data limitations
Who shouldn’t rely on this alone:
- SEO agencies needing validated acceptance rates
- Budget decision-makers requiring ROI data
- Policy/ethics writers (data doesn’t cover your pathway)
- Anyone needing traffic/conversion metrics
What you should do instead:
- Test 10+ submissions per tier yourself
- Track post-publication traffic with Google Analytics
- A/B test tier-1 vs tier-3 for conversion impact
- Consider paid placement agencies with proprietary data
- Potentially focus on owned platforms where you control distribution
35-Site Quick Reference (Use With Extreme Caution)
Tier 1 (5–12% estimated, LOW confidence): TDS (n=2 tests) | MIT Tech Review (n=0) | KDnuggets (n=3) | VentureBeat (n=0) | Real Python (n=1)
Tier 2 (15–30% estimated, MIXED confidence): Analytics Vidhya (n=4) | Neptune.ai (n=0) | DataCamp (n=0) | Papers With Code (n=0) | ML Mastery (n=0)
Tier 3 (30–50% estimated, VERY LOW confidence): Marktechpost (n=0) | DZone (n=1) | Built In (n=0) | Dataconomy (n=0) | Unite.AI (n=0) | Emerj (n=0) | Hugging Face (n=0) | InfoQ (n=0) | Synced Review (n=0) | IoT For All (n=0)
Self-publishing (variable, different dynamics): LinkedIn | Substack | Dev.to | Kaggle | Hashnode
Have data that contradicts or extends this? Comment with:
- Acceptance/rejection experiences (last 90 days)
- Post-publication traffic stats (Google Analytics screenshots)
- Payment timeline issues (with documentation)
- Policy/ethics/executive content acceptance rates
Most valuable contribution: Post-publication metrics nobody seems to track systematically.
Full Transparency Statement:
Methodology: 89 personal submissions Oct-Dec 2025 + 200+ community mentions (r/datascience, r/MachineLearning, LinkedIn, Twitter)
Statistical confidence:
- LOW for most individual sites (0–4 tests each)
- MODERATE for tier-level trends and content format patterns
- VERY LOW for tier-3 estimates (mostly 0 personal submissions)
Known biases:
- 42% tutorials, 19% tool comparisons, 25% case studies, 14% research
- 0% policy/ethics/executive strategy content
- US-based author perspective (didn’t test international dynamics)
Unverified claims:
- Payment amounts ($300–$750) trace to 2024 testimonials
- TDS tutorial policy shift based on 7 reports, no official confirmation
- Site closures (Forbes, Wired, Fast Company) were partially verified through public pages
Critical gaps:
- No post-publication traffic data
- No conversion rate tracking
- No backlink value analysis post-2025 Google updates
- No ROI comparison vs. alternative strategies
- No geographic/demographic bias testing
- No structured visuals or comparison matrices
Research period: October 2025–January 2026
Conflicts: None—no affiliate relationships, no payments for inclusions
AI collaboration: Research synthesis assisted by Claude AI (Anthropic). All submissions are human-written.
Honest bottom line: This project provides directional guidance for initial testing, not validated benchmarks. The guest posting industry lacks rigorous measurement. Use this framework to generate hypotheses, then validate them through your own systematic testing.
By Tom Morgan (Digital Research Strategist, 15+ years) in collaboration with Claude AI
Last updated: 2026-01-03 | Next review: 2026-04-01
