Sıla Ermut
Sıla is an industry analyst at AIMultiple focused on email marketing and sales videos.
Research interests
Sıla's research areas include email marketing, eCommerce marketing campaigns and marketing automation.She is also part of AIMultiple's email deliverability benchmark. She is designing and running email deliverability benchmarks while collaborating with the AIMultiple technology team.
Professional experience
Sıla previously worked as a recruiter and worked in project management and consulting firms.Education
She holds:- Bachelor of Arts degree in International Relations from Bilkent University.
- Master of Science degree in Social Psychology from Başkent University.
Her Master's thesis was focused on ethical and psychological concerns about AI. Her thesis examined the relationship between AI exposure, attitudes towards AI, and existential anxieties across different levels of AI usage.
Latest Articles from Sıla
Top 11 AI in ITSM Use Cases & Examples
Leveraging AI for IT service management (ITSM) tools supports organizations in terms of: operational efficiency, proactive maintenance of IT assets, scalability, improved decision-making, and personalization. See the top 11 use cases of AI in ITSM, examples, and benefits of leveraging AI in ITSM. AI-native use cases AI-native ITSM refers to a new way of managing…
Top 6 AI App Builders: Lovable, Base44 & Glide
We tested the top 6 no-code/low-code AI app builders using 1 prompt across 15 dimensions, including setup, browsing, checkout, design, and usability. AI app builder benchmark results Read the benchmark methodology and evaluation to see how we tested these tools. No-code & low-code app builders VendorFree trial/planPricing/month LovableFree plan with 5 daily credits$29 Base44 (Wix)Free…
200+ Leading AI Benchmarks
We curated a list with over 200 AI benchmarks for LLMs, GPUs, cloud GPUs, AI agents, tabular AI, and cybersecurity that are not yet saturated. List of AI benchmarks BenchmarkCategorySub-categoryMetricLast meas.Freq.PerformancePriceLatencyReliabilityContamination resistantContamination source BenchLM Weighted ScoreLLMIntelligenceIntelligence05-26ContinuousTTFFFbenchlm.ai/methodology Humanity's Last ExamLLMReasoningReasoning05-26ContinuousTFFFTlabs.scale.com/leaderboard/humanitys_last_exam ARC-AGI-2LLMReasoningReasoning05-26ContinuousTTFFTarcprize.org/guide/1 SimpleBenchLLMReasoningReasoning05-26Per releaseTFFFTsimple-bench.com CritPtLLMReasoningReasoning05-26Per releaseTFFFTartificialanalysis.ai FrontierMathLLMMathMath reasoning05-26Per releaseTFFFTepoch.ai/frontiermath FrontierMath Tier 4LLMMathMath reasoning05-26Per releaseTFFFTepoch.ai AIME 2025LLMMathMath04-26Per releaseTFFFFmatharena.ai…
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 15 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. Each question was run 5 times to ensure consistent and reliable results. Visual reasoning benchmark See…
Recommendation Systems: Applications and Examples
We examined the main types of recommendation systems, key concepts, and real-world applications, and benchmarked LightFM, Cornac BPR, and TensorFlow Recommenders using AUC, Precision@10, and Recall@10. Best Python libraries for recommendation systems These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques. Additionally, these libraries…
Compare Relational Foundation Models
We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets. Our goal is to measure where a relational LLM’s pretrained semantic priors may provide advantages over traditional tree models and where they face challenges under scale or low-semantic structure.…
LLM Market Share: Compare Usage & Adoption
We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: The United States dominates global LLM usage in web visits and brand adoption, driven by ChatGPT and Gemini, while China operates largely behind the scenes. China…
AGI/Singularity: 9,800 Predictions Analyzed
Artificial general intelligence (AGI) is when an AI system matches human cognitive abilities across all tasks. We analyzed 9,800 AI researchers‘, leading entrepreneurs‘, and community predictions about the AGI timeline: Will AGI/singularity happen? AGI is inevitable according to most AI experts. When will we reach AGI? Between late 2020s and early 2030s. AGI timeline shortened…
AI Rollups: Funding, Investors and Industry Trends
We analyzed 30 investments involving over 130 investors from the past 3 years to understand the current trend for AI rollups. Based on our analysis, we identified investor activity and trends, including the number of investors backing AI rollups, the total funding raised for AI rollups, and the leading industries. Funding trends in AI rollups…
Compare AI Revenues Across the Stack
The AI market expanded rapidly across all four layers (data, compute, models, and applications). For example, NVIDIA’s data center revenue jumped from $47.5B to $115.2B in a single year; OpenAI reached about $13B in annual revenue; and Anthropic approached $7B in ARR. We tracked revenue data from over 100 AI companies. Explore how revenues shifted…
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