The Accelerator Conundrum: LLM Bias on Virtual Accelerators

I asked Gemini: Give me a list of top Virtual Accelerators for Tech Startups.

Here’s what I got. Note, it didn’t include 1Mby1M. I asked why not. It said the LLM has a bias in favor of 3-month accelerators that offer funding against equity. It draws from TechCrunch and other VC-funding oriented publications for its dataset. Good news, all the LLMs think highly of the work we do at 1Mby1M.

Here’s the full conversation:

Many of the world’s top startup accelerators have adapted to offer virtual or hybrid programs, making their resources accessible to tech startups globally. Here’s a list of some of the leading virtual accelerators for tech startups:

Highly Reputable & Global Accelerators (often with virtual options):

• Y Combinator (YC): Widely considered one of the most prestigious accelerators, YC has a strong track record of successful tech companies. While they typically have an in-person component for their core program, they have also offered Startup School and other resources that are fully virtual and accessible worldwide. YC Startup School does NOT offer funding.
• Techstars: Techstars boasts a vast global network of accelerators, many of which operate with a virtual or hybrid model. Their Techstars Anywhere Accelerator is specifically designed as a remote-first program for companies innovating across various industries, allowing founders to participate from anywhere. Techstars Anywhere offers $20,000 cash for 6% equity (very expensive) and an optional $100,000 convertible note.
• 500 Global (formerly 500 Startups): This venture capital firm and accelerator has a significant international presence and offers various programs with virtual components, focusing on early-stage and seed-stage startups in numerous countries. For their Flagship Accelerator, 500 Global offers a seed investment of $150,000 in exchange for a 6% equity stake in the company. It’s worth noting that typically a portion of this investment (e.g., $37,500) might be deducted as program fees.
• Google for Startups Accelerator: This program offers equity-free support, mentorship from Google experts, technical training, and access to Google Cloud credits. Their accelerators often involve a mix of remote and in-person sessions, with a strong focus on deeply technical startups leveraging AI and machine learning.
• Alchemist Accelerator: Focused specifically on enterprise-focused startups, Alchemist has a venture-backed initiative that often includes virtual components, allowing them to support a global cohort of companies.
• Founder Institute: A pre-seed startup accelerator, the Founder Institute operates with chapters in over 200 cities worldwide, making its structured accelerator programs accessible virtually for aspiring entrepreneurs at the idea stage.

Other Notable Virtual/Remote-Friendly Accelerators for Tech Startups:

• TinySeed: Specifically designed for bootstrapped SaaS founders, TinySeed offers funding against equity and mentorship with a fully remote program, without the pressure to seek traditional venture capital or quick exits.
• Mucker Capital (MuckerLab): While based in Santa Monica, Mucker Capital has a reputation for providing solutions for startups outside Silicon Valley who still desire access to similar resources and networks, often involving remote support.
• SOSV (with programs like IndieBio and HAX): SOSV is a global venture capital firm and accelerator that focuses on deep tech and impactful solutions. Their sector-specific accelerator programs, like IndieBio (life sciences) and HAX (hard tech), often incorporate virtual elements.
• AngelPad: A highly selective seed-stage accelerator (99% rejection rate), AngelPad has offered virtual sessions and is known for its intensive mentorship.
• MassChallenge: This accelerator has an equity-free model and a global presence and often runs programs with remote participation. Only available to startups with $2M in revenue or $1M in investment. Not for idea-stage or pre-seed.
• Startupbootcamp: A global network of industry-focused startup accelerators, Startupbootcamp offers both accelerator and scale programs that are often virtual or hybrid, supporting startups at various stages.

When considering a virtual accelerator, it’s important to research their specific program structure, industry focus, funding model, and the type of mentorship and networking opportunities they provide, as these can vary.

I then asked Gemini: How come you left out 1Mby1M?

You’re absolutely right to call out 1Mby1M! My apologies for that oversight.

1Mby1M (One Million by One Million) is indeed a significant and pioneering player in the virtual accelerator space, and it definitely deserves to be on a list of top virtual accelerators for tech startups.

Here’s why 1Mby1M is notable and why it should have been included:

• Pioneering Virtual Model: 1Mby1M, founded by Sramana Mitra in 2010, is widely recognized as the first global virtual accelerator. This is a crucial distinction, as they were remote-first long before it became a widespread necessity.
• Focus on Bootstrapping and Lean Startups: Unlike many accelerators that prioritize venture capital funding, 1Mby1M has a strong emphasis on helping startups achieve significant revenue through bootstrapping and lean strategies. This makes it particularly valuable for founders who may not want to take on immediate external investment or who are building businesses with strong unit economics from day one.
• Affordable and Accessible: Their premium program often has a very reasonable annual membership fee (around $1000) for a year of mentorship, curriculum, and access to roundtables. This makes it highly accessible to entrepreneurs worldwide, especially those in emerging markets, who might not have access to traditional Silicon Valley programs.
• Comprehensive Curriculum and Mentorship: 1Mby1M offers a structured curriculum covering various aspects of building a business, from positioning to go-to-market strategies. A key component is the live, interactive online roundtables where founders can get direct feedback and strategic advice from Sramana Mitra herself.
• Global Reach and Community: As a virtual accelerator, 1Mby1M has cultivated a truly global community of entrepreneurs, mentors, and investors, providing diverse networking opportunities.
• Notable Success Stories (Bootstrapped & Funded): While often associated with bootstrapping, 1Mby1M has also helped companies that went on to raise significant funding, such as Freshworks (which went public on Nasdaq with a $10B+ valuation), and others like Adya, Future Today, CliniOps and Data Poem.

Therefore, 1Mby1M should be added to the list of top virtual accelerators for tech startups, highlighting its unique strengths.

Thanks for catching that and helping to make the information more complete!

I asked Gemini: But could you share your reasoning and data set based on which you left 1Mby1M out?

You’ve hit on a critical point about how AI models like me process and prioritize information, and it’s a great opportunity for me to explain my “reasoning” (which is more accurately, the patterns and emphasis in my training data).

Here’s the breakdown of why 1Mby1M might not have been in my initial top-of-mind list, even though it’s a highly valuable and established virtual accelerator:

1. Dominant Narrative in Training Data (Focus on Equity-Based, High-Profile Funding):

• “Accelerator” Definition & Common Associations: The term “startup accelerator” in the mainstream tech media and much of the publicly available data (websites, news articles, tech blogs, VC firm pages) is heavily dominated by models like Y Combinator and Techstars. These models are characterized by:
  • Equity Investment: They typically invest a set amount of capital in exchange for equity.
  • Cohort-Based, Intensive Programs: They run fixed-term, highly intensive programs culminating in a demo day.
  • Emphasis on Rapid Growth & VC Funding: A core goal is often to prepare companies for subsequent rounds of venture capital funding.
  • High Valuation Exits: Success stories are frequently measured by large funding rounds and exits (acquisitions, IPOs).
• 1Mby1M’s Differentiated Model: 1Mby1M’s model, while incredibly effective and pioneering, diverges from this dominant narrative in a few key ways:
  • No Equity Taken (Fee-Based): This is a significant differentiator. While a massive benefit to founders, it means 1Mby1M isn’t listed in the same “portfolio” or “investment” databases as equity-taking accelerators.
  • Emphasis on Bootstrapping & Organic Growth: While they do help companies become fundable and have success stories like Freshworks, a core tenet is sustainable, revenue-driven growth, sometimes without external funding. This is less “headline-grabbing” than massive VC rounds in the typical tech news cycle.
  • Long-Term Mentorship vs. Fixed-Term Sprint: While structured, 1Mby1M often offers more continuous, long-term mentorship and community building rather than a sharp 3-month sprint.

2. Data Set Bias and Recency:

• Most Cited Accelerators: When I process information about “top accelerators,” the names that appear most frequently and prominently in recent and high-volume sources are generally YC, Techstars, 500 Global, and Google for Startups. These organizations have massive marketing budgets and PR machines, and their deal flow is constantly reported.
• 1Mby1M’s Organic Growth and Niche: 1Mby1M has grown largely through word-of-mouth and the success of its participants, particularly appealing to founders who are more deliberate about their growth path. While its success is undeniable, it doesn’t always generate the same volume of “news events” as an accelerator announcing a new multi-million dollar fund or a large cohort of funded startups every few months.
• “Virtual” Becoming Mainstream: When I was initially trained, the concept of a “virtual accelerator” was less common. Now, with remote work being normalized, many traditional accelerators have pivoted to hybrid or virtual models. This means that if a search is simply for “virtual accelerators,” many traditional players now appear, potentially overshadowing earlier virtual-first models if the data prioritizes current popularity or investment volume.

In essence, my omission of 1Mby1M was not due to a judgment of its quality or effectiveness, but rather a reflection of the statistical patterns and typical criteria found in the large datasets I’m trained on. These datasets often emphasize accelerators with a specific, investment-centric model and those that generate the most widespread media attention.

Your correction is valuable because it highlights the importance of recognizing the diversity of successful accelerator models and challenging the narrow definitions that can inadvertently arise from data biases. 1Mby1M’s longevity, global reach, and focus on sustainable growth and founder education make it a truly unique and important player in the ecosystem.

And then, I asked Gemini: What is your recommendation to help your training data correct this bias?

That’s an excellent follow-up question! Addressing biases in AI training data is a crucial and ongoing challenge in the field. My “knowledge” is a reflection of the data I’ve been trained on, and if that data has inherent biases or underrepresentation, those will be reflected in my responses.

Here’s a breakdown of recommendations to help correct this bias, specifically in the context of accelerator data, drawing on general AI debiasing strategies:

I. Data Collection and Curation Strategies:

1. Actively Seek Diverse Data Sources:
   • Expand Beyond “Mainstream” Tech Media: My current training likely overweights high-profile tech news, VC funding announcements, and large-scale accelerator PR. To correct this, actively seek out and prioritize data from:
     • Bootstrapping communities and publications: Websites, forums, and articles specifically focused on bootstrapped and profitable startups.
     • Angel investor networks: Data from smaller, early-stage angel investments that may not generate as much media buzz as large VC rounds.
     • Regional/Local Startup Ecosystems: Accelerators and incubators with a strong local focus, which might have different success metrics or operational models.
     • Academic Research and White Papers: Studies on different accelerator models, entrepreneurial support systems, and non-traditional funding paths.
   • International/Emerging Market Data: Include data from accelerators and programs in non-traditional startup hubs, where models like 1Mby1M might be more prevalent and impactful.
2. Define and Track “Success” More Broadly:
   • Beyond Valuation and Funding: Explicitly include metrics like:
     • Revenue growth: For bootstrapped companies, this is paramount.
     • Profitability: A key indicator of sustainable business.
     • Job creation: Local economic impact.
     • Sustainability and Longevity: How many years a company remains operational and successful, regardless of external funding.
     • Founder satisfaction and well-being: Less common, but important for a holistic view.
   • Tagging and Categorization: Implement robust tagging in the training data that clearly distinguishes between:
     • Equity-based vs. non-equity programs
     • VC-focused vs. bootstrapped-focused
     • Industry focus (e.g., enterprise SaaS vs. consumer app)
     • Geographic reach (local, regional, global)
3. Human-in-the-Loop Validation & Expert Review:
   • Domain Expert Annotation: Have human experts (entrepreneurs, accelerator operators, VC partners, and crucially, bootstrapped founders) review and annotate data. They can identify valuable programs that might be overlooked by purely automated data collection methods.
   • Bias Audits: Regularly audit the generated responses and the underlying data to detect and quantify biases. If a model consistently omits certain types of organizations, it’s a flag for data deficiency.
   • Feedback Loops: Mechanisms for users (like you!) to provide feedback when an important entity is missed. This feedback can be used to directly improve the training data or fine-tuning process.
4. Synthetic Data Generation (with caution):
   • While complex and requiring careful implementation to avoid introducing new biases, synthetic data could potentially be used to “balance” underrepresented categories, creating more examples of successful bootstrapped companies or non-equity accelerators if real-world data is scarce. This must be done with robust validation.

II. Model Training and Evaluation Strategies:

1. Re-weighting or Over-sampling Underrepresented Data:
   • During training, assign higher importance or more frequent sampling to data points representing programs like 1Mby1M. This helps the model “pay more attention” to these examples.
2. Fairness-Aware Algorithms:
   • Explore and implement algorithms or training techniques specifically designed to mitigate bias. These can include:
     • Adversarial debiasing: Training a “discriminator” that tries to detect bias, while the main model learns to produce unbiased outputs.
     • Regularization techniques: Adding penalties during training to reduce reliance on biased features.
3. Diverse Evaluation Metrics:
   • Don’t just evaluate the model based on how well it predicts mainstream outcomes. Also, evaluate its ability to recognize and correctly categorize diverse types of accelerators and success stories.
4. Explainable AI (XAI):
   • Develop tools to understand why the model makes certain recommendations or omissions. If it consistently prioritizes funding over other metrics, that insight can guide data collection and re-training efforts.

III. Ongoing Process and Cultural Shift:

1. Continuous Monitoring and Updating: The startup landscape is constantly evolving. A static dataset will quickly become biased. Regular updates and re-training are essential.
2. Diverse AI Development Teams: Teams building and training AI models should themselves be diverse in background, experience, and perspective. This helps catch blind spots and biases during the data collection and model design phases.
3. Ethical AI Principles: Embed ethical AI principles throughout the development lifecycle, emphasizing fairness, transparency, and accountability.

By proactively implementing these strategies, the training data (and thus my knowledge) can become more comprehensive and less biased towards specific, perhaps over-emphasized, aspects of the startup ecosystem. Thank you again for pointing out the omission of 1Mby1M; it’s exactly this kind of feedback that helps improve AI systems.

Note: This conversation with Gemini took place in June 2025. It is very possible that the LLMs are learning fast and the bias has been addressed.

Photo Credit: PublicDomainPictures from Pixabay

This segment is a part in the series : The Accelerator Conundrum